Both platforms cover more than raw LLM logs. Compare how they help your team investigate a failure, test a change, and operate the system over time.
Teams choosing an LLM observability and development platform
Evaluate Lunary
Bring a customer conversation, its feedback, and its underlying model and tool calls into the trial.
Evaluate Langfuse
Bring a representative trace, a dataset experiment, and your open-source deployment requirements.
Decide with the operators
Include the engineers who maintain ingestion and the teammates who review model quality.
At a glance
| Area | Lunary | Langfuse |
|---|---|---|
| Workflow to test | Conversation → application trace → prompt | Trace → score → dataset experiment |
| Prompt management | Templates and playground | Versioned prompts and SDK retrieval |
| Self-hosting | Documented deployment guides | Documented open-source deployment guides |
| Public MCP capability | Documentation search and reading | Documentation and separate data access server |
| Historical move | Explicit field mapping and dataset import | Filtered UI and blob-storage export options |
Begin with the overlapping capabilities
Langfuse documents tracing, evaluation, prompts, and self-hosting. Lunary documents application observability, conversations, prompt templates, and self-hosting. A useful comparison acknowledges this overlap. Start with a difficult workflow: a user reports an incorrect answer, a tool returned stale information, and a prompt change may have made the problem worse.
See how each teammate finds the evidence
Give the same issue to an engineer and a product reviewer. The engineer should identify the failing operation; the reviewer should understand what the customer experienced. Check how easily they share a useful link, narrow the relevant time range, and distinguish a model error from an upstream data problem. Record the missing context you had to retrieve elsewhere.
Compare the actual MCP capability
Langfuse documents an MCP server that lets assistants work with Langfuse data. Lunary's public MCP server supplies read-only documentation search and content. It does not call the Lunary API on your behalf. These are different capabilities, and an MCP checkbox would hide that difference. Separately, application instrumentation can record tool operations regardless of how an assistant accesses documentation.
Price the complete deployment
If self-hosting matters, review each architecture with the person who will operate it. Verify backup recovery, upgrade procedures, capacity, and required feature editions. If you prefer managed hosting, model your actual volume, retention, seats, and evaluator usage. The least expensive subscription can still be a poor fit if it adds recurring operational work.
Check whether the same data tells the same story
Send a representative task to each candidate during a controlled trial. Compare the number of child operations, the model identifiers, token fields, and the recorded failure. Differences may come from instrumentation mapping rather than product behavior. Write down how retries and cancelled operations are counted before comparing charts. Only compare cost and latency over equivalent records; an apparently cheaper or faster dashboard may simply be missing an operation that the other system captured.
Common questions
Does Langfuse have prompt management and user context?
Yes. Do not choose on the premise that these are missing. Use current documentation and a trial of your specific workflow.
Can the Lunary Docs MCP server query my production traces?
No. The documented server is read-only access to developer documentation, not authenticated access to your Lunary project.
Sources & methodology
Lunary publishes this guide. We compare documented workflows and explain where each approach fits; this is not an independent benchmark or a hands-on product rating. Features, limits, and commercial terms can change. Check the linked vendor documentation before deciding.