Grafana Tempo is an open-source, easy-to-operate distributed tracing backend built by Grafana Labs. Unlike traditional tracing systems that index trace data for search, Tempo stores traces in object storage and retrieves them by trace ID only. This design dramatically reduces operational complexity and storage costs while integrating seamlessly with Grafana, Loki, and Prometheus for a complete observability stack.
Why Tempo Matters
Traditional tracing backends like Jaeger with Elasticsearch require significant infrastructure to index and search trace data. Tempo eliminates this complexity by not indexing traces at all. Instead, it relies on exemplars from Prometheus, log-to-trace correlation from Loki, and trace-to-log links in Grafana to navigate to the right trace, making it dramatically cheaper to operate at scale.
Teams that understand and adopt grafana tempo gain a significant operational advantage, reducing manual effort and improving the reliability and scalability of their infrastructure. As cloud-native adoption accelerates, familiarity with grafana tempo has become a core competency for DevOps engineers, platform teams, and site reliability engineers working in production Kubernetes and cloud environments.
How Tempo Works
Tempo accepts trace data from any OpenTelemetry-compatible source, including Jaeger, Zipkin, and OpenTelemetry Collector. It stores the raw trace data in object storage like S3 or GCS without indexing the content. To find a trace, you need its trace ID, obtainable through Prometheus exemplars, Loki log correlation, or TraceQL queries. Grafana provides the unified interface for navigating between metrics, logs, and traces.
Understanding how grafana tempo fits into the broader cloud-native ecosystem is important for making informed architecture decisions. It works alongside other tools and practices in the DevOps and platform engineering space, and choosing the right combination depends on your team’s specific requirements, scale, and operational maturity.
Key Features
Object Storage Backend
Stores all trace data in S3 or GCS with no indexes, making it extremely cost-effective for high-volume tracing.
TraceQL
A query language designed for distributed traces that enables searching and filtering based on span attributes.
Multi-Format Ingestion
Accepts traces from Jaeger, Zipkin, OpenTelemetry, and other formats without requiring format conversion.
Grafana Integration
Seamlessly integrates with Grafana for navigating between metrics, logs, and traces in a single pane of glass.
Common Use Cases
Storing distributed traces from all microservices at a fraction of the cost of Elasticsearch-backed Jaeger.
Navigating from a Prometheus alert to relevant traces using exemplars linked to Tempo trace IDs.
Correlating Loki logs with Tempo traces to understand the full context of application errors.
Running TraceQL queries to find traces matching specific patterns like high-latency database calls.
How Obsium Helps
Obsium’s managed observability team helps organizations implement and optimize grafana tempo as part of production-grade infrastructure. Whether you are adopting grafana tempo for the first time or looking to improve an existing implementation, our engineers bring hands-on experience across cloud platforms and Kubernetes environments. Learn more about our managed observability services →
Frequently Asked Questions
What is Grafana Tempo?
Grafana Tempo is an open-source, easy-to-operate distributed tracing backend built by Grafana Labs. Unlike traditional tracing systems that index trace data for search, Tempo stores traces in object storage and retrieves them by trace ID only.
How does Grafana Tempo work?
Grafana Tempo works by combining the components described in the sections above. The main page walks through the architecture, the typical use cases, and the trade-offs to weigh before adopting it.
Why does Grafana Tempo matter?
Teams adopt Grafana Tempo to ship faster, run more reliably, and reduce the cognitive load on engineers. The benefits, limits, and adjacent tools are covered in the body above.
When should you use Grafana Tempo?
Use Grafana Tempo when the problems it solves match what your team is hitting today. The page above outlines the signals that mean you should adopt it now, and the cases where a simpler approach is fine.
