Jaeger vs Grafana Tempo (2026): Storage, Cost, and Which Tracing Backend to Pick

If you are standing up distributed tracing in 2026 and want an open-source backend, the shortlist usually narrows to two names: Jaeger vs Grafana Tempo. They solve the same problem - store and query the spans your services emit - but they take opposite design bets on storage, and that one decision shapes your cost, your operational load, and where your engineers go to look at a trace.

This guide keeps it tight: just Jaeger vs Grafana Tempo, the two open-source tracing backends most teams actually choose between. If you are instead weighing full commercial observability suites, see our Datadog vs New Relic comparison or the wider four-tool enterprise field.

The short answer

If you only read one section, read this. It is self-contained.

Pick Jaeger if:

• You want a mature, self-contained tracing system with its own polished UI that works without committing to a wider vendor stack.
• You already run Cassandra or Elasticsearch and are comfortable operating an indexed store for traces.
• You want rich search out of the box - by service, operation, tags, and duration - without wiring up Grafana first.

Pick Grafana Tempo if:

• You want the cheapest tracing at scale and are happy with traces living only in object storage like S3, GCS, or Azure Blob.
• You already run Grafana, Loki, and Prometheus and want trace-to-logs and trace-to-metrics correlation in one UI.
• You want minimal operational overhead and no separate index database to scale and babysit.

Use them together (or migrate) when: you start on Jaeger for its UI, then move storage to Tempo for cost as volume grows. Both ingest OpenTelemetry, and Tempo speaks the Jaeger query API, so the switch is mostly configuration.

Most of the decision is storage model and ecosystem fit. Jaeger is the standalone tracing tool with a real UI and a real index; Tempo is the cheap-at-scale backend that shines inside the Grafana stack.

Deciding factor at a glance

The rule: if traces should be cheap and live inside Grafana next to your logs and metrics, pick Tempo; if you want a complete, standalone tracing system with its own UI, pick Jaeger.

What each tool is

Jaeger is a CNCF graduated distributed tracing system, originally built and open-sourced by Uber. It is a complete tracing stack: collectors that receive spans, a query service, and a mature web UI for searching and visualizing traces, dependency graphs, and span timelines. Its defining trait is pluggable storage - you back it with Cassandra, Elasticsearch, OpenSearch, or other stores that index spans so you can search by service, operation, tags, and latency. Jaeger has fully embraced OpenTelemetry and now recommends the OpenTelemetry SDKs for instrumentation. The strength is a self-contained, battle-tested tool with its own UI; the catch is that the indexed store it depends on is what you pay for and operate.

Grafana Tempo is an open-source, high-scale distributed tracing backend from Grafana Labs, designed from the start to be cost-efficient. Its defining trait is that it uses only object storage - S3, GCS, Azure Blob, or local disk - and builds no separate full index, which removes the most expensive and operationally heavy part of a tracing stack. Tempo has no standalone UI; you query it through Grafana using TraceQL, its trace query language. It integrates tightly with the rest of the Grafana stack, so you can jump from a Prometheus metric or a Loki log line straight to the relevant trace. The strength is cheap-at-scale storage and deep stack correlation; the catch is the dependency on Grafana to get value from it.

Jaeger vs Grafana Tempo: head-to-head

The pattern is clear: Jaeger leads on standalone maturity, its UI, and out-of-the-box search; Tempo leads on cost, operational simplicity, and correlation inside the Grafana stack. Both ingest OpenTelemetry, so the choice is about the storage and query layer, not instrumentation.

When to choose Jaeger

Jaeger is the right call when you want a complete, standalone tracing system that stands on its own. Concretely:

• You want a mature, self-contained tracing tool with its own UI rather than tracing bolted into a wider platform.
• You already run Cassandra or Elasticsearch and are comfortable operating an indexed store, so the storage cost is not new overhead.
• You need rich, immediate search - by service, operation, tags, and duration - without first standing up Grafana.
• You want the reassurance of a CNCF graduated project with a long track record and broad community tooling.
• Your team values dependency graphs and span timelines in a purpose-built tracing UI.
• You are not committed to the Grafana stack and do not want to be.

The trade-off you are accepting: the indexed storage backend is what costs money and operational attention, and that cost scales with your span volume.

When to choose Grafana Tempo

Grafana Tempo is the right call when cost at scale and stack correlation drive the decision. Concretely:

• You want the cheapest tracing at scale and are fine with traces living only in object storage like S3, GCS, or Azure Blob.
• You already run Grafana, Loki, and Prometheus and want trace-to-logs and trace-to-metrics correlation in a single UI.
• You want to avoid running a separate index database and the scaling work that comes with it.
• You generate high trace volume - large or autoscaling fleets - where an indexed store gets expensive fast.
• You are comfortable querying traces with TraceQL inside Grafana rather than in a dedicated tracing UI.
• You want a backend that fits an existing Grafana observability investment with minimal new moving parts.

The trade-off you are accepting: no standalone UI, a hard dependency on Grafana to get value, and a query experience that, while strong, lives entirely inside the Grafana stack.

Can you use them together?

You would not normally run both as primary backends, but they coexist and migrate cleanly because they share so much common ground. The most realistic pattern is a phased one: start on Jaeger for its self-contained UI and easy search, then migrate storage to Tempo once trace volume grows enough that the indexed store becomes a cost and operations problem.

That migration is light because both tools ingest OpenTelemetry over OTLP, so your application instrumentation never changes. You point the OpenTelemetry Collector at a new exporter and you are done. Tempo also implements the Jaeger query API, so a lot of Jaeger-era query tooling can keep working against Tempo. Some teams even run the Collector to fan out to both during a transition window to validate parity before cutting over. Whatever you pick, instrumenting with OpenTelemetry is what keeps you portable - the same principle we cover in our Datadog vs New Relic comparison for commercial backends.

Cost comparison

Both Jaeger and Grafana Tempo are open source and free to use, so the real cost is the infrastructure you run them on. That is where the storage design bet decides everything.

Jaeger: you pay for the index store. Jaeger’s pluggable backends - Cassandra, Elasticsearch, OpenSearch - index your spans so search is fast and flexible. The price of that is a database cluster you have to provision, scale, and operate, and it grows with span volume. For high-throughput systems, that store becomes the dominant line item and the biggest source of operational toil. The upside is that you get rich search and a complete tracing tool without depending on anything else.

Tempo: you pay for object storage. Tempo deliberately stores traces only in object storage and skips the full secondary index, so the cost is essentially the cost of S3, GCS, or Azure Blob - among the cheapest durable storage available - plus modest compute. There is no database cluster to scale. For large fleets producing terabytes of traces, this is where Tempo’s advantage becomes decisive, often by a wide margin. The catch is that you need Grafana in front of it and you query through TraceQL rather than a dedicated UI.

The bottom line on cost: at small scale the difference is minor and Jaeger’s standalone UX may be worth it; at large scale Tempo’s object-storage-only model is hard to beat. Neither charges per host or per seat, so the comparison is purely about the infrastructure each model demands.

Common pitfalls

• Assuming Tempo gives you a tracing UI on its own. It does not. Without Grafana wired up, Tempo is just a storage and query backend. Budget for the Grafana integration as part of adopting it.
• Underestimating Jaeger’s storage operations. The Jaeger UI is easy; running and scaling Cassandra or Elasticsearch underneath it is the real work, and it grows with your trace volume.
• Skipping OpenTelemetry and using legacy clients. Instrumenting with vendor-specific or legacy libraries locks you in. Use the OpenTelemetry SDKs and OTLP so you can switch backends with a config change later.
• Letting trace volume and sampling run unmanaged. Both backends get expensive or noisy without a sampling strategy. Decide head-based or tail-based sampling in the Collector before you scale, not after.
• Expecting trace-to-logs-to-metrics correlation outside the Grafana stack. That tight correlation is a Tempo-plus-Grafana strength. If you pick Jaeger, plan how you will jump between traces, logs, and metrics, because it is not built in.

Related reading

• Datadog vs New Relic (2026) - the two-tool comparison for commercial observability platforms.
• Datadog vs New Relic vs Dynatrace vs AppDynamics - the wider enterprise APM field.

Getting help

Choosing a tracing backend is the easy part; getting useful traces, sane sampling, and correlated signals is where teams stall. We run vendor-neutral performance audits that include distributed tracing coverage, and we set up continuous profiling alongside whichever backend you land on, so you get code-level visibility instead of just span timelines. If you want ongoing help keeping it healthy, our performance retainer covers tracing upkeep, CI/CD performance gates, and proactive bottleneck hunting.

Book a free scope call.