LLM Analytics

Langfuse analytics derives actionable insights from production traces.

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

Metrics

• 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.

Dimensions

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).
• Tags: filter different use cases, features, etc. by adding tags to your traces.
• Release and version numbers: track how changes to the LLM application affected your metrics.

Feedback

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.

GitHub Discussions