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.

Cloud-native workloads on edge devices are gaining momentum among organizations as they extend the hybrid cloud closer to the data source and end users at the edge. Successful deployments of cloud-native workloads at the edge help to reduce costs, boost performance, and improve customer experience. As an example, many retailers already leverage containerized workloads in-store to enhance customer experiences using video analytics or streamline inventory management using RFID tracking for improved security.

The challenge of cloud-native observability at the enterprise edge

In aggregate, connected devices generate huge volumes of data. As organizations see an increasingly high number of compute locations, assessing health and finding root causes of emerging problems across heavily distributed workloads, at scale, has become a daunting task. At the same time, privacy and security have never been more critical.

Observability on edge devices presents unique challenges compared to traditional data-center or cloud-based environments. These challenges stem from the distributed and often resource-constrained nature of edge computing. But there’s more than just a need for minimizing resource (CPU, memory, storage) and network (bandwidth) consumption for observability at the edge. Edge devices often handle sensitive data, and ensuring the security and privacy of observability data is crucial. Moreover, edge environments can be highly dynamic, with devices frequently joining and leaving the network. Thus, understanding the end-to-end topology and all observability signals in context is required to detect anomalies and assess end-user impact. Finally, edge deployments may be distributed across many locations, making it challenging to have personnel physically check and maintain devices. Remote management and automated alerting are, therefore, crucial.

Dynatrace on Red Hat Device Edge for enterprise edge scenarios

Many enterprises seek to extend their cloud-native workloads to remote locations while keeping storage and analytics centralized in public or hybrid clouds. For such enterprise edge setups, it’s necessary to keep all data in context and maintain a unified approach for observability and security that can scale on top of the ever-increasing data volume.

The Dynatrace^® unified observability and security platform addresses the needs of enterprise-edge scenarios by managing the health and performance of containerized applications and multi-cloud infrastructures with metrics, traces, and logs in one place.

Deploy Dynatrace on Red Hat Device Edge with MicroShift

Dynatrace can be deployed in various ways depending on the desired observability value and resource constraints of edge devices:

• Application observability
  Brings the full power of Davis^® AI for anomaly detection and causal correlation, world-class distributed tracing, memory and CPU profiling, and powerful deep code-level insights using method hotspots to application workloads. Application observability also helps to improve end-user experiences when combined with Dynatrace Digital Experience monitoring. Applications are automatically instrumented during runtime by leveraging Dynatrace Operator or at build time by including Dynatrace OneAgent^® in your Docker file.
• Kubernetes observability
  Helps to understand and troubleshoot the health and performance of your MicroShift deployments and optimize resources by providing out-of-the-box alerting and anomaly detection, automated root cause analysis, as well as metrics, events, and topology in context. The Kubernetes-based app platform can be deployed inside or outside a data center using distributed OpenShift topologies alongside even smaller Red Hat Device Edge. ActiveGate acts as a secure proxy and compresses and routes observability signals in an optimized manner to Dynatrace servers.

Dynatrace customers can ingest data from sources like Prometheus and OpenTelemetry alongside data from Dynatrace OneAgent for automated Kubernetes monitoring. Data is integrated seamlessly with Kubernetes topology. Signals are uniform, regardless of origin. The following illustrations outline a typical Red Hat Device Edge and Dynatrace setup.

The following sections highlight a few use cases supported by Dynatrace on Red Hat Device Edge, including node resource utilization, workload metrics in the context of resource utilization with Davis AI, and service insights.

Red Hat Device Edge node resource utilization

Built-in Dynatrace node analysis for Kubernetes, including Red Hat Device Edge nodes, provides out-of-the-box insights into CPU and memory utilization by comparing actual usage against requests/limits as well as an overview of all node events and pods running on the selected node.

By drilling down further on the workload level, you can gain valuable insights into the performance and potential problems of any containerized workload.

Red Hat Device Edge workload view

This built-in Dynatrace view shows resource utilization, throughput, related pods, Kubernetes Services, microservices, logs, and events for a workload called “deliveries.” You can ask Davis, the Dynatrace AI engine, to correlate CPU usage against other signals. In this case, Davis finds that a Java Spring Micrometer metric called Failed deliveries is highly correlated with CPU spikes.

Service view

The service view provides out-of-the-box insights into the golden signals (response time, failure rate, and throughput) of the application, its topology, as well as distributed traces. In addition, it allows you to identify details of failed requests and drill down on the code level (for example, response time hotspots and memory profiles).

Seeing is believing

• You can find out more about Dynatrace and Red Hat here.
• Try it out with a free trial.
• Learn more about Red Hat Device Edge here.

The post Dynatrace and Red Hat expand enterprise observability to edge computing appeared first on Dynatrace news.