OpenTelemetry + Tempo + Grafana Tracing Stack

The OpenTelemetry + Tempo + Grafana stack provides vendor-neutral distributed tracing for microservices and distributed systems. OpenTelemetry (OTel) standardizes instrumentation and collection, Grafana Tempo stores high-volume traces cost-effectively in object storage, and Grafana visualizes and correlates them. The stack lets teams follow a single request as it travels across many services to pinpoint where latency accumulates and where errors originate.

Components

• OpenTelemetry: an open standard and set of SDKs for generating traces (plus metrics and logs), along with the OTel Collector that receives, processes, batches, samples, and exports telemetry in a vendor-neutral format.
• Instrumentation: automatic and manual instrumentation across languages emits spans and propagates trace context across service and network boundaries.
• Grafana Tempo: a trace backend that requires no index of trace content, storing traces cheaply in object storage and retrieving them by trace ID and the TraceQL query language.
• Grafana: the UI for viewing trace waterfalls, service graphs, and correlating traces with logs and metrics through shared IDs and exemplars.
• Sampling pipeline: head- or tail-based sampling in the collector controls trace volume and cost.

Strengths

OpenTelemetry is the industry-standard, vendor-neutral instrumentation layer, so you instrument once and can switch backends without re-instrumenting your services — a major hedge against lock-in. Tempo's object-storage design makes retaining large trace volumes inexpensive, and TraceQL together with exemplars and trace-to-logs links speeds root-cause analysis. Grafana ties traces into the wider observability picture alongside metrics and logs. Because the whole stack is open source and standards-based, it avoids proprietary agents and integrates cleanly with the LGTM suite and Kubernetes.

Trade-offs

Instrumenting many services and ensuring correct context propagation across async boundaries takes effort and ongoing discipline. Tempo, lacking a content index, is optimized for fetching known traces and correlating rather than arbitrary attribute search, though TraceQL has narrowed that gap. High trace volumes require thoughtful sampling strategies to balance cost against visibility, and poor sampling can hide the very traces you need. Operating the OTel Collector pipeline and Tempo at scale adds work, and traces alone must be paired with metrics and logs for complete observability.

When to Use It

Choose this stack when you run microservices and need vendor-neutral, cost-efficient distributed tracing to diagnose cross-service latency and cascading failures. It fits teams adopting OpenTelemetry as a standard and wanting to retain large trace volumes affordably without proprietary lock-in. Combine it with Prometheus or Mimir for metrics and Loki for logs — effectively the LGTM suite — for complete, correlated observability across all three signals. It is a strong choice for teams standardizing on OpenTelemetry as policy, since instrumenting once against an open standard protects that significant investment from future backend or vendor changes. Defining sampling and instrumentation standards centrally, rather than leaving them to each team, is what makes the resulting traces consistently useful across services.