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To monitor a Kubernetes cluster, collect metrics, logs, and traces from its control plane, nodes, and workloads, then connect those signals to systems that can store, query, and act on them. OpenTelemetry can instrument applications and provide a Collector to receive, process, enrich, and route telemetry; it is not itself a storage or dashboard backend. Your design depends on which signals you need, how much operational control you want, and where the data should be retained.
What Kubernetes observability covers
Kubernetes documentation describes observability as collecting and analyzing metrics, logs, and traces to understand the cluster’s internal state, performance, and health. Considering the signals together can help connect a resource change or cluster event with application behavior and the requests users experienced.
The scope can include control-plane components, add-ons, nodes, and applications. It is not enough to collect application container output and call the result complete: system component output and audit records may matter too, and each can require different collection permissions and access controls.
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How the three telemetry pipelines work
| Signal | Typical Kubernetes source | Collection and use |
|---|---|---|
| Metrics | Control-plane components expose Prometheus-format metrics at /metrics endpoints. The kubelet also exposes cAdvisor, resource, and probe metric endpoints; kube-state-metrics can add Kubernetes object-status context. |
A collector periodically scrapes endpoints and sends samples to time-series storage. Dashboards, alerts, and automated actions can query the stored data. |
| Logs | Applications write to stdout and stderr. The container runtime and kubelet make container logs available through kubectl logs; system components and audit records can be separate sources. |
A node-level agent commonly tails log files and forwards selected records to central storage. Define collection scope and permissions rather than assuming container logs include every relevant cluster record. |
| Traces | Instrumented services and components emit spans that describe request flow and timing. | Send spans using OTLP to a Collector or, when configured, directly to a backend. A Collector can process spans, including sampling or redaction, before forwarding them. |
Metrics: choose what to scrape and retain
Prometheus is a common time-series database for scraped Kubernetes metrics. The Kubernetes documentation also names Cortex, Mimir, and Thanos as metrics options. For broader or longer-term multi-cluster querying, it identifies Thanos and Cortex as complementary systems; these examples do not mean every deployment needs an additional layer.
Decide which endpoints and object-level metrics answer operational questions before collecting everything. Include relevant Kubernetes context consistently so queries can distinguish, for example, a workload in one namespace from a similarly named workload elsewhere.
Logs: define scope as well as destination
Container logs are only one part of cluster logging. Decide whether the pipeline also needs system component output or audit records, and restrict access according to the sensitivity of those records. Kubernetes documentation lists Fluent Bit, Fluentd, Loki, Elasticsearch, and OpenSearch as logging examples. These are options in a category, not a guarantee that each provides the same ingestion, retention, or analysis features.
Traces: account for version-specific system telemetry
Kubernetes documentation describes system-component span export over OTLP using gRPC exporters and identifies a Collector as a routing option. It also names Kubernetes 1.37 in connection with OTLP span export. Treat that as version-qualified guidance: verify support and configuration against the Kubernetes release you actually operate rather than assuming the same path applies to earlier releases.
What OpenTelemetry does—and what it does not
OpenTelemetry (OTel) is a vendor-neutral, open-source framework for instrumenting, generating, collecting, and exporting telemetry such as traces, metrics, and logs. Its SDKs and other instrumentation produce telemetry; its Collector can receive, process, and export it. Storage, querying, dashboards, and alerting belong to destinations and other tools in the observability stack, not to the Collector by itself.
When a Collector helps
A Collector gives teams a place to enrich, filter, sample, redact, or route telemetry before it reaches one or more destinations. That separation can reduce the coupling between application instrumentation and a particular backend, provided the destination supports the signals and formats being sent. It also creates another component to deploy, configure, monitor, and upgrade.
Direct export is also possible in some cases; Kubernetes documentation describes it for system traces when the endpoint is configured. Use direct export when a pipeline point is unnecessary and supported by the source and destination. Use a Collector when its processing or routing capabilities justify the extra operational responsibility.
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Kubernetes-aware Collector components
OpenTelemetry Collector component guidance identifies several components useful in Kubernetes: the Kubernetes Attributes Processor for metadata enrichment; Kubeletstats Receiver for node, pod, and container metrics; Filelog Receiver for Kubernetes and application logs; Kubernetes Cluster Receiver and Kubernetes Objects Receiver; Prometheus Receiver; and Host Metrics Receiver. For application signals already emitted as OTLP, the guide recommends the OTLP receiver.
Component availability and configuration depend on the Collector distribution and version. Check the status and configuration for the exact distribution you deploy instead of copying a component list as if it were a release-independent compatibility guarantee.
Best Value
- Kubernetes is an open platform that automates container orchestration, enabling seamless deployment, automatic scaling, self-healing, and efficient management of applications across servers or clouds with high availability and optimal resource use
- Kubernetes is perfect for development operations engineers, cloud architects, site reliability engineers, platform engineering teams and infrastructure specialists who build, operate and maintain modern containerized applications in production environments
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Choose an instrumentation method
| Method | Useful when | Trade-off to plan for |
|---|---|---|
| Language SDKs and code instrumentation | Teams can change application code and need explicit control over the telemetry they produce. | Language support varies, and instrumentation requires application-level work and maintenance. |
| OpenTelemetry Operator-managed injection | Teams want Kubernetes-managed Collectors and supported auto-instrumentation without making the same code changes in every workload. | It adds cluster-level configuration and operational responsibilities; supported languages and injection behavior must fit the workloads. |
| eBPF-based auto-instrumentation | A no-code-change approach fits the Linux environment and the workload’s protocol and framework coverage. | Grafana’s Beyla documentation specifies Linux kernel 5.8 or higher with BTF enabled. Validate coverage for the services in scope; this approach is not automatically a substitute for purposeful application instrumentation. |
These approaches can be combined. For example, a team may use code instrumentation where request-level context matters and a less invasive method for selected services. Choose based on the telemetry needed, supported language or platform, and who will operate the configuration—not on the assumption that one method captures everything equally well.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Select destinations by operational fit
Kubernetes documentation names Prometheus, Cortex, Mimir, and Thanos for metrics; Fluent Bit, Fluentd, Loki, Elasticsearch, and OpenSearch for logs; and Tempo, Jaeger, the OpenTelemetry Collector, and Zipkin for traces. They are category examples, not a claim that each is a complete, integrated observability stack. Grafana’s instrumentation guide also names Grafana Cloud as a hosted destination and Loki, Mimir, and Tempo as self-managed options.
Compare candidates against the needs of your own cluster. The available documentation does not establish a universal best backend or a comparative price/performance ranking.
- Signal and protocol coverage: Confirm that the destination ingests the required metrics, logs, and traces, and accepts the formats your instrumentation emits.
- Kubernetes context: Check that cluster, namespace, workload, pod, and node metadata can be attached and queried consistently.
- Retention and scale: Set the history you need and consider how storage and multi-cluster queries will grow.
- Operations: Decide who owns upgrades, storage, access control, alerting, and incident response. Hosted and self-managed choices place that work differently.
- Data governance: Establish what is collected, sampled, redacted, retained, and, for a hosted service, exposed to the provider.
- Cost: Estimate ingestion, retention, query, and operational costs using your own workload and the candidate’s applicable terms.
A practical rollout sequence
- Choose an initial scope. Select the critical services and cluster components whose health or request behavior you need to understand, then identify the signal or signals that answer those questions.
- Map sources and access. Identify the metrics endpoints, log sources, and trace instrumentation involved. Grant only the permissions required to collect them, especially when including system or audit records.
- Select instrumentation and collection paths. Decide where SDKs, Operator-managed injection, or an eBPF approach fit. Choose direct export or a Collector based on the need for processing, enrichment, or routing.
- Attach and validate context. Enrich records with the Kubernetes metadata needed for investigation. Send a representative request through the services in scope and verify that its traces and related telemetry retain usable service and workload context end to end.
- Configure data handling and operations. Set retention, access, sampling, and redaction policies; then build the dashboards and alerts operators need. Include ownership for upgrades and compatibility checks.
- Test failure and change scenarios. Check what happens when an endpoint or destination is unavailable, whether collection resumes as expected, and how Collector or backend upgrades affect the configured pipeline.
Keep version and support claims in context
OpenTelemetry documentation, last modified August 29, 2025, reported support from more than 90 observability vendors. That is a dated figure from the documentation, not an independently audited count for 2026. Likewise, component guidance and Kubernetes system telemetry can change; validate instructions against the exact Kubernetes release, Collector distribution, and backend version you operate.
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