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Grafana Tempo

By Grafana Labs

AdvancedTool7.5K learners

Grafana Tempo is an open source, high-scale distributed tracing backend by Grafana Labs that stores traces cheaply using object storage and minimal indexing, designed to work closely with Grafana, Loki, and Prometheus.

Definition

Grafana Tempo is an open source, high-scale distributed tracing backend by Grafana Labs that stores traces cheaply using object storage and minimal indexing, designed to work closely with Grafana, Loki, and Prometheus.

Overview

Tempo addresses the same cost problem in tracing that Grafana Loki addresses in logging: storing and indexing every field of every trace at scale gets expensive fast. Instead of building a heavily indexed trace store, Tempo indexes traces only by trace ID and stores the trace data itself in cheap object storage (like S3-compatible storage), relying on that minimal index for fast retrieval when you already know which trace you're looking for. Because Tempo doesn't build rich secondary indexes, it's typically paired with metrics or logs to discover which trace IDs are worth investigating — for example, exemplars in Prometheus metrics or log lines in Loki that reference a trace ID can link directly into Tempo to pull up the full distributed tracing waterfall for that request. This tight integration is central to Grafana Labs' philosophy of correlating metrics, logs, and traces through shared identifiers rather than building one monolithic tool. Tempo supports ingestion from multiple tracing protocols and formats, including OpenTelemetry, Jaeger, and Zipkin, making it a flexible backend for teams standardizing on open instrumentation rather than being locked into a single tracing vendor's format. It's typically deployed alongside Grafana, Loki, and a Prometheus-compatible metrics store as part of an open source observability stack.

Key Features

  • Minimal indexing (trace ID only) backed by cheap object storage
  • Support for multiple tracing protocols including OpenTelemetry, Jaeger, and Zipkin
  • Deep integration with Grafana for visualizing trace waterfalls
  • Exemplar linking from Prometheus metrics directly into specific traces
  • Log-to-trace correlation when paired with Grafana Loki
  • Horizontally scalable, designed for very high trace volume ingestion

Use Cases

Storing and querying distributed traces cost-effectively at scale
Correlating a metrics spike or log error with the exact trace that caused it
Debugging latency and failures across microservices via trace waterfalls
Standardizing tracing ingestion around OpenTelemetry across an organization
Running an open source tracing backend alongside Grafana and Loki

Frequently Asked Questions

Frequently Asked Questions

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What is the SkillVeris tech glossary and how big is it?
The SkillVeris glossary is a free reference of roughly 2,000-plus technology terms, each with a clear plain-language definition. It spans AI, programming, web, DevOps, cloud, security and database vocabulary, so whenever a lesson, article or job description uses jargon you do not recognise, the glossary gives you a fast, reliable answer.
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How is the SkillVeris glossary different from Wikipedia?
The glossary is purpose-built for learners: definitions are short, plain-language and answer-first, sized for a quick lookup mid-lesson rather than a deep encyclopedic read. Entries also cross-link to related SkillVeris study notes, blog posts and courses, so a definition becomes a doorway into structured learning instead of a dead end.
Do blog articles use the Learn Through Hobbies method?
Many blog articles teach technical topics through hobby analogies, a hallmark of the SkillVeris blog, so you will find articles explaining programming through cricket, machine learning through music, or system design through cooking. The analogy is the teaching device; the article still delivers the real technical concept underneath.
Where can I find quick programming references while coding?
Open the SkillVeris cheat sheets, which are built exactly for that moment: compact, scannable references for syntax, commands and common patterns across languages and tools. Keep the relevant sheet in a browser tab while you work in Code Lab or your own editor, and dip into the glossary for terminology.
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The blog serves Indian learners plus a worldwide audience. Content stays globally relevant while acknowledging realities that matter in India, such as free access being essential for students and freshers, and career guidance that connects naturally to the SkillVeris jobs portal, which aggregates roles across India, UK, USA, Germany and Remote.
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SkillVeris content grows in response to what learners need, so feedback is welcome through the platform's support channels. If a term is missing from the glossary or a topic deserves an article, telling the team helps prioritise it. Meanwhile, the AI Mentor can answer the question immediately, 24/7, at any depth.
Do cheat sheets and glossary entries link to deeper learning?
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What makes SkillVeris programming references trustworthy?
The references are written to strict internal quality standards, kept consistent with the platform's 37 live courses, and never padded with invented statistics or hype. Definitions and cheat sheets are reviewed against the same content contracts that govern courses, and the answer-first style makes any inaccuracy easy to spot and correct.
How do the blog, glossary and cheat sheets fit into my learning routine?
Use them as satellites around your main course: read blog articles for context and motivation, hit the glossary the instant jargon appears, and keep cheat sheets open while coding. Together with study notes, Code Lab and the 24/7 AI Mentor, they turn passive reading into a complete, free learning system.

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