Jaeger vs Tempo: choosing the right OpenTelemetry tracing backend

Jaeger provides superior search and native OTel support

Jaeger v2 integrates with the OpenTelemetry Collector framework. It operates as a single binary. This change reduces the container image size from 40MB to 30MB. The release removes command-line parameter inconsistencies between components. Users enable specific functions through a single YAML configuration file. This architectural shift makes Jaeger the preferred tracing system for OpenTelemetry. It natively supports the OTLP data format. Older versions required an intermediate translation step to use OTLP. Jaeger also includes adaptive sampling and advanced visualization tools. Users search for traces by service name, operation, tags, or duration. Jaeger users can search for all slow requests from their frontend service using specific filters such as service name, operation, error status, and duration to identify outliers within their massive and complex microservices architectures. This flexibility helps when engineers do not know exactly what they are looking for. Tracing becomes necessary when complexity increases to dozens, hundreds, or thousands of microservices. A service mesh cannot replace tracing because it relies on underlying components to handle correlation and context propagation. Implementation of context propagation depends heavily on the programming language used. Jaeger also replaces the Jaeger Agent with the OpenTelemetry Collector. Uber originally created Jaeger because it needed custom tracing collection and storage code before Zipkin existed. Jaeger also wanted to support OpenTracing data models. A new visualization tool called the transitive dependency graph is also being developed to improve architecture investigation. Jaeger remains similar to Zipkin but includes features Zipkin lacks. It can receive data from Zipkin and process it.

Tempo simplifies scale through object storage

Tempo focuses on high-volume tracing through a unique storage architecture that avoids the operational complexity of managing stateful databases by utilizing object storage such as S3 or GCS. Tempo writes trace data in blocks to these backend storage systems. It uses a Distributor to accept formats including OTLP or Zipkin. An Ingester buffers and groups the data before writing it. A Compactor optimizes these blocks to improve efficiency. You probably already use Grafana for your metrics. Tempo uses Grafana for visualization instead of a custom UI. This integration allows teams to view traces alongside metrics or logs on the same dashboard. Tempo uses 10 to 100 times less storage than Jaeger for the same trace volume. This makes it a cost-efficient choice for massive scales. Tempo uses Grafana Agents to collect, batch, and forward traces. These agents push data to the Tempo backend without needing complex preprocessing. Tempo is a newer entrant that was announced by Grafana Labs in October 2020, and it was released under the AGPLv3 license to provide a high-scale, open-source alternative for modern observability needs. Tempo supports instrumentation via OpenTelemetry or Zipkin. This allows teams to adopt OpenTelemetry gradually while still supporting older formats. However, Tempo lacks traditional search features because it does not build indexes. You must have the trace ID to look up a trace. Newer versions of Tempo introduced TraceQL to allow filtering by common attributes.

Architecture trade-offs for observability pipelines

The choice between Jaeger and Tempo often depends on the scale of the microservices and the existing observability stack. For small setups, the complexity of Jaeger might be unnecessary. For massive scale, the search limitations of Tempo might be a problem. Users must decide if they value search flexibility or storage cost.

Jaeger remains the best option for teams requiring deep exploratory debugging. Its ability to search by tags and attributes helps identify outliers. The management of Elasticsearch or Cassandra at scale involves significant operational overhead and tuning. This complexity arises because Jaeger follows a multi-component architecture where each part handles a specific responsibility, such as processing, validating, or writing data to storage. Tempo works better for teams prioritizing low cost and simple infrastructure. It scales easily by using object storage and avoids database management. You cannot search by attributes or tags in Tempo unless you already captured that context in logs or metrics. Can observability teams achieve full consolidation without sacrificing search flexibility?