Langfuse analytics derives actionable insights from production data.


→ Not using Langfuse yet? Explore the dashboard in our interactive demo.


  • Quality is measured through user feedback, model-based scoring, human-in-the-loop scored samples or custom scores via SDKs/API (see scores). Quality is assessed over time as well as across prompt versions, LLMs and users.
  • Cost and Latency are accurately measured and broken down by user, session, geography, feature, model and prompt version.
  • Volume based on the ingested traces and tokens used.


Analytics is incrementally adoptable based on the data you send to Langfuse. The following dimensions are available:

  • Trace name: differentiate between different use cases, features, etc. by adding a name field to your traces.
  • User: track usage and cost by user. Just add a userId to your traces (docs).
  • Release and version numbers: track how changes to the LLM application affected your metrics.


We are continuously adding new charts to the dashboard. If you have any feedback or requests, please create a GitHub Issue or share your idea with the community on Discord.

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