If OpenTelemetry is the camera that takes the pictures (traces), Jaeger and Tempo are the albums where you store them. They are both open-source backends designed for distributed tracing, helping you visualize exactly how a single user request flows across multiple microservices.

However, they are built with completely opposite engineering philosophies.

Jaeger: The Rich, Standalone Heavyweight

Created by Uber and hosted by the CNCF, Jaeger is the older, battle-tested veteran of distributed tracing.

• How it works: Jaeger loves databases. It is designed to index everything. When you search for a trace, it queries database backends like Elasticsearch, OpenSearch, or Cassandra.
• The Interface: It has its own dedicated, standalone web UI.
• Key Strength (Powerful Searching): Because it indexes everything, Jaeger’s search capability is top-tier. You can effortlessly hunt for traces using custom tags, status codes (e.g., http.status_code=500), specific services, or durations right out of the box.
• The Catch (High Cost): Managing Elasticsearch or Cassandra at a massive scale is an operational nightmare. Indexing every single trace requires huge amounts of RAM and disk space, making Jaeger very expensive to run if you have terabytes of trace volume.

Grafana Tempo: The Cheap, High-Scale Disruptor

Created by Grafana Labs, Tempo is the modern challenger built specifically to fix Jaeger’s massive storage bills.

• How it works: Tempo is completely index-free. Instead of setting up massive, complex databases, Tempo batches traces together and throws them directly into Object Storage (like Amazon S3, Google Cloud Storage, or Azure Blob).
• The Interface: It doesn’t have its own UI; it uses Grafana natively.
• Key Strength (Insanely Cheap & Scalable): Object storage is incredibly cheap. Tempo can run with 10x to 100x lower storage costs than Jaeger because it doesn’t waste resources building heavy search indexes.
• The Catch (How do you find a trace?): Because there are no indexes, you traditionally couldn’t just “search” Tempo for a random trace attribute easily. Instead, it relies on a “Metrics-to-Logs-to-Traces” workflow. You look at a Prometheus chart, click a spike, find the log line in Grafana Loki, click the trace_id embedded in the log, and Tempo instantly pulls it up via that ID. (Note: Tempo now includes a query language called TraceQL to allow searching, but it still heavily relies on object storage scanning).

Side-by-Side Comparison

Feature	Jaeger	Grafana Tempo
Primary Storage Backend	Elasticsearch, Cassandra, OpenSearch	Amazon S3, Google Cloud Storage, Azure Blob
Cost at Scale	High (Requires heavy compute/RAM for DBs)	Extremely Low (Relies on cheap object storage)
User Interface	Standalone Jaeger UI	Natively integrated inside Grafana
Search Mechanism	Full-text, database-indexed search	TraceQL or structural discovery via logs/metrics
Ecosystem	Standalone CNCF tool	Tightly bound to the Grafana LGTM stack (Loki/Grafana/Tempo/Mimir)

Which one should you pick?

• Choose Jaeger if: You want a standalone, rock-solid tracing tool, you have a massive budget for Elasticsearch, or your engineers absolutely need to query traces using complex tag filtering without depending on logs or Grafana.
• Choose Tempo if: You are already using Grafana, you want to view metrics, logs, and traces side-by-side in a single window, and you want to scale up tracing across millions of requests without destroying your cloud infrastructure budget.