Jaeger + OpenTelemetry + Prometheus

Jaeger + OpenTelemetry + Prometheus

This open-source stack covers two observability pillars for microservices: distributed tracing and metrics. OpenTelemetry instruments applications in a vendor-neutral way; Jaeger stores and visualizes traces to follow requests across services; Prometheus collects and queries metrics. Grafana ties dashboards together. All are CNCF projects commonly run on Kubernetes.

It targets platform and SRE teams running microservices who want a fully open-source, self-hosted observability foundation without vendor lock-in.

Components

• OpenTelemetry SDKs and Collector instrument services once and export traces and metrics to multiple backends.
• Jaeger ingests traces, stores them in a backend, and provides a UI for trace search, service dependency graphs, and latency analysis.
• Prometheus scrapes metrics, evaluates alert and recording rules, and serves PromQL queries.
• Grafana visualizes both Prometheus metrics and Jaeger traces in unified dashboards.
• Kubernetes is the usual deployment platform, with the OTel Collector running as an agent and gateway.

Strengths

The stack is fully open source and self-hostable, avoiding per-host SaaS costs and keeping data in your environment. OpenTelemetry standardizes instrumentation, so you can swap backends later. Jaeger gives deep request-level visibility for diagnosing latency and failures across services. Prometheus is the industry standard for metrics with a rich alerting ecosystem. Grafana unifies visualization, and trace-to-metric correlation helps root-cause analysis. All components are CNCF-backed and widely supported.

Trade-offs

You operate and scale several stateful systems yourself, including trace storage backends for Jaeger and long-term metric storage for Prometheus. Logs are not included, so a separate log stack is usually added. Correlating traces, metrics, and logs requires deliberate configuration. Sampling strategy must be tuned for cost and signal. Running it well demands meaningful SRE expertise compared to a turnkey SaaS.

When to Use It

Choose this stack when you run microservices, want open-source self-hosted observability with no vendor lock-in, and need both distributed tracing and metrics. It suits teams with platform engineering capacity. Add a log stack such as Loki for full coverage. If you prefer a managed, all-in-one tool with logs included, a SaaS platform like Datadog or New Relic reduces operational burden.