Jaeger vs Zipkin: Distributed Tracing System Comparison

Overview

Jaeger and Zipkin are both open-source distributed tracing systems designed to help engineers understand request flows across microservices. Zipkin, originally developed at Twitter and inspired by Google Dapper, is the older of the two. Jaeger, created at Uber and donated to the CNCF, was designed with modern cloud-native scale requirements in mind.

Both systems have converged on OpenTelemetry as the standard instrumentation layer, but they differ in architecture, sampling flexibility, storage options, and UI sophistication.

Key Technical Differences

Jaeger's architecture is more modular: a collector receives spans, a query service serves the UI, and a storage backend (Elasticsearch, Cassandra, or Badger) persists trace data. An optional Kafka buffer smooths ingestion spikes in high-throughput environments. This modularity enables operational flexibility but increases deployment complexity.

Zipkin is simpler by design — a single JAR or Docker image handles collection, storage, and UI. This makes it ideal for development environments and smaller deployments where operational simplicity matters more than scalability.

Sampling strategy is a critical differentiator. Jaeger supports remote sampling configuration, allowing sampling rates to be updated without redeploying services. Its adaptive sampling feature automatically adjusts rates per service based on traffic patterns. Zipkin relies on client-side fixed-rate sampling, which is less dynamic in heterogeneous microservice environments.

Performance & Scale

Jaeger was designed from the outset for Uber's scale — tens of thousands of microservices generating billions of spans per day. Its Kafka-buffered ingestion pipeline handles traffic bursts gracefully. Zipkin can scale with Cassandra or Elasticsearch backends, but it lacks Jaeger's built-in buffer and adaptive sampling, making it harder to maintain data quality under load.

When to Choose Each

Choose Jaeger for new distributed tracing deployments, especially in Kubernetes-native environments. Its CNCF status, OpenTelemetry-first design, and adaptive sampling make it the better long-term foundation. The Jaeger Operator simplifies Kubernetes deployment significantly.

Choose Zipkin when migrating existing systems with Zipkin instrumentation, or when simplicity and a single-binary deployment are priorities. Many teams use Zipkin in development and evaluate Jaeger for production.

Bottom Line

Jaeger is the more capable and actively developed system for production distributed tracing. Zipkin remains a solid choice for simplicity and legacy instrumentation compatibility, but new projects should start with Jaeger and OpenTelemetry.