Kubernetes Cluster and Workload Monitoring
Using AI and automation to accelerate Kubernetes observability has never been easier
A truly automated approach to monitoring Kubernetes
Kubernetes is the standard platform for running and managing containerized workloads in distributed environments. There are many container runtimes available now—Docker, CRI-O, containerd, as well as layers running atop of K8s, like Istio and Linkerd service mesh. Dynatrace auto-discovers them all and gives you full visibility without changing any code.
Deploy at speed and scale with the Dynatrace OneAgent Operator
Dynatrace is the only full-stack monitoring platform that provides fully automated, zero-config, distributed tracing, metrics, and code-level visibility into distributed applications without changing code, Docker images, or deployments.
Monitor your Kubernetes Clusters and Workloads in minutes
Purpose built for cloud, container, and microservices environments, while also supporting traditional applications and infrastructure, Dynatrace automated deployment will have you up running in minutes.
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Install the Dynatrace OneAgent Operator
You’re now monitoring your Kubernetes environment
Automated Distributed Tracing
Distributed tracing is about seeing how requests flow through 1,000s of different services. It is key to finding the cause-and-effect relationship of breakdowns or performance issues. While other solutions require manual instrumentation and have limited visibility into containers, Dynatrace provides
- Real time discovery of all containers and microservices
- Automated instrumentation of services running inside of containers with zero changes of the code, container images or deployments
- Code level visibility for fast problem resolution
Cluster Health and Utilization Monitoring
Kubernetes clusters are typically shared across teams. Cluster owners are responsible for providing enough resources and capacity to properly host and run workloads and support the teams who rely on them. Dynatrace provides the needed insights.
- Cluster health and utilization of nodes
- Health status of individual nodes
- Requested usage of resources compared to actual usage
- How much additional workload can be deployed per node
Understand Cause of Failing Microservices
Davis, the Dynatrace AI engine, is built from the ground up to automatically pinpoint anomalies in highly distributed containerized environments. Real time visibility into containers at start-up combined with semantically enriched log, tracing and real user data are the foundation for Davis to precisely determine the:
- Functional root cause of a performance or availability problem
- Foundational root cause, that is the deployment or configuration change responsible
- Impact to real users and business KPIs
See what makes Dynatrace unique
Zero-touch configuration, continuous discovery and mapping, effortless problem identification and root cause
Understand all the relationships and interdependencies, top to bottom, for your enterprise cloud ecosystem
Precise answers, with automatic discovery of dependencies, root cause analysis, and