Directory Observability & MonitoringAPM & Distributed TracingGrafana Tempo

Grafana Tempo

About Grafana Tempo

Grafana Tempo is an open source distributed tracing backend. It requires only object storage to operate. It is integrated with Grafana, Prometheus, and Loki. It can ingest common open source tracing protocols, including Jaeger, Zipkin, and OpenTelemetry. Tempo is released under the AGPLv3 license.

Used for

Sampling 100% of the read path
Storing trace data in object storage for long-term retention
Ingesting traces from Jaeger, Zipkin, and OpenTelemetry protocols

Who it is for

Individuals using the free tier of Grafana Cloud
Teams using Grafana Cloud Traces
Large enterprises using Grafana Cloud Traces
Organizations with data localization and privacy requirements

From Grafana Tempo's own website, read 2026-10-23. How we build listings.

How to evaluate APM & Distributed Tracing

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Grafana Tempo. It comes from our Observability & APM Platforms buyer guide.

22%

Telemetry Coverage & Correlation

Unified metrics, traces, logs, real-user monitoring, synthetics, and continuous profiling; automatic correlation across signals; service maps and topology; Kubernetes, container, serverless, and cloud-provider auto-discovery; breadth and quality of the integration catalog

22%

Cost Control, Cardinality & Data Governance

Pricing-unit transparency and overage terms; high-cardinality handling without runaway cost; ingest-time filtering, sampling, aggregation, and tiered retention; a native or compatible telemetry pipeline; per-team budgets, quotas, and chargeback visibility so spend is governable, not just observable

20%

Distributed Tracing & APM Depth

End-to-end trace correlation across services and async boundaries, tail-based and adaptive sampling, code-level and method-level profiling, error tracking and exception grouping, database and dependency visibility, and frictionless OpenTelemetry trace ingestion alongside any native agent

16%

AIOps, Anomaly Detection & Root Cause

Quality of automated anomaly detection and alert noise reduction, causal versus correlative root-cause analysis, forecasting and capacity signals, and emerging agentic remediation — judged on accuracy during a real incident, not on the marketing name of the AI engine

12%

OpenTelemetry & Openness

Depth of native OTel support (traces, metrics, logs) versus a proprietary agent; whether collection is genuinely vendor-neutral or quietly locks you in; query-language portability; export and data-egress freedom; and how much re-instrumentation a future migration would actually require

Scale, Reliability & Operability

Proven performance at your peak event and series volumes, multi-region availability and platform SLA, data-retention options, RBAC and SSO, and — if self-hosted — the operational burden of running and upgrading the backend at high cardinality

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Airbrake

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