Jaeger vs Zipkin: Which Distributed Tracing?
Jaeger is a CNCF tracing system with rich sampling and storage options; Zipkin is a lightweight, long-established tracing system that is simple to run.
Jaeger, originally from Uber and now CNCF-graduated, offers adaptive sampling, multiple storage backends, and deep OpenTelemetry integration. Zipkin, one of the earliest open tracing systems, prizes simplicity and a small footprint with broad language support. Jaeger wins on scale, sampling sophistication, and modern OTel alignment; Zipkin wins on simplicity and ease of getting started.
| Jaeger | Zipkin | |
|---|---|---|
| Maturity | CNCF graduated | Long established |
| Sampling | Adaptive, advanced | Basic |
| Storage | Cassandra, ES, more | In-memory, MySQL, ES |
| Footprint | Larger | Lightweight |
| Best for | Scale, OTel-native | Simple, quick start |
Use case and ecosystem
Jaeger suits larger systems needing adaptive sampling, scalable storage, and tight OpenTelemetry collector integration. Zipkin suits smaller setups or teams wanting a minimal tracing backend that is trivial to deploy and instrument.
Ops and CI fit
Both run as containers with pluggable storage; Jaeger has more moving parts at scale. Tracing backends and their instrumentation are integration-tested in CI, where faster managed runners speed up image builds and end-to-end trace verification.
The verdict
Want advanced sampling, scalable storage, and OTel-native design: Jaeger. Want a lightweight, simple-to-run tracer: Zipkin. Most new deployments lean Jaeger; Zipkin remains a solid minimal choice.