Enterprise cloud has outpaced traditional reactive monitoring. As workloads multiply across AWS, Azure, and Google Cloud—and extend into data centers—teams find themselves contending with vast amounts of telemetry, ephemeral infrastructures, and constant change. Visibility is tough; clarity of impact, ownership, and root cause is tougher.

Today’s tech stacks are distributed by design, featuring containers, serverless architectures, managed services, and data planes that can spin up and down in seconds. High-cardinality signals, fragmented logs, and partial traces create blind spots across accounts, subscriptions, and projects. In hybrid setups, network overlays, identity boundaries, and platform services blur the lines between where issues begin and how they propagate.

Organizationally, the challenge is even bigger. Platform, SRE, Dev, SecOps, and FinOps each hold a piece of the truth. However, inconsistent tagging and governance, manual runbooks, and handoffs across time zones can slow MTTR, fuel tool sprawl, and inflate budgets. The goal is to provide clear risk and cost insights, reduce noise, and deliver faster, context-rich answers.

However, visibility alone isn’t enough to achieve this goal. AI-driven context, such as topology-aware analytics, causal correlation, and safe automation, shifts operations from reactive firefighting to proactive prevention that’s aligned with SLOs, compliance, and budgets.

Today, we’re introducing Dynatrace enhanced cloud operations for AWS, Azure, and Google Cloud, delivering complete visibility across all your cloud and hybrid environments with deeper, actionable insights. If you’re ready to take the guesswork out of monitoring your cloud environments, read on to see how Dynatrace helps you collaborate faster, resolve issues earlier, and run at enterprise scale with confidence.

What is Dynatrace Cloud Platform Operations?

Dynatrace Cloud Platform Operations takes the guesswork out of monitoring by redefining how organizations manage complex cloud environments. By unifying all cloud signals—metrics, logs, and events—into a single AI-powered platform, Dynatrace delivers complete visibility and actionable insights at scale.

Every data point is enriched with context and analyzed by Dynatrace AI, enabling proactive automation and informed decision-making. This ensures faster troubleshooting, better resource efficiency, and simplified onboarding—all without the need for additional infrastructure components.

Organizations eliminate blind spots and accelerate resolution by gaining real-time visibility into the health, performance, and configuration of their cloud resources. This allows them to maintain the correct governance through tag-based control, ownership, and cost allocation, thereby aligning teams while reducing operational noise.

Additionally, the latest advancements in cloud observability provide real-time visibility into resource health, performance, and configurations, allowing teams to act quickly and decisively while reducing the time to resolution.

Start monitoring your cloud environment

Spin up monitoring without spinning up your infrastructure. The new cloud connections are fully managed by Dynatrace and guided by a simple wizard. It gets you from setup to actionable telemetry in minutes; no agents to wrangle, no custom pipelines to maintain.

Once connected, you gain instant visibility across all your cloud environments. Connect your AWS, Azure, or GCP accounts once, and Dynatrace auto‑ingests everything from compute and databases to storage, networking, and security configurations. Platform coverage has been expanded to capture more data types than ever, including metrics for any supported cloud service and a richer set of cloud events, such as hyperscaler‑native security alerts.

The new cloud connections are:

• Secure by design: native authentication offers least‑privilege access and auditable permissions.
• Practitioner‑friendly: a step‑by‑step wizard with built‑in checks and defaults works across accounts and regions.
• Zero overhead: the Dynatrace platform manages the connection end‑to‑end, so you focus on insights, not maintenance.

With all your cloud data unified, Dynatrace automatically applies the tags you already use in your cloud environments, bringing your existing operational model directly into the platform. Your existing cloud tags instantly drive access control, ownership, cost allocation, alert routing, and preventive workflows, with no manual tagging or re‑mapping required. Additionally, tag‑enriched signals keep operations aligned by allowing you to filter everything by owner, app, or environment for precise alert routing and actionable insights. This paves the way for more advanced analytics and clear insight into what’s happening across every environment.

The enhanced cloud ingest not only unifies telemetry and context; it also captures all configuration details (VPCs, load balancers, security groups, subnets, network services, compute metadata, and more). The new Smartscape® uses this information, along with cloud monitoring data, to provide a comprehensive, always-accurate topology of your cloud infrastructure. You can use the new Smartscape app to navigate your cloud topology, visualize dependencies, and eliminate hidden or forgotten resources. Ready-made views for AWS and Azure provide a comprehensive, automatically discovered cloud inventory across services, databases, networking layers, and security controls.

Additionally, you get ready-made dashboards, essential metrics, and expanded cross-cloud visibility, providing you with immediate clarity, especially when issues originate on the provider side, thanks to built-in AWS Health event integration.

All of this comes together in our newly enhanced Clouds app, a shared place for teams to analyze services, metrics, events, and logs. Pre-built dashboards and alerts automatically highlight unhealthy resources, helping teams stay ahead of issues, while the streamlined onboarding process eliminates the need for additional collectors or components. It’s fast, safe, and modern—exactly how cloud onboarding should feel. Ready to see it in action? Keep reading to see the magic.

Transform from reactive monitoring to proactive cloud operations

Dynatrace transformed cloud monitoring into proactive cloud operations by combining AI-powered observability with intelligent automation. This allows organizations to move beyond simply identifying problems to actively solving and preventing problems.

With Dynatrace, you can remediate issues before they impact your users, prevent future issues, and optimize your cloud environments to ensure they operate at maximum efficiency and resiliency.

Prevention

Leave reactive firefighting behind and gain the foresight needed to stay ahead of issues. Dynatrace’s AI-driven automation delivers proactive insights that allow you to identify and resolve potential problems before they impact your users. By predicting anomalies and triggering automated workflows, Dynatrace helps maintain high availability and optimal performance across your cloud environments. This proactive approach minimizes downtime, protects user experience, and ensures your teams can focus on strategic initiatives rather than crisis management.

Remediation

Instead of wasting valuable time on lengthy resolution cycles that disrupt business operations, accelerate recovery with intelligent automation. Dynatrace automates root cause analysis and remediation, enabling self-healing workflows that dramatically reduce resolution times. By eliminating manual troubleshooting and streamlining incident response, your teams can focus on driving innovation rather than firefighting. With AI-driven insights and automated corrective actions, Dynatrace ensures issues are resolved quickly and efficiently, thus minimizing impact, improving reliability, and keeping your business moving forward.

Optimization

Stop overspending on infrastructure due to a lack of visibility into resource usage and performance. With Dynatrace, you can continuously optimize both cost and efficiency across your environment. Real-time insights into resource consumption and application performance allow you to identify waste and prevent unnecessary expenses. By leveraging AI-driven analytics and automated recommendations, you can improve cost management and drive peak performance in your applications.

Ready to experience the new world of Cloud Operations for yourself?

With the introduction of enhanced cloud operations for AWS, Azure, and GCP, you can achieve smarter collaboration, faster issue resolution, and streamlined operations at scale using the Dynatrace Cloud Platform Operations solution.

AWS enhanced cloud operations

Discover how easy it is to get started using proactive monitoring capabilities for AWS. AWS cloud operations are now generally available for all Dynatrace SaaS customers.

Azure enhanced cloud operations

Interested in seeing the magic for your Azure environments?
Join the Azure Preview program

GCP enhanced cloud operations

Are you a GCP customer looking for a cloud operations solution?
Join the GCP Preview program

The post Redefining cloud operations: Dynatrace brings intelligence to observability appeared first on Dynatrace news.

Infrastructure monitoring is the process of collecting critical data about your IT environment, including information about availability, performance, and resource efficiency. The goal? Reducing downtime, improving user experience, speed, reliability, and flexibility, and ensuring IT investments are delivering on promised ROI across local IT stacks and in the cloud.

The challenge? Getting adequate insight into an increasingly complex and dynamic landscape. Between multicloud environments, container-based architecture, and on-premises infrastructure running everything from the latest open-source technologies to legacy software, achieving situational awareness of your IT environment is getting harder to achieve. Many organizations respond by adding a proliferation of infrastructure monitoring tools, which in many cases, just adds to the noise.

To keep pace with innovation and deliver great user experiences at ever-increasing rates of reliability, speed, and scale, IT operations (ITOps) teams need to mature their approach to infrastructure monitoring.

Why ITOps needs to work smarter, not harder

Cloud services, mobile applications, and microservices-based application environments offer unparalleled flexibility for developers and users. The Cloud Native Computing Foundation (CNCF) paints a fast-growing landscape of nearly 1000 cloud-native technologies, and most organizations use many of them. However, this variety and flexibility also create numerous complexity concerns for ITOps teams. The result is a production paradox: with each new cloud service, container environment, and open-source solution, the number of technologies and dependencies increases, which makes it more difficult for ITOps teams to actively monitor systems at scale and address performance problems as they emerge.

To get ahead of this ever-expanding diversity and complexity, ITOps teams need to work smarter, not harder. Leveraging artificial intelligence and continuous automation is the most promising path—to evolve from ITOps to AIOps. Artificial intelligence for IT operations (AIOps) is the discipline of applying artificial intelligence, typically machine learning and pattern recognition—or in the case of Dynatrace, deterministic, causation-based AI—to perform and automate tasks an ITOps team normally performs manually.

Achieving AIOps may seem daunting, but with some planning, teams can implement this evolution in three phases: 1. Evaluate monitoring maturity and goals; 2. Automate infrastructure monitoring; 3. Integrate monitoring on a single AIOps platform.

1. Evaluate infrastructure monitoring maturity and goals

Effective monitoring and diagnostics starts with availability monitoring. But as Gartner notes, most organizations are on a journey to increase both the maturity of their monitoring and its impact. Gartner defines five maturity stages organizations can use to evaluate where they are and where they want to go:

Stage 1: Availability monitoring

This stage is defined by the question “is it up?” and focuses on the ability to collect and correlate events to assess the availability of key services. While this basic information is essential, it provides no insight into the root causes of anomalies or the specific remedies required to resolve, let alone prevent, disruptions.

Stage 2: Service monitoring

The service monitoring stage digs deeper to ask, “is it working?” Here, collecting metrics and monitoring performance help evaluate the efficacy of services rather than simply identifying their state.

Stage 3: Diagnostics

Stage three tackles a bigger question: “What’s the problem?” Dependency mapping, distributed tracing and root-cause analysis (RCA) operations all play a role in identifying what’s gone wrong, why, and what’s required to fix it.

Stage 4: Business insights

With basic operational needs well in hand, stage four focuses on customer journeys and KPIs to answer key questions: How is the performance of the monitored apps and services affecting end users? And what impact is this having on business?

Stage 5: Self-driving solutions

The final stage defines continual optimization and improvement through intelligent automation. This stage of maturity integrates the goals of the previous stages through automation and AI-assisted observability—automatic instrumentation and baselining of dynamic systems, reliable root cause analysis, and auto-remediation workflows—to enhance operational outcomes and prevent issues that can cause disruption.

Worth noting? Recent survey data from Gartner found that half the companies surveyed identified their monitoring operations as stage 2 or below, indicating that for most, monitoring maturity is a work in progress.

2. Automate infrastructure monitoring

Armed with an understanding of their monitoring maturity, organizations can develop a strategy for harnessing their data to automate more of their operations. Such a strategy relies on the ability to implement three capabilities:

End-to-end observability across a broad spectrum of technologies

The first requirement toward automating monitoring is comprehensive observability across the network. This includes all infrastructure layers (networks, hosts, virtualizations, and so on); cloud-native and on-premises environments (including mainframes); and open-source observability frameworks, such as StatsD, Telegraf, Prometheus, and OpenTelemetry.

Automation at every stage of the software delivery life cycle (SDLC)

Automating the entire data life cycle requires the ability to instrument tens of thousands of hosts, including highly dynamic and ephemeral function as a service (FaaS) components. It also requires performance baselining for anomaly detection and precise root-cause analysis, which relies not only on metrics, logs, and traces, but the context of each transaction, including user data.

Automated problem resolution

Automating proactive problem resolution requires intelligence, system optimization, and user experience data to facilitate an understanding of the context and business impact of each transaction. To deliver precise root-cause analysis, AI should be a core component of the monitoring solution. To trigger remediation workflows automatically, system health and anomaly data must be precise and reliable.

3. Integrate infrastructure monitoring on a single AIOps platform

As organizations evolve to embrace Kubernetes and cloud-native architectures, many have also adopted a host of monitoring point solutions—specialized monitoring tools that capture specific metrics of their application environment. With telemetry from many different sources, it can be difficult for teams to have a holistic view of their apps and dependencies. A single integrated observability platform can enable collaboration among operations, development, security, and business teams so they can easily coordinate and make decisions and automate more processes based on the same data.

This platform approach does not require that teams eliminate their point solutions. Rather for teams to adopt a single platform that can ingest data from any point solution in any environment, from legacy software in on-premises infrastructure to microservices-based apps in cloud environments. As different teams have implemented point-solution infrastructure monitoring tools across different disciplines, having a single platform to integrate their data and its context enables them to gain new insights so they can spend more time innovating.

Out-of-the-box AIOps

As organizations adopt more technologies in their evolving multicloud networks, infrastructure monitoring has become much more complex. To keep up with the demands on IT teams, deliver on customer expectations, and achieve business goals, digital teams must also transform how they work. Key to this transformation is adopting end-to-end observability across a broad spectrum of technologies, automating every stage of the SDLC, and automating problem resolution. Adopting an integrated observability platform like Dynatrace enables collaboration among operations, development, security, and business teams.

The Dynatrace Software Intelligence platform is a self-driving AIOps solution that delivers automation and AI-assisted observability for infrastructure monitoring, applications and microservices, application security, digital experience, and business analytics use cases in hybrid and multicloud environments.

The Dynatrace deterministic AI engine, Davis, automatically serves up precise answers, prioritized by business impact. To help organizations achieve AIOps out-of-the-box, Dynatrace combines automatic instrumentation and baselining of dynamic systems with automatic root cause analysis and auto-remediation workflows. Instead of infrastructure monitoring tool sprawl, Dynatrace’s platform approach integrates data from disparate monitoring point solutions, enabling teams to easily coordinate and automate responses.

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