What is APM?

Application performance monitoring (APM) is the practice of tracking key software application performance metrics using monitoring software and telemetry data. Practitioners use APM to ensure system availability, optimize service performance and response times, and improve user experiences.

Research firm Gartner defines APM this way:

“Application performance monitoring is a suite of monitoring software comprising digital experience monitoring (DEM), application discovery, tracing and diagnostics, and purpose-built artificial intelligence for IT operations.”

In today’s digital markets, ensuring an application is always operating at peak performance is essential. The connection between a user’s experience with the back-end services supporting its functions is not always clear, which is especially true with distributed cloud-native applications. To address these ambiguities, APM enhanced by AI greatly increases the efficiency of analyzing application dependencies and how each component affects the other.

APM provides insight into how users experience applications and where performance gaps lie. Mobile apps, websites, and business applications are some examples of front ends that APM can monitor, providing insight into an application’s user experience. APM also includes supporting elements, such as hosts, processes, services, the network, and logs, to foster additional understanding of application performance.

Application performance monitoring vs. application performance management

In addition to application performance monitoring, APM also stands for application performance management.

While application performance monitoring focuses on specific metrics and measurements, application performance management is the wider discipline of developing and managing an application performance strategy. Both these terms refer to related technology and practices.

Observability vs. monitoring

True to its name, APM is about monitoring application performance and system health by capturing and displaying data that teams then analyze using various means. Monitoring focuses on individual metrics that can indicate specific problems.

Observability, on the other hand, ascertains a system’s internal state based on the data it generates, such as logs, metrics, and traces. With this additional granularity, observability can determine the root cause of problems and realize their effects. Using this telemetry data, observability captures the context of what’s happening across multicloud environments so teams can detect and resolve the underlying causes of issues.

The highly distributed nature of modern cloud environments requires that an effective APM solution take a holistic observability-based approach.

What does APM do?

APM has rapidly expanded to encompass a broad range of capabilities, technologies, and use cases to keep pace with cloud-native IT environments.

Continuous improvement

APM can assist teams in optimizing application performance, reliability, and response times by providing the necessary metrics and data for continuous improvement. By using APM to acquire key data points relating to application performance, such as user interaction patterns, application bottlenecks, and software issues, teams gain a greater understanding of where to concentrate efforts and resources for enhancing applications.

Cloud resource utilization

Moreover, APM assists in managing cloud spend and meeting sustainability goals by identifying where organizations can consolidate and optimize resource utilization and consumption.

Application security

APM also enhances application security, a crucial aspect of preserving business value and delivering secure user experiences, by identifying software vulnerabilities and abnormal or suspicious activities.

AI model performance

Amid the increasing prominence and necessity of AI-driven services, APM can also monitor the performance of AI models embedded in applications. AI model monitoring helps ensure that organizations can predict and control AI costs, performance, and data reliability.

Why do organizations need APM?

Every day, customers use apps to shop, work, stream shows and movies, connect to social media, and manage finances. When an app crashes, is slow to load, or doesn’t load at all, users become frustrated, which can cause the business to suffer brand damage or lose revenue. When an internal business application begins to falter, the company may also see reduced employee productivity.

Discover problems before they disrupt

By monitoring systems at the level of metrics, logs, and traces, an advanced observability-based APM solution can discover problems before they disrupt operations or cause outages. observability metrics can establish a performance baseline and detect variances that could lead to wider problems.

Determine the root cause of issues

If an issue does occur, digital teams often find it difficult to identify the root cause of an application performance problem. Causes can run the gamut, from coding errors to database slowdowns and hosting or network performance issues. Even a conflict with the operating system or the specific device being used to access the app can degrade an application’s performance. Observability-based APM can pinpoint and help teams to prioritize these issues.

Cut through cloud complexity

While modern applications such as mobile apps, websites, and business apps may seem simple on the surface, they’re highly complex. These apps comprise millions of lines of code. They include hundreds of interconnected digital services and open source solutions, and they run in containerized environments hosted across multiple cloud services. Without APM technologies, teams struggle to resolve the numerous problems that can arise, raising the likelihood of customers getting frustrated and abandoning the app altogether.

For this reason, a powerful APM solution based on end-to-end observability is necessary to properly maintain and optimize modern applications.

APM core features

APM encompasses many types of monitoring across the full IT stack, including the following, among others:

• Infrastructure monitoring
• Network monitoring
• Database monitoring
• Log monitoring
• Container monitoring
• Cloud monitoring
• Serverless monitoring
• Synthetic monitoring
• End-user monitoring

Organizations often run dozens of individual monitoring tools at once, especially when they’re holding onto legacy applications and managing them using the tools they find most familiar.

Although individual tools may seem easier, especially to meet the needs of many teams, fragmented monitoring frequently creates problems. A single APM solution that takes a full-stack observability approach makes monitoring all these use cases easy and more reliable.

What are the benefits of APM?

Observability-based APM provides modern IT operations with numerous benefits, including the following.

Full-stack observability

As application infrastructures expand to encompass both on-premises and multicloud environments, organizations increasingly understand that only a full-stack observability approach can deliver comprehensive visibility into the root causes of issues, wherever they originate. Teams can monitor their entire infrastructure from end to end—encompassing everything from infrastructure health to application performance and even the end-user experience. With this visibility, teams can see all these components and understand the interdependencies among them, getting faster answers to key questions.

Continuous automation

Trying to manually maintain, configure, script, and source the volume of data in a cloud-native environment is beyond human capabilities. Therefore, organizations must continuously automate these tasks to ensure proper application performance. Processes including deployment, configuration, discovery, and updates require automation to keep pace with modern multicloud environments and user demands. For this reason, an APM solution that continuously informs and automates every touchpoint of the software development lifecycle (SDLC) and other business processes is crucial to maintaining efficiency.

AI assistance

AI assistance empowers teams by reducing manual or redundant work, allowing them to be more productive in areas of critical importance to the business. An observability-based APM solution that provides multi-tiered AI capabilities goes beyond just collecting data by using that data for real-time answers. A successful APM solution uses predictive, causal, and generative forms of AI in tandem to proactively resolve problems and improve performance without the need for extensive manual effort.

Cross-team collaboration

APM is a team sport, typically requiring the expertise of multiple teams. When organizations can depend on a unified observability-based platform as a single source of truth, teams can break down silos and achieve greater cross-team collaboration. When business, operations, application, and development teams are working from the same data sets, they can streamline communication and reach decisions quickly to resolve problems and optimize applications.

User experience and business impact

User experience is inextricably linked to business outcomes, whether the application is mobile app-to-user, IoT device-to-customers, or a web application behind the scenes. With intelligence into user sessions, including real user monitoring and session replay, teams can connect user experiences to application performance and business outcomes such as increased conversions, revenue, and completed customer journeys.

Synthetic monitoring also enables teams to proactively resolve issues and optimize applications by simulating artificial user interactions, which can ensure optimal user experience before any real issues occur.

With data-backed decisions, answers at the ready, and real-time visibility into user journeys and business key performance indicators (KPIs), organizations can consistently and more efficiently deliver ideal customer experiences across all their channels for better business outcomes.

The technical, operational, and business benefits of APM

APM provides specific benefits for technical, operations, and business teams.

APM technical benefits

Business, operations, application, and development teams can expect several practical benefits from adopting APM practices and tools, including the following:

• Increase application stability and uptime by AI-powered root-cause analysis and real-time answers
• Reduce performance incidents with proactive alerting
• Speed up and automate performance problem resolution
• Accelerate and increase the quality of software releases with automated development and delivery processes

APM operational benefits

Long-time users also report that APM has given their organizations some unexpected but impactful advantages. Operational benefits include the following:

• Increase collaboration across teams with a single source of truth
• Boost confidence to make well-informed and impactful business decisions for teams across the organization with new insights and reliable intelligence
• Increase efficiency and innovation for application, operations, and development teams for faster issue resolution
• Bolster job satisfaction and higher employee retention among team members

APM business benefits

Those in the boardroom have just as much to gain from adopting APM solutions as those on the front lines of DevOps efforts. Business benefits include the following:

• Reduce operational costs with greater automation and efficiency
• Upgrade developer and operational productivity by facilitating cross-team collaboration
• Improve customer experience by increasing understanding of end users and their preferences
• Increase conversion rates from improved application stability and optimized user experience
• Achieve sustainability goals with greater insight into the IT carbon footprint

However, modern cloud-native environments present challenges for APM solutions that require specialized capabilities to achieve these benefits.

Why do cloud-native applications make APM challenging?

Even though the benefits of APM are well established, the rise of complex cloud-native applications has made it more challenging for organizations to perform well and remain competitive.

Massive amount of telemetry data

For example, cloud-native apps generate far greater quantities of telemetry data because they are made up of myriad microservices that dynamically spin up and down in the background. Each of these microservices exists for a short period and generates its own telemetry data, adding to the overall signal noise. When this happens, it becomes more difficult to find the most important events taking place within an application’s infrastructure.

Distributed cloud architectures

What’s more, the distributed and dynamic nature of microservices often makes it difficult to pinpoint the root cause of issues without the assistance of a reliable AI engine. As a result, strong AI capabilities are a necessity for cutting through the noise and garnering meaningful answers relating to problem remediation and application optimization.

Heterogeneous data

Cloud-native apps also produce many kinds of data. Telemetry data from a serverless environment is quite different from a database or a virtual machine (VM), for example. But an organization still needs to centrally manage and make sense of all the information as it comes in.

Increased velocity

The velocity at which systems generate this data is another problem. When a cloud-native app includes many smaller microservices, data comes in at a much faster rate than with a monolithic application. All these factors add challenges that make traditional APM more difficult in a cloud-native application environment.

APM tools vs. APM platforms

Though often referred to as one in the same, both APM tools and APM platforms offer unique benefits that teams can apply based on an organization’s needs, use cases, and resource availability.

What are APM tools?

APM tools are software utilities that often focus on one specific aspect of application performance. Such point solutions can help identify specialized issues. Over time, however, organizations often find themselves using multiple APM tools that don’t necessarily integrate with one another or provide comprehensive insights into the application environment.

In response to the rapid influx of telemetry data, organizations can take one of two approaches when picking APM tools. By default or by design, different teams may deploy a combination of point solutions—tools that solve only one business problem. Conversely, they may choose a single platform that more fully encompasses the many layers and use cases within the application environment.

What is an APM platform?

An APM platform is a software system that provides a single integrated solution using AI and automation to deliver a precise, context-aware analysis of the application environment. Organizations can use an APM platform to continuously monitor the full stack for system degradation and performance anomalies.

With the deluge of telemetry data associated with cloud-native apps comes a profusion of performance monitoring tools and platforms. In response, organizations are turning to open source standards and tools, such as OpenTelemetry, to standardize how they instrument, generate, and collect telemetry data for analysis. However, to thoroughly analyze the data such tools gather, the tools often must be complemented by an observability-based APM platform. This combination can provide valuable insights into software performance and behavior across multiple cloud platforms and tools.

Point solutions can pose benefits at a local level and challenges at a macro level, while a platform approach embraces a modern vision of APM that demonstrates clear advantages at the local and macro levels. What’s more, a platform approach also streamlines and simplifies business and technical processes, enhancing collaboration across teams.

Benefits of individual APM tools

Individual APM tools are specialized to monitor specific components and provide advantages for those specific use cases. For example, some organizations use Grafana to consolidate their metrics visualizations in a single dashboard while others use Jaeger for its distributed tracing capabilities to gain better observability of their systems and troubleshoot performance issues. Both these tools are highly specialized for the environments to which they’re applied.

Teams focused on solving a specific, specialized issue, such as implementing a service mesh to help manage orchestration in their Kubernetes environment, turn to individual tools because they’re cost-effective and easy to implement.

Challenges of individual APM tools

Individual tools only provide a limited view of an organization’s application architecture. This limited visibility makes it harder to identify root causes of application performance issues, resulting in longer downtimes when problems arise. Further, they only provide a single view of the application architecture, often missing the “cause and effect” of performance problems—for example, increased CPU usage caused by a microservice failure. This limited visibility may result in unnecessary troubleshooting exercises and finger-pointing, not to mention wasted time and money.

Because the scope of these solutions is limited, they can also create silos in which teams may disagree on service-level objectives (SLOs) and metrics. This silo effect can lead to more inefficiency and blame-shifting, as teams rely on different sets of information.

APM as part of a larger multicloud observability strategy

Because APM has its roots in the era of monolithic applications before the rise of microservices, open source technologies, and cloud-native environments, some industry observers have argued that APM platforms lack the innovation and deep-dive capabilities required to keep up with bespoke point solutions and individual tools. This may be true for many traditional APM monitoring tools.

However, an observability-based platform such as Dynatrace can offer broad technological coverage across the full stack, including bespoke point solutions.

Purpose-built for cloud-native environments

By leveraging data capture for any type of application and APIs to ingest data, a cloud-native platform like Dynatrace can broaden its coverage to the entire hybrid-multicloud network. This provides a macro-level view across multiple environments to provide continuous discovery. Visibility extends to the applications running within these environments, providing proactive anomaly detection prioritized by business impact.

AI and continuous automation

Crucial capabilities of a modern APM platform include AI and continuous automation. With the explosion of observability data, a platform needs to automatically process billions of dependencies in real-time, continuously monitor the full stack, and deliver precise answers with root-cause determination. Dynatrace takes a power-of-three approach to AI that leverages predictive, causal, and generative AI to help organizations deliver the highest software performance and enable workflow automation.

Integration with cloud platforms

With the scale, diverse functionality, and dynamic nature of cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform, successful APM solutions need to work immediately without configuration or model training. The Dynatrace platform provides complete observability out of the box for dynamic cloud environments, at scale and in context.

By going beyond metrics, logs, and traces with AI-powered analytics, Dynatrace provides automated and intelligent answers from data across the full stack, including entity relationships, user experience data, cloud services, and the latest open source standards, including OpenTelemetry.

The future of APM

APM has always played a crucial role in optimizing digital business, but the recent influx of rapid AI innovations has rendered unified, holistic APM especially crucial. AI introduces new monitoring challenges that require teams to understand their expanding applications to maintain efficient operations and a positive user experience. But achieving this level of understanding can be difficult due to the complexity of generative and agentic AI stacks. As a result, auto-discovery and AI-powered answers are essential to help resolve issues when they arise. Teams need a reliable way to cut through the noise with an efficient, unified approach to problem remediation and application optimization.

The increasing size and complexity of AI workloads have also grown past human ability to manage. For this reason, automating as many processes as possible (with an end goal of fully autonomous operations) is the most reliable way to meet the demands of modern AI workloads. However, autonomous operations are only as effective as the answers they rely on. These answers must be deterministic and exact, not based on causation. APM equipped with deterministic AI is key to facilitating effective autonomous operations, enabling meaningful action based on answers, not guesses.

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OpenTelemetry is an open-source framework of tools, APIs, and SDKs that help analysts understand software performance and behavior. Also referred to as OTel, OpenTelemetry is rapidly solidifying its position as a fundamental tool in the world of observability.

Born as an open-source project under the Cloud Native Computing Foundation (CNCF), OpenTelemetry provides a unified framework for generating, collecting, processing, and exporting telemetry data—including logs, metrics, and traces.

Using OpenTelemetry,  IT teams can instrument, generate, collect, and export telemetry data for analysis to better understand software performance and behavior. When OpenTelemetry debuted in beta in 2020, it replaced its predecessors, OpenTracing and OpenCensus.

OpenTelemetry enables observability

To appreciate what OTel does, it helps to understand observability. Traditionally speaking, observability is the ability to understand what’s happening inside a system from the knowledge of the external data it produces; usually logs, metrics, and traces.

But the data itself is only as good as what you can learn from and do with it. This definition from Hazel Weakly sums it up nicely:

“Observability is the ability to ask meaningful questions, get useful answers, and act effectively on what you’ve learned.”

Observability is important because the systems of today are exponentially more complex than the systems of ten, or even five years ago. The shift from monolithic to distributed IT architectures introduces many more moving parts and interactions to keep track of, sometimes leading to systems behaving in unpredictable ways. Observability helps you make sense of what’s happening so you can act on this information, and OpenTelemetry helps to enable observability.

By promoting consistency and interoperability, OpenTelemetry enhances observability practices and benefits the entire industry by streamlining and standardizing how everyone can collect and use data.

Since the project’s start, many vendors, including Dynatrace, have contributed to the project to make rich data collection easier and more consumable. In fact, Dynatrace is one of the top contributing organizations to OpenTelemetry.

Benefits of OpenTelemetry

Collecting application data is nothing new. However, the collection mechanism and format are rarely consistent from one application to another. This inconsistency can be a nightmare for developers and Site Reliability Engineers (SREs) who are just trying to understand the health of an application.

Most of the major observability vendors, including Dynatrace, support OTel. As a result, it has become the de facto standard for instrumenting cloud-native applications. What differentiates observability solutions from one another is what they do with your data to help you ask the right questions. Asking the right questions unlocks an elevated level of understanding, giving businesses the ability to accelerate growth, drive innovation, and deliver experiences customers love.

It’s akin to how Kubernetes became the standard for container orchestration. This broad adoption has made it easier for organizations to implement container deployments since they don’t need to build their own enterprise-grade orchestration platform. Using Kubernetes as the analog for what it can become, it’s easy to see the benefits it can provide to the entire industry.

To understand why observability and OTel’s approach to it are so critical, let’s take a deeper look at telemetry data itself and how it can help organizations transform how they do business.

What is telemetry data?

Telemetry is the process of gathering and transmitting signals (data) emitted by instrumentation code within a system’s components. Traces, metrics, and logs make up most of all telemetry data.

• Traces result from following a process (for example, an API request or other system activity) from start to finish, showing how services connect. Keeping watch over this pathway is critical to understanding how your ecosystem works, if it’s working effectively, and if any troubleshooting is necessary. Traces consist of individual operations called spans, which include unique identifiers, such as operation name, timestamp, context, attributes, events, and status.
• Metrics are numerical data points, either counts or measures, that systems can calculate or aggregate over time. Metrics originate from several sources, including infrastructure, hosts, and third-party sources. While logs may not always be accessible, most metrics are readily available via query. Timestamps, values, and even event names can preemptively uncover a growing problem that needs remediation.
• Logs are important because you’ll naturally want an event-based record of notable anomalies across the system. Structured, unstructured, or plain text, these readable files can tell you the results of any transaction involving an endpoint within your multicloud environment. However, not all logs are inherently reviewable—a problem that’s given rise to external log analysis tools.

Telemetry data becomes observability data when, as noted above, you can “ask meaningful questions, get useful answers, and act effectively on that information.” Making sense of it all requires an observability backend.

How does OpenTelemetry work?

OTel consists of a few components as depicted in the following figure. Let’s take a high-level look at each one from left to right:

• Specification. Defines a standard telemetry data format and describes how to build instrumentation. This ensures that users have a similar experience, regardless of what language they’re using.
• Data model. Defines fields for each signal and how they interact. Signals include traces, logs, and metrics.
• API. Defines the methods used to instrument applications and serves as the entry point for instrumentation. Each language supported by OpenTelemetry has its own API implementation.
• SDK. Implements the API and also determines how systems generate and correlate their telemetry. Each language supported by OpenTelemetry has its own SDK. Both the APIs and the SDKs are defined in the specification to ensure a consistent experience across implementations.
• Collector. A vendor-neutral binary used to ingest, transform, and export data to one or more observability backends.
• OpenTelemetry Protocol (OTLP). A vendor—and tool-agnostic specification for encoding—transmitting and delivering OpenTelemetry data. Telemetry data emitted by the SDK uses OTLP, and many observability backends now support ingesting data in the OTLP format. For those who do not, there are exporters available that convert data from OTLP to a tool-specific format. OTLP supports both HTTP and gRPC.
• Observability backend. A system or tool where telemetry data collected by OpenTelemetry is sent, stored, and analyzed. It enables organizations to derive meaningful insights and make sense of telemetry data in a cohesive way.

Flexible API/SDK integration

You can decouple the API from the telemetry-generating code with minimal implementation. This decoupling allows your app or library to run with just the API package, without sending telemetry data to the backend. This setup acts as a placeholder until you’re ready to integrate an SDK. When ready, you can choose an SDK that best fits your needs, whether it’s the OpenTelemetry SDK, a vendor-specific one, or a custom-built SDK. This flexibility ensures you can add functionality without significant code changes.

What’s next for OpenTelemetry?

OpenTelemetry is maturing and is fast approaching its graduation as a CNCF project. Traces, logs and most parts of metrics are now considered generally available. The OpenTelemetry project’s goals extend well beyond its current offerings. Exciting initiatives are paving the way for even broader use cases, such as improving digital experiences and enabling detailed insights into application performance through code-level profiling. Let’s check out some highlights of OpenTelemetry’s exciting initiatives, all designed to take observability to the next level.

• Digital Experience Monitoring. Developers and product teams will soon be able to gather telemetry data directly from user-facing applications. This allows organizations to identify where users face lags or issues, enhancing overall app performance.
• Code-Level Profiling. OpenTelemetry is also evolving to profile application code in real-time. This provides deeper insights into how specific sections of code behave in production, helping engineers optimize critical parts of their applications.
• AI Agents. Recently, there’s been an explosion in the need for monitoring AI systems. OpenLLMetry is donating their code to the OpenTelemetry project. If accepted, it will soon become an extension of the OpenTelemetry ecosystem.

How can I contribute to the OTel community?

If you’ve been curious about contributing to OpenTelemetry (OTel) but are unsure where to begin, there are plenty of ways to get involved. Whether you’re a newcomer or a seasoned practitioner, the OpenTelemetry community offers a variety of opportunities suited to different interests and skill levels.

Some ways you can contribute to the OpenTelemetry Project include the OpenTelemetry documentation, OpenTelemetry blog, End User SIG, OpenTelemetry Demo, or a language or component-specific Special Interest Group SIG). No matter how big or small your contributions, they make a difference and are deeply valued.

OpenTelemetry veteran, Adriana Villela, wrote a great article to help you get started.

How does Dynatrace contribute to the Otel community?

Dynatrace is an active member of the OpenTelemetry community. Dynatracers hold key leadership roles as project maintainers or approvers in the following groups:

• OpenTelemetry Technical Committee
• OpenTelemetry Specification (Metrics, Semantic Conventions)
• OpenTelemetry for JavaScript
• OpenTelemetry Collector
• OpenTelemetry Demo project
• OpenTelemetry End User SIG

In fact, Dynatrace has a team dedicated to contributing to OpenTelemetry and ensuring that the Dynatrace platform integrates smoothly with OpenTelemetry data.

Dynatrace and OpenTelemetry together can deliver more value

OpenTelemetry is a key enabler in the observability space, providing a unified framework for collecting telemetry data. However, to unlock the full potential of this data and turn it into actionable insights, a powerful platform to manage, analyze, and visualize it effectively is essential.

Dynatrace is purpose-built to enhance OpenTelemetry’s capabilities. Data plus context are critical to supercharging observability, and with Dynatrace, you’re not just collecting data; you’re gaining a deep understanding of how your systems work and how to optimize them. With seamless integration, advanced analysis across telemetry data, insights into business outcomes, and predictive analytics spanning your entire stack, Dynatrace turns your OpenTelemetry data into actionable intelligence to optimize your systems.

Explore how Dynatrace can transform your OpenTelemetry data into a powerful driver of innovation and business success. Learn more with this video series on getting started with Dynatrace and OpenTelemetry.

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Complex information systems fail in unexpected ways. That’s why IT teams need both observability tools and an observability platform. To understand the distinction between observability tools and an observability platform, let’s start with some definitions.

What is observability?

Observability gives developers and system operators real-time awareness of a highly distributed system’s current state based on the data it generates. With observability, teams can understand what part of a system is performing poorly and how to correct the problem.

Observability is made up of three key pillars: metrics, logs, and traces.

• Metrics are measures of critical system values, such as CPU utilization or average write latency to persistent storage.
• Logs are files that record events in a system, such as the start of a subprocess or the trapping of an error.
• Traces provide performance data about tasks that are performed by invoking a series of services. They’re particularly important in distributed systems, such as microservices architectures.

Each is useful alone, but integrating all three in context gives you a more comprehensive view of a system’s state. This real-time visibility creates situational awareness and opens the door for IT use cases ranging from DevSecOps to digital experience management.

Teams gain observability from telemetry data sent by endpoints across the environment using instrumentation from a wide variety of tools.

Observability platform vs observability tools: What’s the difference?

Observability tools, such as metrics monitoring, log viewers, and tracing applications, are relatively small in scope. Teams can use them independently to gain insights into single components of larger systems. Unfortunately, they often don’t communicate with each other or offer a single source of truth. This means the teams relying on these tools—teams that should be working together—must make decisions with incomplete data. With limited visibility, teams have a narrow understanding of how those decisions impact other software components and vice-versa.

A platform approach, on the other hand, presents a more effective option for understanding observability as a whole.

What is an observability platform?

An observability platform is a comprehensive toolset designed to provide IT professionals with deep visibility into the performance, health, and behavior of complex systems and applications. Unlike traditional monitoring tools that only track predefined metrics, an observability platform enables you to explore and diagnose unexpected issues across your entire system.

Key features of an observability platform

Log management: Aggregates, processes, and analyzes system logs to identify anomalies and patterns.

Metrics monitoring: Tracks system-wide metrics, such as CPU usage or memory consumption, providing a quantitative view of performance.

Distributed tracing: Captures end-to-end visibility of user requests across services to pinpoint bottlenecks and latency issues.

Dashboards and visualizations: Presents system data in easily digestible charts, graphs, and heatmaps, allowing for quick assessments.

Anomaly detection powered by AI/ML: Utilizes advanced algorithms to identify irregularities in real time and predict potential issues.

By correlating logs, metrics, and traces, observability platforms empower teams to efficiently identify the root causes of issues, reduce downtime, and maintain optimal application performance.

Ultimately, an observability platform isn’t just about monitoring; it’s about gaining actionable insights that help IT professionals make informed decisions to ensure system reliability and scalability. An observability platform that also integrates user experience data and business context into these capabilities provides a real-time advantage that helps teams respond faster and get more done.

The case for an integrated observability platform

As applications have become more complex, observability tools have adapted to meet the needs of developers and DevOps teams. For example, in 2005, Dynatrace introduced a distributed tracing tool that allowed developers to implement local tracing and debugging. This was sufficient for monolithic applications, which were common at the time. But by 2015, it was more common to split up monolithic applications into distributed systems. The key driver behind this change in architecture was the need to release better software faster.

The shift to multicloud microservice-based architectures introduced an unintended but inevitable consequence: operational complexity. Today, developers face the challenge of understanding what happens within a system comprising hundreds or thousands of interdependent services. Observability tools that provide local tracing and debugging are no longer sufficient for either operations or development teams.

Observability platforms provide root-cause analysis

Operations teams need broad, system-wide views and focused, drill-down views into services. This visibility ensures systems function as expected and helps teams understand the conditions that cause a system failure. For example, if the average response time for a service is increasing, the operations team needs to understand the cause. It could be due to a spike in load on the service, which increases system response time.

Adding more compute resources to an application cluster could address the problem, but load spikes are only one possible cause. A database could start executing a storage management process that consumes database server resources. In this case, the best option may be to stop the process and execute it when the system load is low. The key is knowing what is the root cause of the performance issue. This is where an observability platform approach becomes a real advantage.

Observability platforms provide context

The shift to multicloud has increased complexity further, driving the need for an observability platform that can provide visibility into the operational details of distributed systems.

A microscopic view of systems is also particularly valuable to developers. Debugging can require access to low-level details about how an operation works and how it may be causing problems for a downstream service.

For example, an operation may fail 2% of the time it is performed. Developers need to know what distinguished those 2% instances from the 98% that succeed. It could be differences in inputs, such as malformed inputs from another service. It could also be a bug on an infrequently executed logic path in the service.

OpenTelemetry and modern observability tools

Collecting data from observability tools is an important part of what an observability platform does. With the spread of DevOps and microservices, the vast array of possible data formats can be a nightmare for developers and SREs who are just trying to understand the health of an application.

The open-source observability framework, OpenTelemetry, provides a standard for adding observable instrumentation to cloud-native applications. OpenTelemetry provides a standardized method to instrument, generate, collect, and export telemetry data for analysts to understand software performance and behavior.

With unified data collection formats, libraries, and utility tooling, OpenTelemetry makes data more interchangeable and integrable. This streamlining simplifies how teams gather telemetry data, but to make sense of it, teams need a modern observability platform to store the data and to help generate actionable insights from the information.

Such a modern observability platform must also scale to ingest, analyze, and store the increasing volumes of metrics, logs, and trace data. It must provide analysis tools and artificial intelligence to sift through data to identify and integrate what’s most important. This approach helps developers and operations teams understand and act on the state of a complex system.

Observability tools and an observability platform: better together

For observability that scales with cloud-native technologies, organizations need an AI-driven observability platform like Dynatrace. Our distributed tracing technology powered by PurePath 4 automatically captures and analyzes transactions at every tier of an application stack. With no code changes, Dynatrace extends distributed tracing and code-level analysis to OpenTelemetry data, service mesh, and all data from your serverless computing services.

Capturing every process from start to finish and automatically providing insights enables fully integrated, no-silo collaboration across development, operations, and applications teams. PurePath end-to-end tracing contrasts tools that require manual instrumentation and user expertise to understand performance issues.

For development, operations, security, and SRE teams alike, Dynatrace brings automation and answers rather than just raw data in dashboards. To drive better business outcomes with automatic and intelligent analysis, integrate observability tools with an observability platform.

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From managing complex IT environments to ensuring seamless customer experiences, the demands on IT departments have never been greater. To manage these complexities, organizations are turning to AIOps, an approach to IT operations that uses artificial intelligence (AI) to optimize operations, streamline processes, and deliver efficiency. One Dynatrace customer, TD Bank, placed Dynatrace at the center of its AIOps strategy to deliver seamless user experiences.

Why AIOps?

AI for IT operations (AIOps) uses AI for event correlation, anomaly detection, and root-cause analysis to automate IT processes. It plays a crucial role in managing complex multicloud environments by streamlining operations and enhancing efficiency, reducing costs, and driving innovation.

Paired with an observability platform, AIOps identifies and helps to resolve cloud application performance and security issues, preventing problems before they disrupt operations. Its adoption is growing rapidly, driven by the explosion of data complexity that accompanies modern cloud IT environments. Valued at $17 billion annually, the AIOps market reflects its importance as large companies increasingly integrate AIOps and digital experience monitoring tools, with adoption expected to rise significantly in the coming years.

As a leader and trailblazer in the AIOps space, Dynatrace uses AI-powered root-cause analysis to provide precise actionable insights, enabling businesses to automate operations across the enterprise.

TD Bank adopts an observability-based AIOps strategy

As one of the 10 largest banks in the U.S., with $1.4 trillion in assets and 27 million customers, TD Bank places customers at the center of everything it does. As its enterprise monitoring team modernized the bank’s digital ecosystem from legacy on-premises data centers to a hybrid multicloud environment, TD Bank faced significant challenges with complexities its traditional monitoring tools couldn’t handle.

TD Bank’s modernized technology stack became increasingly intricate, leading to operational inefficiencies. The bank had accumulated multiple monitoring tools, each providing fragmented insights. This disjointed approach made it difficult to collaborate effectively and resolve issues promptly.

The Dynatrace unified observability platform provided TD Bank with a single source for answers, offering end-to-end visibility across its entire technology stack. Using Dynatrace at the center of its AIOps strategy, the TD Bank team reduced the number of IT incidents they were experiencing, improving customer trust.

Faster responses for greater reliability

A standout feature of Dynatrace is its ability to deliver rapid and precise answers. For TD Bank, this meant significantly reducing the time to identify and resolve transaction failures. With AI-driven certainty, the bank could instantly pinpoint the root cause of issues, leading to a 25% increase in proactive incident identification and a 20% faster response rate. This efficiency translated to a dramatic reduction in the transaction failure rate, from 0.16% to just 0.06%.

Cost optimization and efficiency

Using Dynatrace, TD Bank was able to consolidate its observability tools and achieve substantial cost savings. With its platform-based approach to end-to-end observability, Dynatrace enabled the bank to eliminate up to seven redundant monitoring solutions, reducing infrastructure and licensing costs by up to 45%. Beyond cost savings, this consolidation freed up TD Bank’s teams to focus on innovation rather than routine maintenance, driving further efficiency.

Enhanced customer satisfaction

For TD Bank, customer satisfaction is paramount. With the efficiencies stemming from Dynatrace AI capabilities, the bank reduced customer irritants by more than 60% and sped up issue resolution by 20%. With precise answers, TD Bank’s teams can quickly understand issues and resolve customer calls , enhancing the overall customer experience and building trust in the bank’s digital services.

How Dynatrace delivers on AIOps

AI-powered root-cause analysis

At the heart of Dynatrace AIOps capabilities is its power-of-three AI engine, Davis®. Using causal, predictive, and generative AI, Davis delivers detailed insights into issues, including their root cause and impact. For TD Bank, the technology has been instrumental in quickly identifying and resolving issues, ensuring minimal disruption to customer services.

Automated remediation

By automating routine tasks and responses to common issues, Dynatrace helps businesses like TD Bank achieve zero-touch operations. This automation not only improves efficiency but also ensures teams can address critical issues promptly, minimizing downtime.

Predictive analytics

Dynatrace AI-driven predictive analytics provide foresight into potential issues before they occur. For enterprises, this means staying ahead of the curve, preventing disruptions, and ensuring seamless operations. TD Bank has used these capabilities to anticipate and mitigate risks, ensuring a smooth banking experience for its customers.

The broader effect of AIOps: Transforming IT operations

AIOps is not just a tool; it’s a transformation strategy. By integrating AI into IT operations, businesses can achieve unparalleled efficiency, agility, and resilience. It is clear that the future of IT operations lies in AI, and Dynatrace is leading the charge. With its deep-rooted AI expertise and innovative AIOps platform, Dynatrace is transforming the way businesses operate. The success TD Bank has achieved demonstrates how Dynatrace helps unlock the hidden value in customer data and maximize the tangible benefits of AI-driven operations.

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As recent events have demonstrated, major software outages are an ever-present threat in our increasingly digital world. From business operations to personal communication, the reliance on software and cloud infrastructure is only increasing.

Outages can disrupt services, cause financial losses, and damage brand reputations. Understanding the causes of these outages is crucial for preventing them and ensuring smoother, more reliable tech operations. It’s also critical to have a strategy in place to address these outages, including both documented remediation processes and an observability platform to help you proactively identify and resolve issues to minimize customer and business impact.

How software outages happen

Outages can occur for many reasons, ranging from internal mishaps to external attacks. They may stem from software bugs, cyberattacks, surges in demand, issues with backup processes, network problems, or human errors. Each of these factors can independently cause a major disruption, but often, outages result from a combination of issues. Let’s explore each of these elements and what organizations can do to avoid them.

1. Software bugs

Software bugs and bad code releases are common culprits behind tech outages. These issues can arise from errors in the code, insufficient testing, or unforeseen interactions among software components.

Possible scenarios

• A new software update contains a bug that causes a critical application to crash, disrupting business operations.
• A poorly tested feature release leads to incompatibility issues, resulting in downtime for users.

To prevent outages caused by software bugs, organizations should implement thorough testing procedures, including automated testing and continuous integration practices. Regular code reviews and a robust quality assurance process are also vital to help identify issues before they reach production.

2. Cyberattack

Cyberattacks involve malicious activities aimed at disrupting services, stealing data, or causing damage. These attacks can be orchestrated by hackers, cybercriminals, or even state actors.

Possible scenarios

• A Distributed Denial of Service (DDoS) attack overwhelms servers with traffic, making a website or service unavailable.
• Ransomware encrypts essential data, locking users out of systems and halting operations until a ransom is paid.
• Remote code execution (RCE) vulnerabilities, such as the Log4Shell incident in 2021, allow attackers to run malicious code on a remote system without requiring authentication or user interaction.

To cope with the risk of cyberattacks, companies should implement robust security measures combining proactive preventive measures such as runtime vulnerability analytics, with comprehensive application and perimeter protection through firewalls, intrusion detection systems, and regular security audits. Employee training in cybersecurity best practices and maintaining up-to-date software and systems are also crucial.

3. High demand

Sudden spikes in demand can overwhelm systems that are not designed to handle such loads, leading to outages. This often occurs during major events, promotions, or unexpected surges in usage.

Possible scenarios

• A retail website crashes during a major sale event due to a surge in traffic.
• An online streaming service experiences downtime during the premiere of a highly anticipated show, as too many users try to access it simultaneously.

To manage high demand, companies should invest in scalable infrastructure, load-balancing, and load-scaling technologies. Conducting performance testing and having contingency plans for peak times can help ensure systems remain operational during spikes in usage.

4. Backup process

Failures in the backup process can lead to outages, especially when primary systems fail, and backups do not activate as expected. This can result from improperly configured backups, corrupted data, or insufficient testing.

Possible scenarios

• A data center experiences a power failure, but the backup generators fail to start, leading to prolonged downtime.
• A company tries to restore a system from backups after a cyberattack, only to find the backups are corrupted or incomplete.

It’s critical to regularly perform backup and recovery tests to ensure that systems are properly configured. Companies should ensure they have a range of recovery options in place, including snapshots, replication, and backups to provide a range of RTO and RPO options. A comprehensive DR plan with consistent testing is also critical to ensure that large recoveries work as expected.

5. Network issues

Network issues encompass problems with internet service providers, routers, or other networking equipment. These can be caused by hardware failures, or configuration errors, or external factors like cable cuts.

Possible scenarios

• A major network provider experiences an outage, causing disruptions to services that rely on its infrastructure.
• Misconfigured network settings result in lost connectivity, impacting cloud services and online applications.

To mitigate network issues, organizations should ensure robust network monitoring and management practices. Redundant network paths and automated failover systems can help maintain connectivity during disruptions.

6. Human error

Human error remains one of the leading causes of tech outages. This can include mistakes made during routine maintenance, misconfigurations, or accidental deletions.

Possible scenarios

• An IT technician accidentally deletes a critical database, causing a service outage.
• Incorrectly applied configuration changes lead to system failures and downtime.

Comprehensive training programs and strict change management protocols can help reduce human errors. Automated systems for routine tasks and thorough review processes for critical actions can also minimize the risk of mistakes.

Mitigating the causes of software outages

Understanding the diverse causes of tech outages is essential for developing strategies to prevent them, but it’s just the start. An effective mitigation strategy requires an observability solution that provides a complete end-to-end view of all applications and services. A platform such as Dynatrace enables companies to proactively identify issues, prioritize remediation, and validate that implemented fixes address the underlying issues. This approach minimizes the impact of outages on end users and maximizes the efficiency of IT remediation efforts.

The unfortunate reality is that software outages are common. However, by understanding the root causes of outages and implementing an observability platform, organizations can enhance the reliability and resilience of their technology infrastructure, ensuring continuity and maintaining trust in an increasingly digital world.

Contact us to learn how you can mitigate the causes of software outages in your IT environment to maintain business resilience.

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When it comes to critical applications and environments, organizations can’t afford to leave any stone unturned, as IT unknowns can have significant consequences. Any gaps, slowdowns, or potential issues can have a significant effect on an organization’s success — or lack thereof.

This is where observability analytics can help. Here’s a look at how it works, what it does, and its role in ensuring reliable operations.

What is observability analytics?

Observability analytics enables users to gain new insights into traditional telemetry data such as logs, metrics, and traces by allowing users to dynamically query any data captured and to deliver actionable insights. By connecting the dots between multiple points of observation, teams can take action to identify any potential issues or insights and understand it all.

With an all-source data approach, organizations can move beyond everyday IT fire drills to examine key performance indicators (KPIs) and service-level agreements (SLAs) to ensure they’re being met. They can identify and analyze trends to determine what short- and long-term futures may look like. And they can create relevant queries based on available data to answer questions and make business decisions.

Breaking down the benefits of observability analytics

Observability analytics offers organizations several benefits, including the following:

Uncovering unknown unknowns

Unknown unknowns are answers without questions. While measuring app response time under different circumstances provides a latency value, for example, it doesn’t tell you why the app is slow, fast, or somewhere in between. These unknowns are often tied to the root cause of IT issues.

Observability analytics can help teams solve for unknown unknowns. By analyzing the big picture of IT operations — including logs, metrics, traces, security reports, and usage data — and combining the output with advanced AI tool sets, organizations can conduct exploratory analytics that go beyond the surface to discover the why behind the what.

Breaking down data silos

Data silos remain a challenge for organizations. According to recent survey data, 79% of knowledge workers said teams in their organization are siloed, and 68% said these silos negatively affect their work. Part of the problem is time wasted searching for data, with workers reporting an average of 11.6 hours lost per week.

If teams can’t find the data they need when they need it, productivity declines. Collaboration is also challenging if teams need to work in tandem but have access to different data sets and applications.

Observability analytics can help identify and break down silos, providing common ground for inter-team efforts.

Democratizing data consumption

Democratizing data consumption means making data available and accessible. While many employees are familiar with IT processes, few are trained data practitioners. Observability platforms make it possible to capture and contextualize data, creating a shared foundation for staff.

Weighing the challenges of observability analytics

Implementing observability analytics comes with potential challenges. Common concerns include managing tool sprawl, creating context, and validating output.

Managing tool sprawl

More observability tools means more data — and more complexity. Consider that the average multicloud environment includes at least 12 different applications and services. To effectively monitor and manage these services, organizations often rely on multiple monitoring tools, each with its own feature set and focus. In some cases, these features overlap. In others, there may be gaps in observability that organizations can’t see.

Part of implementing effective observability, therefore, is minimizing the number of tools required to deliver actionable insight.

Creating context

Data without context is just noise. Best-case scenario: This noise distracts from but doesn’t derail effective analytics, costing companies time. Worst-case scenario: It becomes part of the decision-making process, potentially costing time and money.

Put simply, context is king. If solutions can’t provide context for collected data, they can do more harm than good.

Validating output

Analytical outputs aren’t guaranteed. Consider the recent rise of natural-language input AI tools. While these solutions make it easy for users to ask questions and get answers, these answers are only as accurate as the data available to the AI model. If the data is incomplete or inaccurate, so is the output. As a result, regular validation is critical to ensure accurate results.

Three components of successful observability analytics

Effective observation doesn’t happen automatically. Three components are critical to set the stage for successful observability analytics.

1. Automation

Based on the sheer volume and variety of data available to observability tools, IT automation is critical to ensure efficient operations. While human oversight is required to ensure outputs meet expectations, relying on manual processes to collect and correlate data is no longer feasible. In practice, teams need a combination of infrastructure and operations, digital processes, and automation tools to ensure information is effectively handled at every step of the observability process.

2. Streamlined data collection

Organizations also need tools that enable streamlined data collection. In practice, this means finding and implementing solutions capable of collecting data from multiple sources — such as cloud environments, co-located data centers, and on-site servers — and extracting key insights from this data, regardless of format.

This means observability solutions must be equally proficient at extracting structured usage from cloud services as they are at capturing data from on-premises mainframes.

3. Data lakehouse

Data lakes are a cost-efficient way to store information, while data warehouses provide contextual, high-speed querying capabilities. To make the most of observability analytics, organizations need both.

A data lakehouse such as Dynatrace Grail can combine the best features of lakes and warehouses, allowing them to effectively handle analytical and machine learning workloads.

Common use cases to consider

While observability analysis has no set boundary and can be used in any business application, several use cases are commonplace.

Exploratory analysis. Exploratory analytics enables teams to explore IT environments with observability solutions to discover unknown dependencies, interactions, or outcomes that help identify root causes.

Metrics-based performance thresholds. By observing current processes and outcomes, organizations can map these results onto service-level objectives, SLAs, service-level indicators, and KPIs to establish baselines and outliers. This allows for the creation of performance-based thresholds tied to concrete, observable metrics.

Consider the Dynatrace Carbon Impact app, which provides an overview of your current carbon footprint compared to previous time frames and recommendations to reduce your overall impact. By leveraging observability analytics, organizations can establish a baseline carbon footprint level along with emissions targets and upper limits that trigger a notification event when reached.

Predictive analysis. Complete visibility of IT infrastructure sets the stage for predictive analytics that can help inform key business objectives. For example, observability analytics can provide data on current infrastructure usage and any bottlenecks or performance problems. Equipped with this data, teams are better prepared to anticipate application, bandwidth, and security needs over time, streamlining the infrastructure scaling process.

Taking observability analytics to the next level

With Dynatrace, observability comes standard with built-in solutions. Here are the main functions teams can use to discover new insights:

Data forecasting. With Dynatrace Grail and Davis AI, organizations can predict capacity demands and make proactive changes. Using the Dynatrace Query Language (DQL), they can analyze all data stored within the Dynatrace Grail data lakehouse for any given time series. Davis AI then analyzes the time series and automatically chooses the best prediction model.

Predictive analytics. Davis AI can also help predict the impact of multiple workflows on business operations. This assists with long-term capacity planning and helps IT leaders make short-term decisions that boost operational efficiency.

Exploratory analytics. Using Dynatrace Notebooks, teams can carry out exploratory analytics for observability, security, and business data analysis. With Notebooks, users can collaborate using code, text, and rich media to build and share insights.

With observability analytics powered by Dynatrace, teams are better equipped to discover unknown unknowns, understand their impact, and take action that improves business operations. For more information on observability analytics from Dynatrace, check out our website.

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As organizations embrace cloud-native technologies, system architectures have dramatically increased in complexity and scale. Customer experiences are more important than ever, and as a result, IT teams face mounting pressure to track and respond to issues much faster. To address these challenges, teams are turning to observability solutions so they can proactively identify and resolve issues and automate workflows in their highly distributed and complex computing environments. But what is observability, and what do teams need to do it right?

What is observability?

In IT and cloud computing, observability is the ability to measure a system’s current state based on the data it generates, recorded as logs, metrics, and traces:

• Logs record the details of an event
• Metrics capture the numeric measurements used to quantify the performance and health of services
• Traces track how services connect from end to end in response to requests

Observability has become more critical in recent years as cloud-native environments have gotten more complex, and the potential root causes for a failure or anomaly have become more difficult to pinpoint.

Because cloud services rely on a distributed and dynamic architecture, observability may also refer to the specific software tools and practices organizations use to interpret cloud performance data.

How observability works

Observability relies on telemetry derived from instrumentation that comes from the endpoints and services in your multicloud computing environments. In these modern environments, every hardware, software, and cloud infrastructure component and every container, open source tool, and microservice generates records of every activity. The goal of observability is to understand what’s happening across all these environments and among the technologies, so you can detect and resolve issues to keep your systems efficient and reliable and your customers happy.

Implementing observability

Organizations usually implement observability using a combination of instrumentation methods, including open source instrumentation tools, such as OpenTelemetry.

Many organizations also adopt an observability solution to help them detect and analyze the significance of events to their operations, software development life cycles, application security, and end-user experiences.

As teams begin collecting and working with observability data, they are also realizing its benefits to the business, not just IT.

Although some people may think of observability as a buzzword for sophisticated application performance monitoring (APM), there are a few key distinctions to keep in mind when comparing observability and monitoring.

Monitoring vs. observability: What’s the difference between monitoring and observability?

Is observability really monitoring by another name? In short, no. While observability and monitoring are related—and can complement one another—they are actually different concepts.

Monitoring

In a monitoring scenario, you typically preconfigure dashboards to alert you about performance issues you expect to see later. However, these dashboards rely on the key assumption that you’re able to predict what kinds of problems you’ll encounter before they occur.

Cloud-native environments don’t lend themselves well to this type of monitoring because they are dynamic and complex, which means you cannot predict what problems might arise in advance.

Observability

In an observability scenario, where teams have fully instrumented an environment to provide complete observability data, you can flexibly explore what’s going on and quickly figure out the root cause of issues you may not have been able to anticipate.

Traditionally, the industry defines observability as logs, metrics, and traces. In more complex cloud environments, however, observability must encompass more, including metadata, user behavior, topology and network mapping, and access to code-level details.

Why is observability important?

In enterprise environments, observability helps cross-functional teams understand and answer specific questions about what’s happening in highly distributed systems. Observability enables you to understand what is slow or broken and what you need to do to improve performance. With an observability solution in place, teams can receive alerts about issues and proactively resolve them before they impact users.

Understanding “unknown unknowns”

Because modern cloud environments are dynamic and constantly changing in scale and complexity, teams neither know about nor can monitor most problems. Observability addresses this common issue of “unknown unknowns,” enabling you to continuously and automatically understand new types of problems as they arise.

Automating AIOps and DevSecOps

Observability is also a critical capability of artificial intelligence for IT operations (AIOps). As more organizations adopt cloud-native architectures, they are also looking for ways to implement AIOps, harnessing AI as a way to automate more processes throughout the DevSecOps lifecycle. By bringing AI to everything—from gathering telemetry to analyzing what’s happening across the full technology stack—your organization can have the reliable answers essential for automating application monitoring, testing, measuring service level objectives (SLOs), continuous delivery, application security, and incident response.

Optimizing user experiences

The value of observability doesn’t stop at IT use cases. Once you begin collecting and analyzing observability data, you have an invaluable window into the business impact of your digital services. This visibility enables you to optimize conversions, validate that software releases meet business goals, and prioritize business decisions based on what matters most.

When an observability solution also analyzes user experience data using synthetic and real-user monitoring, you can discover problems before your users do and design better user experiences based on real, immediate feedback.

Benefits of observability

Observability delivers powerful benefits to IT teams, organizations, and end users alike. Following are some of the use cases observability facilitates.

1 Application performance monitoring

Full end-to-end observability enables organizations to get to the bottom of application performance issues much faster, including issues that arise from cloud-native and microservices environments. Teams can also use an advanced observability solution to automate more processes, which increases efficiency and innovation among Ops and Apps teams.

2 DevSecOps and SRE

Observability is not just the result of implementing advanced tools but a foundational property of an application and its supporting infrastructure. The architects and developers who create the software must design it to be observed. Then DevSecOps and SRE teams can leverage and interpret the observable data during the software delivery lifecycle to build better, more secure, and more resilient applications.

3 Monitoring infrastructure, cloud, and Kubernetes environments

Infrastructure and operations (I&O) teams can leverage the enhanced context an observability solution offers for monitoring on-premises and cloud infrastructure and Kubernetes environments. This unified observability-based approach can improve application uptime and performance, cut down the time required to pinpoint and resolve issues, detect cloud latency issues, optimize cloud resource utilization, and improve administration of their Kubernetes environments and modern cloud architectures.

4 End-user experience

A good user experience can enhance a company’s reputation and increase revenue, delivering an enviable edge over the competition. By spotting and resolving issues well before the end-user notices and making an improvement before it’s even requested, an organization can boost customer satisfaction and retention. It’s also possible to optimize the user experience through real-time playback, gaining a window directly into the end-user’s experience exactly as they see it, so everyone can quickly agree on where to make improvements.

5 Business analytics

Business analytics enable organizations to combine business context with full stack application analytics and performance to understand real-time business impact, improve conversion optimization, ensure that software releases meet expected business goals, and confirm that the organization is adhering to internal and external SLAs.

6 DevOps and DevSecOps automation

DevSecOps teams can tap observability to get more insights into the apps they develop, and automate testing and CI/CD processes so they can release better quality code faster. This means organizations waste less time on war rooms and finger-pointing. Not only is this a benefit from a productivity standpoint, but it also strengthens the positive working relationships that are essential for effective collaboration.

These organizational improvements open the door to further innovation and digital transformation. And more importantly, the end-user ultimately benefits in the form of a high-quality user experience.

How do you make a system observable?

If you’ve read about observability, you likely know that collecting the measurements of logs, metrics, and distributed traces are the three key pillars to achieving success. However, observing raw telemetry from back-end applications alone does not provide the full picture of how your systems are behaving.

Neglecting the front-end perspective potentially skews or even misrepresents the full picture of how your applications and infrastructure are performing in the real world for real users. Extending the three-pillars approach, IT teams must augment telemetry collection with user-experience data to eliminate blind spots:

1. Logs: Logs are structured or unstructured text records of discreet events that occurred at a specific time.
2. Metrics: Metrics are the values represented as counts or measures that are often calculated or aggregated over a period of time. Metrics can originate from a variety of sources, including infrastructure, hosts, services, cloud platforms, and external sources.
3. Distributed tracing: Tracing follows the activity of a transaction or request as it flows through applications and shows how services connect, including code-level details.
4. User experience: User experience data extends traditional observability telemetry by adding the outside-in user perspective of a specific digital experience on an application, even in pre-production environments.

Why the three pillars of observability aren’t enough

Obviously, data collection is only the start. Simply having access to the right logs, metrics, and traces isn’t enough to gain true observability of your environment. Once you’re able to use that telemetry data to achieve the end goals of improving end-user experience and business outcomes, only then can you really say you’ve achieved the purpose of observability.

The importance of open source solutions

There are other observability capabilities organizations can use to observe their environments. Open source solutions, such as OpenTelemetry, provide a de facto standard for collecting telemetry data in cloud settings. These open source solutions enhance observability for cloud-native applications and make it easier for developers and operations teams to achieve a consistent understanding of application health across multiple environments.

The role of real-user monitoring (RUM) and synthetic testing

Organizations can also use real user monitoring to gain real-time visibility into the user experience, tracking the path of a single request and gaining insight into every interaction it has with every service along the way. Teams can observe this experience using synthetic monitoring or even view a recording of the actual session. These capabilities extend telemetry by adding in data for APIs, third-party services, errors occurring in the browser, user demographics, and application performance from the user’s perspective.

With real-user monitoring IT, DevSecOps, and SRE teams can not only see the complete end-to-end journey of a request but also access real-time insight into system health. From there, they can proactively troubleshoot areas of degrading health before they impact application performance. They can also more easily recover from failures and gain a more granular understanding of the user experience.

Don’t forget over-burdened teams

While IT organizations have the best of intentions and strategy, they often overestimate the ability of already overburdened teams to constantly observe, understand, and act upon an impossibly overwhelming amount of data and insights. Although there are many complex challenges associated with observability, the organizations that overcome these challenges will find it worth their while.

What are the challenges of observability?

Observability has always been a challenge, but cloud complexity and the rapid pace of change have made it an urgent issue. Cloud environments generate a far greater volume of telemetry data, particularly in microservices and containerized application environments. They also create a far greater variety of telemetry data than teams have ever had to interpret in the past. Lastly, the velocity with which all this data arrives makes it that much harder to keep up with the flow of information, let alone accurately interpret it in time to troubleshoot a performance issue.

Organizations also frequently run into the following challenges with observability.

1 Data silos

Multiple agents, disparate data sources, and siloed monitoring tools make it hard to understand interdependencies across applications, multiple clouds, and digital channels, such as web, mobile, and IoT.

2 Volume, velocity, variety, and complexity

It’s nearly impossible to get answers from the sheer amount of raw data collected from every component in ever-changing modern cloud environments, such as AWS, Azure, and Google Cloud Platform (GCP). This is also true for Kubernetes and containers that can spin up and down in seconds.

3 Manual instrumentation and configuration

When IT resources are forced to manually instrument and change code for every new type of component or agent, they spend most of their time trying to set up observability rather than innovating based on insights from observability data.

4 Lack of pre-production

Even with load testing in pre-production, developers still don’t have a way to observe or understand how real users will impact applications and infrastructure before they push code into production.

5 Wasting time troubleshooting

Application, operations, infrastructure, development, and digital experience teams are pulled in to troubleshoot and try to identify the root cause of problems, wasting valuable time guessing and trying to make sense of telemetry and come up with answers.

6 Multiple tools and vendors

While a single tool may give an organization observability of one specific area of their application architecture, that one tool may not provide complete observability across all the applications and systems that can affect application performance.

7 Inability to determine root-causes

Also, not all types of telemetry data is equally useful for determining the root cause of a problem or understanding its impact on the user experience. As a result, teams waste time digging for answers across multiple solutions and painstakingly interpreting the telemetry data when they could be applying their expertise toward fixing the problem right away.

However, with a single source of truth, teams can get answers and troubleshoot issues much faster.

The importance of a single source of truth

Organizations need a single source of truth to gain complete observability across their application infrastructure and accurately pinpoint the root causes of performance issues. When organizations have a single platform that can tame cloud complexity, capture all the relevant data, and analyze it with AI, teams can instantly identify the root cause of any problem, whether it lies in the application itself or the supporting architecture.

A single source of truth enables teams to do the following:

• Turn terabytes of telemetry data into real answers rather than asking IT teams to cobble together an understanding of what has happened using snippets of data from disparate sources
• Gain crucial contextual insights into areas of the infrastructure they might not have otherwise been able to see
• Work collaboratively and accelerate the troubleshooting process further, which empowers the organization to act faster than it could by using traditional monitoring tools thanks to enhanced awareness

Making observability actionable and scalable for IT teams

To achieve observability, resource-constrained teams need to be able to collect and act upon a deluge of telemetry data in real time. Responding in real time enables teams to prevent business-impacting issues from propagating further or even occurring in the first place. Following are some ways teams can make observability actionable and scalable.

1 Understand the context and the topology

Understanding the context and topology of an IT environment involves instrumenting applications and infrastructure in a way that identifies relationships between every entity and interdependency among potentially billions of interconnected components. Rich context metadata enables real-time topology maps, providing an understanding of causal dependencies both vertically throughout the stack and horizontally across services, processes, and hosts.

2 Implement continuous automation

Automatic discovery, instrumentation, and baselining of every system component on a continuous basis shifts IT effort away from manual configuration work to value-add innovation projects that can prioritize understanding of the things that matter. Observability becomes “always-on” and scalable, so constrained teams can do more with less.

3 Establish true AIOps

Exhaustive AI-driven fault-tree analysis combined with code-level visibility enables teams to automatically pinpoint the root cause of anomalies without having to rely on time-consuming human trial and error. Additionally, causation-based AI can automatically detect any unusual change points to discover “unknown unknowns” that teams are not aware of or monitoring. These actionable insights drive the faster and more accurate responses that DevOps and SRE teams require.

4 Foster an open ecosystem

An open ecosystem extends observability to include external data sources, such as OpenTelemetry, which is an open-source project led by vendors such as Dynatrace, Google, and Microsoft. OpenTelemetry expands telemetry collection and ingestion for platforms that provide topology mapping, automated discovery and instrumentation, and actionable answers required for observability at scale.

5 Utilize AI

An AI-driven solution-based approach makes observability truly actionable by solving the challenges associated with cloud complexity. An observability solution makes it easier to interpret the vast stream of telemetry data arising from multiple sources at increasingly greater velocities. With a single source of truth, teams can quickly and accurately pinpoint root causes of issues before they result in degraded application performance or, in the event a failure has already occurred, accelerate their time to recovery.

Advanced observability also improves application availability through end-to-end distributed tracing across serverless platforms, Kubernetes environments, microservices, and open-source solutions. By gaining visibility into the complete journey of a request from start to finish, teams can proactively identify application performance issues and gain crucial insight into the end-user experience. This way, IT teams can quickly act on issues of concern, even as the organization scales its application infrastructure to support future growth.

Bring observability to everything

You can’t waste months or years trying to build your own tools or test out multiple vendors that only enable you to solve one piece of the observability puzzle. Instead, you need a solution that can help make all your systems and applications observable, give you actionable answers, and provide technical and business value as fast as possible.

Advanced observability from Dynatrace provides all these capabilities in a single platform, empowering your organization to tame modern cloud complexity and transform faster. Now, more than ever, it’s critical to make comprehensive observability part of every cloud migration. At Dynatrace, we call that approach cloud done right.

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Because open source software (OSS) is taking over the world, optimizing open source contributions is becoming an essential competitive strategy. OSS is a faster, more collaborative, and more flexible way of driving software innovation than proprietary-only code. This flexibility appeals to developers and can help organizational leadership drive down costs while supporting digital transformation goals. The figures speak for themselves: 80% of organizations increased their OSS use in 2022. Especially those operating in critical infrastructure sectors such as oil and gas, telecommunications, and energy.

However, open source is not a panacea. Governance, security, and balancing between contributing to OSS development and preserving a commercial advantage pose challenges for many organizations. These considerations are important if developers want to maximize the impact of their contributions to open source projects.

Benefits of making open source contributions

There’s no one-size-fits-all approach with OSS. Projects could range from relatively small software components, such as general-purpose Java class libraries, to major systems, such as Kubernetes for container management or Apache’s HTTP server for modern operating systems. Organizations are more likely to adopt projects that receive regular contributions and updates from reputable developers. These projects provide a range of proven benefits.

1 Saves time and resources

Open source can save time and resources, as developers don’t have to expend their own energies to produce code. One study estimates that the top four OSS ecosystems recorded more than three trillion component requests last year. That utilization represents a great deal of time and effort developers are saving. That saving means teams can devote more time to developing proprietary functionality to boost revenue streams. The same study estimates that $1.1 billion in OSS investments in the EU generated around $100 billion.

2 Benefits from diverse contributions

OSS also encourages experts from across the globe—whether individual hobbyists or DevOps teams from multinational companies—to contribute their coding skills and industry knowledge. The idea is projects benefit from a diverse pool of developers, driving up the quality of the final product. In contributing to these projects, businesses and individuals can also stake a claim to the future direction of a particular product or field of technology. With such a stake, they can help shape the technology to advance their priorities. Organizations also benefit from being at the leading edge of any new discoveries and innovations. Fostering such involvement can help organizations gain an advantage over the competition by being first to market.

3 Drives a culture of innovation

Organizations that regularly contribute to OSS can also help to drive a culture of innovation. Alongside an organization’s track record on patents, a commitment to OSS projects can allow prospective new hires to contribute their own innovation, which can help attract the brightest and best talent.

Three ways to get the most out of open source contributions

To maximize the benefit of their contributions to the OSS community, DevOps leaders should ensure their organization has a clear, strategic approach. There are three key points to consider in these efforts:

1 Define the scope of the organization’s contribution

OSS is built on the expertise of a potentially wide range of individuals and organizations, many of whom are otherwise competitors. This “wisdom of the crowd” can ultimately help to create better-quality products more quickly. However, it can also raise difficult questions about how to keep proprietary secrets under wraps. Often, there is pressure from the community to share certain code bases or functionality that could benefit others. By defining what they want to keep private at the outset, contributors can draw a clear line between commercial advantage and community benefit.

2 Contribute to open standards

Open standards are the foundation on which OSS contributors can collaborate. By contributing to these initiatives, organizations have a fantastic opportunity to shape the future direction of OSS. Helping to solve common problems can enhance the value of their own commercial products. OpenTelemetry is one such success story. This collection of tools, APIs, and SDKs enables organizations to capture and export telemetry data from applications to make tracing more seamless across boundaries and systems. As a result, OpenTelemetry has become a de facto industry standard for how organizations capture and process observability data. OpenTelemetry brings organizations closer to achieving a unified view of hybrid technology stacks in a single platform.

3 Build robust security practices

Despite the benefits of OSS, there’s always a risk of vulnerabilities slipping into production if teams don’t detect and remedy them quickly and effectively in development environments. Three-quarters (75%) of chief information security officers (CISOs) worry the prevalence of team silos and point solutions throughout the software development lifecycle makes it easier for vulnerabilities to fly below the radar. Their concerns are valid. For example, according to one estimate, the average application development project contains 49 vulnerabilities. These risks will only grow as more organizations adopt ChatGPT-like tools to support software development by compiling code snippets from open source libraries.

Essential capabilities needed to support open source contributions

Given the dynamic, fast-changing nature of cloud-native environments and the increasing use of open source software, teams need a way to manage and monitor OSS at scale. To support safe and efficient OSS development and usage, teams should enlist a unified source of end-to-end observability. By combining comprehensive OSS observability data with trustworthy AI, teams can understand the full context behind what OSS they use and how. With this insight, organizations can unlock precise, real-time answers about OSS-related vulnerabilities in their environment. In turn, DevOps teams can implement security and quality gates throughout the delivery pipeline. These gates help teams automatically detect and resolve vulnerabilities and bugs before releasing software into production.

OSS is increasingly important to long-term success, even for commercially motivated organizations. Developing a strategy for effectively using and contributing to key OSS projects can help determine an organization’s success.

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Today’s organizations are constantly enhancing their systems and services as new opportunities arise, inspiring new forms of collaboration while relying on open ecosystems and open source software. However, while open ecosystems offer benefits such as increased flexibility, faster development, and improved collaboration, they also present new observability challenges. In turn, this drives the need for increased integration of heterogeneous telemetry data such as metrics, logs, and traces, and intelligent awareness of context across disparate data types.

To realize the benefits of open ecosystems, organizations must plan for ecosystem-level observability.

What is an open ecosystem?

In software computing, an open ecosystem is an operating model in which organizations and applications share data and services so they can jointly create more value for customers than they could on their own.

Open source software is an example of the value created in an open ecosystem. It enables organizations to benefit from collective innovation for common tasks so they can concentrate on building their own IP. A single entity does not control open ecosystems. Therefore, anyone can contribute to them and use their resources.

A closed ecosystem, on the other hand, is controlled by one entity and follows rigid protocols for interaction. Rather than looking at the big picture, in a closed ecosystem, teams focused on point-to-point integrations that fixed small problems. This led to scalability issues, as teams were stuck managing dozens of custom integrations where a small issue could lead to a major problem.

Bringing observability into open ecosystems

Open ecosystems are the product of multiple systems’ interactions, and they generate logs and other metrics that reflect the state of the ecosystem. These logs and metrics are distinct from the logs, metrics, and traces of individual components. This means while observability into individual components is essential, organizations also need to plan for how to support observability at the open ecosystem level.

The key to observability in these systems is the ability to collect and integrate metrics, logs, and trace data from components. Fortunately, open source software can help with this. For instance, organizations frequently use OpenTelemetry to instrument microservices, Fluentd for collecting logs, and Prometheus for collecting metrics.

Collecting data that supports observability is just one part of ensuring observability in open ecosystems. Organizations also need AIOps to integrate data, provide context, and find meaningful signals across metrics, logs, and traces.

How open ecosystems change the way organizations work

The ways in which businesses and organizations work together are evolving due, in large part, to open ecosystems. In the past, for example, collaborators might create a point-to-point integration between two closed systems, which solved a specific problem but limited innovation and made it difficult to adapt to changing customer needs. Today, fast-moving organizations operate with an open ecosystem, which facilitates faster development and encourages partner integrations.

These ecosystems promote communities and help expand collaboration across environments. Collaborations between closed-system vendors may have created a deep relationship between two entities, but open ecosystems tend to foster a community approach to identifying new functionality and working collaboratively to implement those new features.

Reaping the benefits of an open ecosystem and combatting the challenges

Open ecosystems offer myriad benefits, including the flexibility of having multiple open source tools and components to solve specific problems. However, taking a siloed approach creates more challenges.

Teams are committed to specific tools and platforms with which they are comfortable and familiar. While change can be painful, maintaining hundreds of manual integrations does not work. That’s why organizations need an open ecosystem platform that can take data from any source and provide automation and intelligence. This provides the answers and observability organizations need to run the business while giving business units the flexibility to use whatever they need. Further, leveraging open standards allows simpler collaboration and integration.

While open ecosystems foster communication, collaboration, and integration to avoid breaking changes, end-to-end observability is crucial for managing burgeoning open source tools. Today’s organizations need a full-stack observability platform that can spot potentially breaking changes automatically. That’s where Dynatrace can help.

How Dynatrace embraces open ecosystems

The Dynatrace platform simplifies the open ecosystem experience with one platform for any data source. Dynatrace brings all an organization’s open source data into one place. There, the Davis AI engine monitors this data in context. Additionally, Dynatrace provides organizations with more than 625 integrations, including AWS Lambda, Microsoft Azure Functions, Google Cloud Functions, and more.

To learn more about how Dynatrace can help with your adoption of open ecosystems, particularly around support for observability, download the ebook, “OpenTelemetry and the opportunity for intelligent observability.” There, you can delve into more details about how a common platform for collecting application and infrastructure telemetry data combined with AI-based observability at scale can help improve developer collaboration and further realize the benefits of a new way to collaborate.

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Organizations are depending more on distributed architectures to provide application services. This trend is prompting advances in both observability and monitoring. But exactly what are the differences between observability vs. monitoring?

Understanding when something goes wrong along the application delivery chain is essential so you can identify the root cause and correct it before it impacts your business. Monitoring and observability provide a two-pronged approach. Monitoring supplies situational awareness, and observability helps pinpoint what’s happening and what to do about it.

To better understand of observability vs. monitoring, we’ll explore the differences between the two. Then we’ll look at how you can best utilize both to improve business outcomes.

Monitoring vs. observability

First, let’s define what we mean by observability and monitoring.

What is meant by monitoring?

By textbook definition, monitoring is the process of collecting, analyzing, and using information to track a program’s progress toward reaching its objectives and to guide management decisions. Monitoring focuses on watching specific metrics. Logging provides additional data but is typically viewed in isolation of a broader system context.

What is meant by observability?

Observability is the ability to understand a system’s internal state by analyzing the data it generates, such as logs, metrics, and traces. Observability helps teams analyze what’s happening in context across multicloud environments so you can detect and resolve the underlying causes of issues.

What is the difference between observability and monitoring?

Monitoring is capturing and displaying data, whereas observability can discern system health by analyzing its inputs and outputs. For example, we can actively watch a single metric for changes that indicate a problem — this is monitoring. A system is observable if it emits useful data about its internal state, which is crucial for determining the root cause.

What are the similarities between observability and monitoring?

Observability and monitoring are closely related concepts in systems and software engineering. Both aim to provide insights into the health, performance, and behavior of a system. They utilize data collection, analysis, and visualization techniques to enable proactive detection and troubleshooting of issues. Ultimately, they empower engineers to ensure system reliability, performance optimization, and efficient resource utilization.

Between observability and monitoring, which is better?

So how do you know which model is best for your environments?

Monitoring typically provides a limited view of system data focused on individual metrics. This approach is sufficient when systems failure modes are well understood. Because monitoring tends to focus on key indicators such as utilization rates and throughput, monitoring indicates overall system performance. For example, when monitoring a database, you’ll want to know about any latency when writing data to a disk or average query response time. Experienced database administrators learn to spot patterns that can lead to common problems. Examples include a spike in memory utilization, a decrease in cache hit ratio, or an increase in CPU utilization. These issues may indicate a poorly written query that needs to be terminated and investigated.

Conventional database performance analysis is simple compared to diagnosing microservice architectures with multiple components and an array of dependencies. Monitoring is helpful when we understand how systems fail, but as applications become more complex, so do their failure modes. It is often not possible to predict how distributed applications will fail. By making a system observable, you can understand the internal state of the system and from that, you can determine what is not working correctly and why.

However, correlations between a few metrics often do not diagnose incidents in modern applications. Instead, these modern, complex applications require more visibility into the state of systems, and you can accomplish this using a combination of observability and more powerful monitoring tools.

The “three pillars” of observability and beyond

As mentioned earlier, traditionally, observability is understanding what’s happening inside a system from its logs, metrics, and traces. Modern observability includes these three original pillars along with user experience and security. Systems are observable when they generate and readily expose the type of data that enables you to evaluate the state of the system. Here’s a closer look at logs, metrics, distributed traces, user experience, and security.

• Logs include application- and system-specific data that details the operations and flow of control within a system. Log entries describe events, such as starting a process, handling an error, or simply completing some part of a workload. Logging complements metrics by providing context for the state of an application when metrics are captured. For example, log messages might indicate a large percentage of errors in a particular API function. At the same time, metrics on a dashboard are showing resource exhaustion issues, such as a lack of available memory. Metrics may be the first sign of a problem, but logs can provide details about what is contributing to the problem and how it impacts operations.
• Metrics in this context are sets of measurements taken over time, and there are a few types:
  • Gauge metrics measure a value at a specific point in time, such as the CPU utilization rate at the time of measurement.
  • Delta metrics capture differences between previous and current measurements, such as a change in throughput since the last measurement.
  • Cumulative metrics capture changes over time — for example, the number of errors returned by an API function call in the last hour.
• Distributed tracing is the third pillar of observability and provides insights into the performance of operations across microservices. An application may depend on multiple services, each with its own set of metrics and logs. Distributed tracing is observing requests as they move through distributed cloud environments. In these complex systems, traces highlight any problems that can happen with the relationships among services.
• User experience considers how users interact with the front end; understanding where time is spent and which actions are critical helps prioritize and identify users’ needs. This is essential when the goal is to deliver an exceptional customer experience. This important piece of the puzzle takes into consideration things like revenue, conversions, and customer engagement. All of these are important inputs to get a full understanding of the application landscape.
• Security is an essential component in understanding the internal state of a system. Organizations are shifting away from siloed security teams and taking a DevSecOps approach. This includes security at each stage of the SDLC. So should it be in observability, where security is one element that affects the health, performance, and customer experience of an application.

True observability, however, relies on more data than just key indicators.

Why monitoring and observability need a next-gen approach

When trying to effectively monitor, manage, and improve complex microservices-based applications, observability and monitoring are both vital. Monitoring and observability represent a continuum from basic telemetry of single servers to profound insights about complete applications and dependencies.

Many organizations start with monitoring and realize these tools lack contextual insights. Context is critical to understanding why problems exist and how they impact the business. Organizations look to observability to provide the data they need for contextual analysis. Understanding the problem means they can understand the root cause and its effects.

DevOps practitioners need help to maintain highly available and scalable applications. That’s because these complex, interdependent systems behave in unpredictable ways and issues originate from sources that are often not apparent. Practices and tools that worked when we built monolithic applications simply can’t handle the level of data distributed environments generate. They don’t ingest enough data or provide enough insight into the state of applications to understand how to correct problems quickly. Luckily, some tools and practices address these challenges.

An automatic and intelligent approach to monitoring and observability

An advanced software intelligence solution like Dynatrace automatically collects and analyzes highly scalable data to make sense of these sprawling multicloud environments. Dynatrace’s causal AI engine, Davis, sifts through massive volumes of disparate, high-velocity data streams, and analyzes them through a unified interface. This single source of truth tears down information silos that traditionally separate teams that perform different functions on many application components. This centralized, automatic approach eliminates the need for manual diagnostics. It also provides paths to remediation to keep the technology users rely on functioning smoothly.

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As the pace of business quickens, software development has adapted. Increasingly, teams release software features more quickly to accommodate customer needs. As a result, organizations are weighing microservices vs. monolithic architecture to improve software delivery speed and quality.

Traditional monolithic architectures are built around the concept of large applications that are self-contained, independent, and incorporate myriad capabilities. As developers move to microservice-centric designs, components are broken into independent services to be developed, deployed, and maintained separately. Shifting from monolith to microservices makes it easier to test, develop, and release innovative features more rapidly.

Data supports this shift from monolithic architecture to microservices approaches. IDC predicted, by 2022, 90% of all applications will feature microservices architectures that improve the ability to design, debug, update, and use third-party code.

According to IDC, the requirement of the digital economy to deliver high-quality applications at the speed of business has driven a shift to highly modular, distributed, and continuously updated microservices-based architectures that use cloud-native technologies. Combined with Agile or DevOps approaches and methodologies, enterprises can accelerate their ability to deliver digital services.

C-level executives “must race to reinvent their organizations for the fast-paced, multiplied innovation world,” says Frank Gens, senior vice president and chief analyst at IDC. “This means reinventing IT around a distributed cloud infrastructure, public cloud software stacks, agile and cloud-native app development and deployment, AI as the new user interface, and new, pervasive approaches to security and trust at scale.”

In developing critical applications and services, it’s crucial to understand legacy software development.

What is monolithic architecture?

Monolithic architecture is development where an application is built on a single codebase, and the code is unilateral. Generally speaking, monolithic architecture is composed of three parts:

• Database. This is usually a relational database management system.
• Client-side user interface (UI). The UI generally consists of HTML pages or JavaScript running within a browser.
• Server-side application. This handles HTTP requests and executive domain-specific logic. Additionally, it will populate HTML views directed to the browser and retrieve, update, and modify data from the associated database.

The number of modules within an application depends on an organization’s complexity and the corresponding technical features. However, in a monolithic architecture, the entire application — including dependencies — is built on a single platform with a single executable for deployment. So, to make changes to the system, the development team needs to build and deploy an updated version of the server-side app.

Monolithic architecture pros

• Easier to develop. Working with a single executable is simple. So, for straightforward applications or the beginning of a development project, a monolithic architecture is easier. However, as development progresses and complexities arise, monolithic environments can become a drawback.
• Easier to test. Due to the nature of the application, you can simply launch the app and test the user interface with a given tool. Using one executable means there’s only one application you need to set up for logging, monitoring, and testing.
• Easier to deploy. There’s much less complexity when working with a single executable. To deploy to other systems, you need to copy the packaged application to a different server and run it.
• Less complex and lower overhead. Microservices can become complex quickly. However, in a monolithic architecture, you can avoid additional costs associated with interservice communication, service discovery and registration, load balancing, distributed logging, distributed performance monitoring and management, and data management.

Monolithic architecture cons

• Too tightly coupled. Although a monolithic application can be easier to work with initially, it becomes more challenging as the application evolves. Development tasks, such as isolating services for independent scaling or code maintainability, become more difficult.
• App architecture is too inflexible to evolve. Today’s applications evolve quickly. As the application rapidly develops layers and interdependencies, it becomes increasingly challenging to understand the codebase and its underlying dependencies.
• Challenging during build, test, and release cycles. While simple applications are easy to work with, they are difficult to update. Developers may need to recode the entire application and service.
• Hard on DevOps. A significant part of DevOps discipline is effectively working in teams to distribute application and service development. Breaking monolithic applications into disparate parts that can be developed separately is challenging, which limits DevOps’ ability to work in a distributed fashion.
• Limited because of a single programming language. Most monolithic apps rely on a single programming language. The challenge is, as the application grows and becomes more complex, you may need to integrate components written in different languages. A monolithic app poses challenges in incorporating other parts of apps or services written in a different codebase. As a result, this hamstrings DevOps teams’ ability to add features and functions that could’ve been better written in a different programming language.
• Difficulty integrating third-party tools. It’s challenging to deploy a third-party tool that requires complicated connections to different parts of the monolithic application. Because the app can’t be broken into chunks, developers need to jump through hoops to integrate a single codebase with a third-party service with dependencies and other requirements. This complicates the adoption of third-party tools. Adding self-contained, third-party components to a single codebase with multiple dependencies requires complicated hookups to different layers of a monolithic application.

What is microservices architecture?

As opposed to monolithic architecture, microservices are an approach to developing a single app as a suite of small services. Each of those services runs in its own process and communicates with lightweight mechanisms. Microservices often accomplish this communication with an HTTP resource application programming interface (API).

Another critical factor is microservices’ capabilities are expressed with business-oriented APIs, and the implementation of the service is defined purely in business terms. Additionally, these microservices are independently deployable by fully automated deployment tools.

Finally, applying the principle of loose coupling minimizes the dependencies between services and users. This allows the development owners of certain services or parts of the application to change the implementation and modify the systems of record or service compositions without downstream effect.

Microservices pros

• Improved continuous integration/continuous delivery. Because the architecture decouples services, DevOps teams can scale complex applications in a more straightforward manner. Instead of one extensive application, teams can work on application pieces to ensure a better development pipeline.
• Better testing. With smaller services, it’s easier to test and monitor application performance and components. This simplifies visibility and enables faster testing of specific application parts.
• Easier deployment. Instead of deploying one large application, you can deploy specific services to support your application. Furthermore, you can deploy services independently without affecting downstream operations.
• Improved distributed team functionality. With independent services, teams can own parts of the development effort. Each team can develop, deploy, and scale services independently of other teams.
• Easier to understand. Microservice size is relative to the project and overall application. However, loose coupling and service separation make microservices and the entire application architecture easier to understand. A developer can focus on one service or see how different independent services affect the application overall.
• Faster performance. Microservices are decoupled and independent. Therefore, DevOps teams can better control application performance, so applications can start faster and run more efficiently. This improved performance makes developers more productive and speeds deployments.
• Improved fault isolation. With independent, isolated services, you can find application faults faster than with one monolithic executable. For example, you can isolate a memory leak to one service instead of an entire application. Additionally, you can adjust that service while allowing other microservices to support the application. A single memory leak could take down the entire application in a monolithic architecture.
• Less code and stack lock-in. You’re not limited to one codebase with microservices. This eliminates any long-term commitments to a technology stack. You can keep parts of the application on one platform while designing a new service on a different stack.

Microservices cons

• Additional complexity. Developers need to become accustomed to working with the complexity of a distributed system.
• Transition challenges. If an organization primarily uses monolithic applications, then it’s more difficult for teams to develop distributed applications. Teams need to do an inventory of exiting tools and development practices before moving to microservices. Remember, it’s often a massive undertaking to refactor an application built on monolithic architecture.
• Complicated testing. Because teams no longer work with one executable, they have more services and pieces of an application to test.
• Interservice communication needs. Unlike a single monolithic application, DevOps teams must ensure microservices can talk to one another. Developers need to deploy an interservice communication mechanism to support the app.
• Careful deployments. If an application spans multiple services, it will require careful coordination between teams to deploy it properly. Further, deploying and managing a system in production that’s composed of different microservice types introduces complexity.
• Increased resource consumption. Rather than a single application instance, DevOps teams must provision resources — memory, CPU, and disk — to accommodate each cluster or service requirement.

Microservices vs. monolithic architecture: What are the differences?

Monolithic vs. microservices architecture features two main differences at a high level:

• Monolithic architecture is a single, extensive, executable application.
• Microservices are a set of loosely decoupled services to support larger application deployments.

Microservices architecture provides a different approach to software development. In conjunction with cloud deployment technologies, API management, integration technologies, and microservices monitoring, microservices create an agile and efficient way to deploy large, complex enterprise applications. The big difference is your monolithic application is disassembled into a set of independent services, which are developed, deployed, and maintained separately.

Which software architecture suits your solution and business best?

Although many in the development space argue that microservices are better for developing applications, there are specific use cases for monolithic architecture:

• You’re just starting. Smaller teams that simply can’t tackle the broader requirements of a microservices architecture should stick to monolithic designs — or work with partners who can help the team transition to support microservices.
• You’re building a proof of concept or test software. If you need to test and deploy proofs of concept quickly, skip microservices and build a monolithic architecture to allow for rapid product iteration.
• You’re simply not ready for microservices. If your team has no microservices experience, start with a monolithic architecture. There is a lot of risk learning microservices as you build the application. So, either work with what you know or work with partners who can help you migrate to microservices.

Of course, there will be use cases to work with microservices from the start. Consider the following:

• Teams want service speed. Microservices allow for quick, independent service delivery.
• Teams want efficiency. With microservices, you’ll be able to deploy a highly efficient, easy-to-scale platform.
• An organization is growing. If you plan on growing your team and working with enterprise applications, go with microservices, as they enable you to scale up your team without introducing exponential complexity.

How to migrate from monoliths to microservices

As your company grows, your applications and services become more complex. Subsequently, to support a growing business, you need to ensure your apps and services can scale with it.

If you’re ready to migrate, new tools from Dynatrace can give you valuable information about whether you should break out certain pieces of the monolith. This approach allows you to do continuous experimentation, and it gives you fast feedback without changing a single line of code.

Monitoring microservices made easy

Leading organizations adopt microservices to make enterprise applications more agile and resilient. So, a significant part of ensuring a successful process is monitoring critical microservices.

Dynatrace’s PurePath makes it easier to monitor microservices. Its out-of-the-box distributed tracing and code-level insights provide end-to-end visibility across complex application environments.

By enlisting PurePath and the Dynatrace platform to automatically connect logs and traces, analyze them in real-time, and assemble the data for AI-powered analytics, organizations can more effectively and proactively optimize digital services to innovate faster and scale smoothly. PurePath also supports the latest cloud-native architectures. Therefore, teams receive intelligent observability for serverless applications, containers, microservices, service mesh, and integrating with the latest open-source standards such as OpenTelemetry. Check out the demo today.

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As cloud environments become increasingly complex, legacy solutions can’t keep up with modern demands. As a result, companies run into the cloud complexity wall – also known as the cloud observability wall – as they struggle to manage modern applications and gain multicloud observability with outdated tools.

But what exactly is this “wall,” and what are the big-picture implications for your organization? Let’s explore this concept as we look at the best practices and solutions you should keep in mind to overcome the wall and keep up with today’s fast-paced and intricate cloud landscape.

What is the cloud observability wall?

Cloud applications are different from traditional monolithic applications – they are both ephemeral and dynamic. At any given time, the state of your application is undergoing rapid, automated changes in response to the environment. You may be using serverless functions like AWS Lambda, Azure Functions, or Google Cloud Functions, or a container management service, such as Kubernetes. Either way, you are spinning new resources up or down in response to the load, and a continuous delivery pipeline may be promoting a set of functions or containers to production.

These rapid changes — as well as the increasing volume and variety of data created — require a new approach to observability. Many customers try to use traditional tools to monitor and observe modern software stacks, but they struggle to deal with the dynamic and changing nature of cloud environments. As a result, they hit what the industry refers to as the cloud complexity wall – or cloud observability wall – and waste time, money, and resources trying to force their legacy toolsets to work in modern environments.

How observability works in a traditional environment

In contrast to modern software architecture, which uses distributed microservices, organizations historically structured their applications in a pattern known as “monolithic.” A monolithic software application has a few properties that are important to understand. Let’s break it down.

Centralized applications

Monolithic applications earned their name because their structure is a single running application, which often shares the same physical infrastructure. In a monolithic architecture, there is generally limited ability to evolve, upgrade, or enhance specific subsets of functionality without restarting or upgrading the entire application.

There are a few important details worth unpacking around monolithic observability as it relates to these qualities:

1. The nature of a monolithic application using a single programming language can ensure all code uses the exact same logging standards, location, and internal diagnostics. Just as the code is monolithic, so is the logging.
2. When an application runs on a single large computing element, a single operating system can monitor every aspect of the system. Modern operating systems provide capabilities to observe and report various metrics about the applications running.
3. The last aspect is the centralization of compute. As the entire application shares the same computing environment, it collects all logs in the same location, and developers can gain insight from a single storage area.

This centralization means all aspects of the system can share underlying hardware, are generally written in the same programming language, and the operating system level monitoring and diagnostic tools can help developers understand the entire state of the system.

Dynamic applications with ephemeral services

Modern cloud-native architectures leverage a completely different development paradigm compared to monolithic applications. The core of a microservice design pattern aims to make each discrete subset of system functionality into its own self-contained unit, known as a microservice. Each microservice, running as a discrete, completely self-contained, stateless application, runs inside a container or serverless function that shares no underlying operating system with any other microservice.

The components of partitioned applications generally communicate over a network call. This boundary is language agnostic, which means the service is compatible with all other microservices written in any language, so long as the network interfaces remain the same. As it relates to observability, logging practices don’t require singular technical enforcement because services don’t share code across microservice boundaries.

Another aspect of microservices is how the service itself relates to the underlying hardware. Serverless functions typically run on hyperscale clouds and so there’s no hardware to manage. Containers and container managers, such as Kubernetes, allow the hardware to be abstracted away from the application. In both cases, microservices are in a constant, ephemeral state of transition, scaling up and down in response to the environment. Because the state of the system is always in flux, there is no centralized log location that one can readily use to gain observability.

But it’s important to note, it’s not the case that traditional observability tools are bad; they’re simply not the right tools for multicloud observability.

Observability challenges of multicloud environments

Multicloud microservices-based environments bring a new set of challenges, many of which are around the velocity and volume of data generated. In many cloud-native deployments, there can be hundreds or thousands of containers and serverless functions running at any given time. With this volume of data, not only do traditional tools break down due to technical differences, but the sheer volume of data generated is also orders of magnitude greater.

Furthermore, the large data quantities generated, on top of the increasing need to sift through millions of events to uncover actionable patterns and unexpected discrepancies to optimize application performance and availability, is much more than a single person – or even a single legacy observability system – can manage to gain any insight from. As a result, organizations are looking to AI as the modern observability capability to address these challenges, using advanced AI algorithms to understand and make sense of all the discrete events in a cloud-native microservices environment is absolutely critical for your application.

Overcoming the cloud complexity wall with Dynatrace

Dynatrace provides a unique Software Intelligence Platform built for multicloud observability. Incorporating AI, automation, and front-end monitoring, the Dynatrace Platform provides complete end-to-end visibility for modern applications enabling companies to optimize application performance and reliability while accelerating innovation.

As organizations steadily replace monolithic applications with emerging cloud-native solutions, modern cloud observability tools will provide the vital backbone. They have become the requisite tools needed to help companies overcome the cloud complexity wall and accelerate application performance and innovation.

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Visibility into system activity and behavior has become increasingly critical given organizations’ widespread use of Amazon Web Services (AWS) and other serverless platforms.

With AWS, applications may be distributed horizontally across worker nodes, and microservices may run in Kubernetes clusters that interact with AWS managed services or in serverless functions. These resources generate vast amounts of data in various locations, including containers, which can be virtual and ephemeral, thus more difficult to monitor. These challenges make AWS observability a key practice for building and monitoring cloud-native applications.

Let’s take a closer look at what observability in dynamic AWS environments means, why it’s so important, and some AWS monitoring best practices.

What is AWS observability? And why it matters

Like general observability, AWS observability is the capacity to measure the current state of your AWS environment based on the data it generates, including its logs, metrics, and traces.

Because of its matrix of cloud services across multiple environments, AWS and other multicloud environments can be more difficult to manage and monitor compared with traditional on-premises infrastructure. To cope with this complexity, IT pros need a clear understanding of what’s happening, the context it’s happening in, and what is affected. With dependable, contextual observability data, teams can develop data-driven service-level agreements (SLAs) and service-level objectives (SLOs) to make their AWS infrastructure more reliable and resilient.

AWS: A service for everything

AWS provides a suite of technologies and serverless tools for running modern applications in the cloud. Here are a few of the most popular.

• Amazon EC2. EC2 is Amazon’s Infrastructure-as-a-service (IaaS) compute platform designed to handle any workload at scale. With EC2, Amazon manages the basic compute, storage, networking infrastructure and virtualization layer, and leaves the rest for you to manage: OS, middleware, runtime environment, data, and applications. EC2 is ideally suited for large workloads with constant traffic.
• AWS Lambda. Lambda is Amazon’s event-driven, functions-as-a-service (FaaS) compute service that runs code when triggered for application and back-end services. AWS Lambda makes it easy to design, run, and maintain application systems without having to provision or manage infrastructure.
• Amazon Fargate. Fargate is an AWS serverless compute environment for containers. It manages the underlying infrastructure that hosts distributed container-based applications, which frees developers to focus on innovating and developing applications. Fargate is tailored to run containers with smaller workloads and occasional on-demand usage.
• Amazon EKS. EKS is Amazon’s managed containers-as-a-service (CaaS) for Kubernetes-based applications running in the AWS cloud or on-premises. EKS integrates with AWS Fargate using controllers that run on the managed Amazon EKS control plane, which governs container orchestration and scheduling.
• Amazon CloudWatch. Amazon’s AWS monitoring and observability service, CloudWatch, monitors applications, resource usage, and system-wide performance for AWS-hosted environments. But if you are using non-AWS tooling or need more breadth, depth, and analysis for your multicloud environment beyond AWS and CloudWatch, you need a different approach.

Serverless technologies can reduce management complexity. But like any other tool used in production, it’s critical to understand how these technologies interact with the broader technology stack. If a user encounters an error page on a website, for example, it’s vital to trace the behavior to the original source of failure.

While AWS provides the foundation for running serverless workloads and coordinated tools for monitoring AWS-related workloads, it lacks comprehensive instrumentation for observability across the multicloud stack. As a result, various application performance and security problems can go unnoticed absent sufficient monitoring.

AWS monitoring best practices

To gain insight into these problems, software engineers typically deploy application instrumentation frameworks that provide insight into applications and code. These frameworks can include break-points/debuggers and logging instrumentation, or processes, such as manually reading log files. The manual approach is usually effective only in smaller environments where applications are limited in scope. Here are some best practices for maintaining AWS observability in larger, multicloud environments.

1. Make full use of CloudWatch data. As part of your monitoring plan, use CloudWatch to collect monitoring data from all parts of your AWS environment so you can debug any failures. This approach should also include using multiple investigative tools — both AWS native and open source — to deliver a comprehensive view of activity in a multicloud environment. While this provides greater scalability than on-site instrumentation, it also introduces complexity. Multiple tools require teams to collect, curate, and coordinate data sources across disparate environments.
2. Automate monitoring tasks. Given the sheer number of AWS services and connections to outside technologies, teams now need observability and monitoring tools capable of doing more using automation. With data from sources such as Kubernetes and user experience data, teams need the ability to automatically detect the “unknown unknowns.” Such unknowns include glitches that haven’t yet been identified, can’t be discovered via dashboards, and don’t lend themselves to quick and easy remediation.
3. Create a special plan for EKS and Fargate monitoring. EKS leaves users with much of the responsibility for detecting and replacing failed nodes, applying security updates, and upgrading Kubernetes versions. If you have multiple EKS clusters, you should set up automation to handle manual tasks and quickly detect bottlenecks or failures. To gain optimum observability of EKS on Fargate, take an all-in-one approach that tightly integrates with AWS and your other Kubernetes environments. The goal is to gain automatic observability into Kubernetes clusters, nodes, and pods combined with analytics tools, such as application metrics, distributed tracing, and real user monitoring.

Automated and intelligent: The Dynatrace approach to AWS observability

Dynatrace provides a wide range of Powered by AI and automation at its core, Dynatrace turns your application data and log analytics into actionable insights and automatable SLOs.

As a long-standing AWS Advanced Technology Partner, Dynatrace integrates closely with AWS services with no code changes. Through auto-instrumentation, Dynatrace provides seamless end-to-end distributed tracing for AWS Lambda functions. Using OneAgent with Dynatrace Operator, Dynatrace combines observability for EKS clusters, nodes, and pods on AWS Fargate with distributed tracing, application metrics, and real user monitoring. Dynatrace ingests CloudWatch metrics and, as a launch partner for Amazon CloudWatch Metric Streams, can provide full observability of AWS services with a fast and direct push of metric data from the source to Dynatrace.

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For many companies, the journey to modern cloud applications starts with serverless. While these serverless services provide business benefits and a flexible on-demand usage and pricing, they also introduce complexities for observability.

Amazon Web Services (AWS) offers a wide range of serverless solutions. To get a better understanding of AWS serverless, we’ll explore the basics of serverless architectures and review AWS serverless offerings. We will also explore common use cases and discuss how you can ensure observability in serverless environments. Let’s get started.

Serverless architecture: A primer

Serverless architecture shifts application hosting functions away from local servers onto those managed by providers. This means you no longer have to provision, scale, and maintain servers to run your applications, databases, and storage systems.

While function-as-a-service (FaaS) serverless architecture is similar to platform-as-a-service (PaaS) solutions, there’s a significant difference. PaaS applications are typically deployed as single units, whereas serverless applications are delivered as functions, each hosted by your provider.

Why use a serverless architecture?

Serverless architecture offers several benefits for enterprises.

• Simplicity. The first benefit is simplicity. Instead of worrying about infrastructure management, capacity provisioning and hardware maintenance, teams can focus on application design, deployment, and delivery.
• Speed. Serverless solutions spin up or down quickly as needed, with no delays due to limited storage or resource access.
• Reliability. Serverless solutions are also more reliable than their traditional application counterparts. Since apps are hosted as interconnected functions in the cloud they’re naturally redundant and less prone to unexpected failure.
• Scalability. Finally, there’s scalability. Using a FaaS model makes it possible to scale up individual application functions as needed. Rather than increasing total resource allocation for your entire application, this flexibility reduces resource costs and improves overall app efficiency.

AWS serverless offerings

Amazon divides its serverless solutions into three broad categories with 12 specific services. But which fit your business best, and where do they make the most sense in your serverless application stack? Let’s explore each in more detail.

Compute services

Amazon compute solutions are designed to streamline resource provisioning and container management with two services:

• AWS Lambda: Lambda provides serverless compute infrastructure that lets you run code in response to predetermined events or conditions and automatically manage all compute resources required for these processes. Lambda functions can be written in the language of your choice, and the service also supports container tools.
• AWS Fargate: Fargate is a serverless compute engine designed for containers that work with Amazon’s Elastic Kubernetes Service (EKS) and the Amazon Elastic Container Service (ECS). It automatically allocates the compute resources you need, eliminating the need for server management. It also helps to control costs since you only pay for the resources needed to run your containers.

Application integration

Amazon’s application integration services form the bulk of its serverless offerings. Its six solutions are designed to streamline the interconnection of application functions.

• Amazon EventBridge: EventBridge to bridges the data gap between your applications and other services, such as Lambda or specific SaaS apps. Users control where their data goes in real-time. This flexibility makes it possible to create app architectures that respond to data sources on demand.
• AWS Step Functions: Step Functions focuses on orchestration. Using a low-code visual workflow approach, organizations can orchestrate key services, automate critical processes, and create new serverless applications.
• Amazon SQS: The Amazon Simple Queue Service enables users to decouple and scale microservices, serverless applications, and distributed systems, reducing both IT overhead and total complexity.
• Amazon SNS: The Simple Notification Service provides fully managed messaging across application-to-person (A2P) and application-to-application (A2A) frameworks, enabling users to send messages at scale via SMS, mobile push, and email.
• Amazon API Gateway: Amazon’s API gateway handles API calls, enabling teams to create RESTful or WebSocket APIs that deliver real-time, two-way communication.
• AWS AppSync: AppSync offers a fully managed approach to developing APIs with GraphQL — connecting to AWS DynamoLB or Lambda along with adding caches and client-side data.

Data Store

As data volumes rapidly increase, streamlined data storage is a top priority. AWS offers four serverless offerings for storage.

• Amazon S3: The Simple Storage Service stores and retrieves data from anywhere with scalability, data availability, security, performance, and a high degree of durability.
• Amazon DynamoDB: DynamoDB is a key-value and document database capable of handling more than 10 trillion requests per day and has the capacity to manage 20 million requests per second.
• Amazon RDS Proxy: The Relational Database Service (RDS) proxy reduces failover times by up to 66%, enabling companies to increase the scalability, resiliency and security of their applications.
• Amazon Aurora Serverless: Amazon Aurora is a serverless relational database that provides automatic startup, shut down, and capacity scaling based on application needs.

Common use cases for AWS serverless services

While leveraging Amazon’s suite of serverless services makes it possible to take on almost any IT task without increasing in-house complexity, it’s worth examining four common use cases to explore how these services work in concert.

Empowering web applications

Organizations often use serverless solutions from Amazon to underpin critical application functions. By leveraging Lambda and the API Gateway for business logic — combined with DynamoDB for streamlined data store — organizations can create purpose-built, event-driven application back-ends that empower front-end functions.

Improving data processing

By combining Lambda, S3, SQS, SNS, and DynamoDB, organizations can develop and deploy general-purpose, event-driven parallel processing architectures. These architectures deliver real-time file processing, analysis, and output.

Boosting batch processing

Tools such as Lambda and SNS enable better batch processing. Batching makes it possible to quickly download, upload, and process files — and then automatically send notifications to IT teams.

Enhancing event ingestion

By pairing Lambda and SQS with AWS machine learning services, such as Amazon Rekognition and Comprehend, organizations can create serverless document repositories that offer fast indexing and simplified search.

Seamless observability of AWS serverless services

While AWS serverless solutions offer a solid framework for resource distribution, application management, and storage provision, the sheer number of interconnected solutions leveraged by enterprises to deliver on use case scenarios comes with its own challenge: observability.

Although individual Amazon services are typically transparent, once integrated, IT teams can easily lose track of what’s happening. Services can span multiple cloud providers and connections. This distribution makes it more difficult to discern where and when specific transactions happen, which increases overall complexity.

The Dynatrace Software Intelligence Platform provides seamless observability of AWS serverless services across the full hybrid-cloud stack and multicloud platforms. Dynatrace has partnered with Amazon to be part of the future AWS distro for OpenTelemetry deployments to deliver enhanced visibility across serverless stacks.

Dynatrace has also developed new extensions for Lambda and intelligent operations for both EKS and Fargate, which deliver automatic observability in context with the other services, hybrid-cloud resources, and cloud resources. In practice, Dynatrace’s observability and AI-driven analysis of AWS serverless services in context with the full hybrid-cloud stack enable organizations to simplify cloud environments. Without losing sight of critical operations, Dynatrace enables teams to streamline and optimize service architecture, and scale to meet evolving demands.

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Modern applications—enterprise and consumer—increasingly depend on third-party services to create a fast, seamless, and highly available experience for the end-user. Due to this growing complexity, it has become absolutely critical — and somewhat difficult — for IT staff to make sure these services are running and communicating as intended. As a result, API monitoring has become a must for DevOps teams.

So what is API monitoring? To answer that, it helps to understand what an API is. An application programming interface (API) is a set of definitions and protocols for building and integrating application software that enables your product to communicate with other products and services. APIs each have a web address, known as an API endpoint, and serve as the channel by which a consuming application and API communicate.

What is API Monitoring?

API monitoring is the process of collecting and analyzing data about the performance of an API to identify problems that impact users. If an application is running slowly, you need to first understand the cause before you can correct it. Modern applications use many independent microservices instead of a few large ones, and one poor-performing service can adversely impact the overall performance of an application. In addition, isolating a single poor-performing service among hundreds can be challenging unless proper monitoring is in place. This makes API monitoring and measuring API performance a crucial practice for modern multicloud environments.

What is an API?

An application programming interface (API) serves as a bridge between different software components, allowing them to communicate and exchange data, features, and functionality. Think of it as a set of rules or protocols that enable seamless interaction between software applications. APIs empower developers to create robust, interconnected software systems by providing a standardized way to access functionality across different components.

The need for API monitoring

API monitoring captures and analyzes metrics that describe the vital aspects of an application’s performance, which can help developers gain a deeper understanding of the health and efficiency of the APIs they’re utilizing.

To understand the importance of API monitoring, consider a website that provides weather information. That site uses APIs provided by a weather forecasting service. If that API is slow, then Web pages that display weather information will load slowly and may even fail and cause user frustration.

In addition to performance monitoring, your team may want insights into how developers are using APIs, such as which functions are most frequently used. This can provide a better understanding of areas that need improvement or updating. For example, some developers may be using an old version of an API that will soon be deprecated. If this were the case, IT teams would need to plan to migrate to the newest available version of that API.

API monitoring is especially important when using third-party components, such as payment services, customer relationship management services like Salesforce, and internal services provided by other teams within an organization for specialized internal processes. The growing popularity of these highly modular third-party services drives the need for better monitoring solutions.

In addition, API monitoring can provide information on the extent to which business functions depend on APIs as well as the impact of using those APIs on business objectives and key performance indicators (KPIs).

Benefits of API monitoring

1. Early detection of issues

APIs are the backbone of modern applications, enabling seamless communication between services. However, they can be prone to latency, errors, or unexpected behavior. API monitoring allows you to detect these problems early, preventing them from affecting end users. You can proactively address issues before they escalate by monitoring key metrics like response time, error rates, and throughput.

2. Performance optimization

Monitoring APIs provides insights into their performance. You can track response times, identify bottlenecks, and optimize resource usage. For example:

Response time: Monitoring helps you ensure that APIs respond within acceptable time limits. Slow APIs can impact user experience and overall system performance.

Throughput: Monitoring throughput helps you understand how many requests your APIs can handle. It guides capacity planning and scaling decisions.

Error rates: High error rates indicate issues that need immediate attention. Monitoring helps you pinpoint the root cause and fix it promptly.

3. Security and compliance

APIs are vulnerable to security threats such as unauthorized access, injection attacks, or data leaks. Monitoring helps you:

Detect anomalies: Monitor for unusual patterns in API traffic, which could indicate security breaches.

Rate limiting: Enforce rate limits to prevent abuse or DoS attacks.

Compliance: Ensure APIs adhere to security standards (for example, OAuth, JWT) and regulatory requirements (for example, GDPR).

4. Dependency management

Modern applications rely on internal and external APIs. Monitoring helps you manage dependencies effectively. For example:

Dependency health: Monitor third-party APIs to ensure they’re available and performing well.

Version compatibility: Track API versions and deprecations to avoid breaking changes.

5. Business insights

API monitoring provides valuable business insights, such as:

Usage patterns: Understand which APIs are heavily used and which are underutilized.

Billing and cost optimization: Monitor API usage to optimize costs and prevent unexpected bills.

6. SLA adherence

Service-level agreements (SLAs) define acceptable performance levels for APIs. Monitoring helps you track adherence to SLAs and take corrective actions if needed.

7. Alerting and incident response

Set up alerts based on predefined thresholds (for example, high error rates and prolonged response times). When anomalies occur, receive notifications and take immediate action.

Challenges of API monitoring

Diverse ecosystems: Modern applications rely on a mix of internal and external APIs, which might be built using different technologies, protocols, and standards. Monitoring this diverse ecosystem requires tools that handle various formats and communication patterns.

Dynamic environments: APIs operate in dynamic environments where services scale up or down based on demand. Monitoring tools must adapt to these changes and provide real-time insights without causing performance overhead.

Latency and performance: Monitoring APIs introduces additional latency. Balancing the need for detailed monitoring with minimal impact on API performance is challenging. Tools must collect relevant metrics efficiently.

Rate limiting and throttling: APIs often impose rate limits to prevent abuse. Monitoring tools must account for these limits and avoid exceeding them during monitoring. Otherwise, they risk disrupting the very APIs they’re monitoring.

Security and authentication: Monitoring APIs requires proper authentication and authorization. Tools must handle API keys, tokens, and security protocols (for example, OAuth) to access protected endpoints.

Data volume and retention: APIs generate substantial data, including request/response payloads, logs, and metrics. Managing this data volume efficiently while retaining historical information for analysis is challenging.

Complex dependencies: APIs interact with other services, databases, and third-party APIs. Monitoring tools must trace these dependencies to identify bottlenecks or failures accurately.

Error handling and alerts: Monitoring tools should detect anomalies, errors, and performance degradation. Configuring meaningful alerts and integrating them with incident response workflows is crucial.

Versioning and compatibility: APIs evolve. Monitoring tools must handle version changes, deprecations, and backward compatibility. Tracking which versions are in use and when to migrate is essential.

Multi-protocol support: APIs use various protocols (REST, SOAP, GraphQL, etc.). Monitoring tools must understand and interpret these protocols to extract relevant information.

Geographical distribution: APIs may be distributed across multiple regions or data centers. Monitoring tools must account for latency variations and regional differences.

Business context: Monitoring isn’t just about technical metrics; it’s about understanding the business impact. Tools should correlate API performance with user experience, revenue, and business goals.

Ways to monitor APIs

When monitoring, it’s important to track metrics that describe the vital aspects of an application’s performance. When it comes to APIs, some important metrics include the number of calls to API functions, the time to respond to API function calls, and the amount of data returned.

There are two predominant methods of API monitoring: synthetic monitoring and real user monitoring (RUM).

Synthetic monitoring is an application performance monitoring practice that emulates the paths users might take when engaging with an application. Synthetic monitoring can automatically keep tabs on application uptime and tell you how your application responds to typical user behavior, and it uses scripts to generate simulated user behavior for various scenarios, geographic locations, device types, and other variables. Once this data has been collected and analyzed, a synthetic monitoring solution can give you crucial insights into how well your app is performing.

Similarly, RUM provides valuable insights into an application’s usability and performance, but it does this by observing the actual experiences of end-users. RUM is more than a simple data sample. It captures every aspect of a customer’s interaction or transaction, allowing developers to visualize their entire journey within the app and resolve issues quickly with real-time data. This monitoring method provides full-stack observability for the end-user experience, eliminating blindspots that impact business performance and allowing for wise decision-making across development.

While synthetic monitoring and RUM gather their insights in different ways, they both facilitate a smoother application development process and end-user experience. When used together for API monitoring, they can provide more complete visibility of your API performance.

API testing complements monitoring

While API monitoring helps you understand the performance of APIs in a production environment, you’ll also want to be aware of any problems associated with your APIs before the application is released. This is done through testing.

Using agile methodologies, developers are constantly updating code and integrating it into production services. This practice comes with the risk of introducing new bugs, so it’s important developers test code before it’s released to production – something developers routinely do for the code they develop (a practice that should also be done for third-party APIs). If there’s a problem during testing, developers can quickly identify the root cause by looking at the differences in code between the last stable release and the release that produced the issue.

Both testing and monitoring play vital roles in the development of quality applications. Testing helps prevent bugs from being released into production code while monitoring helps to identify failures or performance issues when code is running in production.

Choosing an API monitoring tool

When choosing an API monitoring tool, keep in mind that not all have the same breadth of functionality or depth of analytic capabilities. Look for key features, including:

• Comprehensive analysis of all data, not just samples of monitoring data
• Support for both RUM and synthetic monitoring
• Ability to identify third party APIs that are adversely affecting application performance

The best way to understand how your customers are using and experiencing APIs is to collect data about and track every user-API interaction — automatically. If you were interested in aggregate measures, such as the average response time to an API function call, then missing a small number of outliers in a large sample may not materially impact the quality of the results. However, if you want to trigger an alert based on an outlier, such as a sudden spike in latency in one region or for a single customer, then sampling may not provide the alerting system with the data it needs to perform its job.

RUM, with comprehensive, real-time data collection and analysis, not sampling, provides a view into what customers are experiencing. This is needed to identify and remediate failures and slowdowns as soon as they occur. Synthetic monitoring is helpful when developing a baseline of performance. For example, API calls in one geographic region may be consistently slower than calls in another region. This may be due to the implementation choices of the API provider and not something that will likely change in the near future. In that case, you can plan accordingly and limit the use of API services in that region or adjust your alerting thresholds to account for the longer latency in regions with poorer performance.

With its AI-to-everything approach, the Dynatrace Software Intelligence Platform provides API monitoring capabilities, including multi-request HTTP monitors and detection of impacting third-party API calls with RUM. This approach delivers a comprehensive view into the state of your API usage that’s essential to ensure the high availability and consistent performance your customers expect.

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The concept of APM has evolved in recent years, and it’s important to understand the differences between APM tools and APM platforms when evaluating your options. Here’s what APM is, how organizations use APM to manage application performance, the difference between APM tools and APM platforms, and the business benefits an advanced APM platform provides.

What is APM and what are the challenges?

What is APM? This acronym can stand for application performance monitoring or application performance management — two distinct but related concepts. Both terms refer to technologies and practices, and both approaches aim to detect and pinpoint application performance issues before real users are impacted.

Application performance monitoring

Application performance monitoring involves tracking key software application performance metrics using monitoring software and telemetry data. Organizations use this kind of APM to ensure reliable system performance, optimize service performance and response times, and improve the user experience. Application performance monitoring has a strong focus on specific metrics and measurements.

Application performance management

Application performance management is the wider discipline of developing and managing an application performance strategy. Organizations use this to ensure an expected level of service — for example, to meet service level agreements — as measured by performance metrics and user experience monitoring.

APM has become more challenging with the rise of cloud-native apps running on microservices in containerized environments across multiple cloud services. What was once a straightforward process in the days of monolithic applications is now considerably more complex. At the same time, it has never been more important for companies to deliver a smooth, seamless user experience. For these reasons and many more, organizations now use modern APM tools or APM Platforms to understand and resolve the myriad issues that can impact an application’s performance.

What’s the difference between APM tools and an APM platform?

Organizations often start their APM journey by implementing APM tools before moving on to APM platforms. Let’s look at the differences.

What are APM tools?

APM tools are typically designed to look at one specific aspect of application performance. These point solutions can help identify specialized issues. However, over time, organizations often find themselves using multiple APM tools that don’t necessarily integrate with one another or provide comprehensive insight into the application environment. As a result, they struggle to identify root causes or resolve application performance issues, and the business may suffer follow-on effects related to the user experience and revenue generation.

What are APM platforms?

APM platforms provide a single integrated platform using AI and automation to deliver a precise, context-aware analysis of the application environment. A modern APM platform that’s expressly designed with cloud-native environments in mind can deliver coverage across the full stack, encompassing the entire hybrid multicloud network. Utilizing an APM platform, organizations can continuously monitor the full stack for system degradation and performance anomalies and achieve accurate, prompt root cause determination.

What are the benefits of APM platforms?

Adopting APM platforms can bring many benefits to organizations, spanning across a technical and business scale:

• Technical benefits – Organizations can gain several technical benefits by adopting advanced APM platforms and practices, including achieving increased application stability, reducing the number of performance incidents, and quickly resolving any issues that do arise. Organizations can also optimize their infrastructure usage, achieving a better return on their technology spend. They can even deliver faster and better software releases, gaining a competitive advantage over other players in the market who aren’t yet using modern APM platforms.
• Business benefits – Intelligent APM platforms also offer several compelling business benefits. With less time spent in the weeds hunting for root causes of application and infrastructure performance issues, DevOps teams can enjoy increased productivity, and the organization can reduce its operational costs. With the time and effort saved, they can focus on innovations and user experience enhancements that increase conversion rates and boost revenue. All these advantages help organizations transform faster and compete more effectively in a dynamic digital landscape.
• Team benefits – APM platforms also offers softer business benefits that will ultimately help organizations innovate and strengthen their competitive position. Advanced APM platforms that provide a single source of truth to all teams within an organization can aid cross-functional collaboration and greatly accelerate the process of root cause identification. This can significantly reduce the need for war rooms or finger-pointing when application performance issues arise. Subsequently, it can strengthen working relationships, increase employee satisfaction, and improve employee retention. With happier employees and increased productivity, organizations can focus instead on delivering even more ambitious innovations that differentiate their companies in the market.

What is the impact of APM on the business?

Ultimately, APM is a digital transformation enabler, freeing up internal capacity to focus on user experience enhancements and innovations that boost the bottom line.

Businesses need these capabilities to compete and win in today’s dynamic and fast-changing market. As they’ve increased their investments in cloud and mobile technologies — pursuing bold digital transformation to meet increased expectations for an exceptional user experience — they’ve also confronted growing complexity in the application environment and its underlying infrastructure. APM empowers companies to continue transforming without needlessly getting bogged down by the process bottlenecks and collaboration challenges that complex environments create when left unaddressed.

Isn’t APM just for monitoring applications?

Although it may sound like APM is just for application performance monitoring, it actually provides value well beyond this. It’s true that many organizations begin using APM tools because they need to monitor individual parts of their applications more effectively, but they often discover an APM platform also allows them to conduct comprehensive monitoring across the full stack. Not only can an organization use APM for application performance monitoring, but it can also use an APM platform for infrastructure monitoring, AIOps, digital experience monitoring, and business observability.

Perhaps most importantly, organizations can use APM to understand the relationships and interdependencies between different aspects of their application environment and its underlying infrastructure, even as they become more complex.

Streamlining success: A single APM platform

Organizations increasingly understand an integrated approach to APM is critical to their future success. According to the 2021 Gartner Magic Quadrant for Application Performance Monitoring, by 2025 70% of new cloud-native application monitoring will use open-source instrumentation rather than vendor-specific agents for improved interoperability. However, these open-source tools can create new APM challenges as they can potentially exist in silos, preventing end-to-end observability.

The ideal solution is an APM platform that is open and can accept data from virtually any APM tool. If it delivers contextual insights powered by AI analytics and automation, organizations can gain even greater insight into application performance issues. With a full-stack observability platform, digital teams can accelerate root cause identification, resolve issues faster, collaborate more effectively, and deliver an exceptional user experience more consistently.

Learn why Gartner named Dynatrace a Leader in the 2025 Gartner® Magic Quadrant™ for Observability Platforms — again.

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