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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Scalable, cost-efficient tracing backend with seamless Grafana integration
Tempo provides high‑scale distributed tracing using only object storage, integrates tightly with Grafana, Prometheus, Loki, and supports Jaeger, Zipkin, OpenTelemetry formats.
Grafana Tempo is a high‑volume tracing backend designed for teams that need to monitor complex, micro‑service architectures. It stores trace data exclusively in object storage such as S3, GCS, or Azure, keeping operational costs low while handling massive request rates.
Tempo offers a queryless Traces Drilldown UI that lets users explore latency issues and errors with point‑and‑click interactions. The built‑in TraceQL language enables powerful, trace‑first queries and can generate ad‑hoc metrics from trace attributes. Compatibility with Jaeger, Zipkin, OpenCensus, OpenTelemetry, and Kafka ingestion formats ensures easy migration from existing tracing solutions.
Deployments are flexible: Tempo can run via Docker Compose, Helm charts, or Jsonnet configurations, making Kubernetes installations straightforward. The system writes to Azure Blob, Google Cloud Storage, Amazon S3, or local disk, providing a minimal‑dependency, highly available tracing stack that integrates natively with Grafana, Prometheus, and Loki.
When teams consider Grafana Tempo, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Identify latency bottlenecks in a Kubernetes microservice mesh
Engineers use the Traces Drilldown UI to pinpoint slow spans, reducing request latency by 30%.
Correlate error rates across services with RED metrics
Operations view Rate, Errors, Duration metrics in Grafana, enabling rapid detection of failing components.
Generate custom trace‑based metrics via TraceQL
Developers create ad‑hoc metrics from trace attributes, feeding them into Prometheus for alerting.
Migrate existing Jaeger traces to a cost‑effective backend
Legacy Jaeger data is ingested into Tempo, stored on S3, cutting storage costs by up to 80%.
Tempo can write traces to Azure Blob Storage, Google Cloud Storage, Amazon S3, or local disk.
Yes, it accepts Jaeger, Zipkin, OpenCensus, OpenTelemetry, and Kafka‑based trace formats.
The Traces Drilldown UI provides a queryless, point‑and‑click interface for exploring traces.
Deployment options include Helm charts, Docker Compose, and Jsonnet, making Kubernetes installations straightforward.
Tempo is released under the AGPL‑3.0 license.
Project at a glance
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