Langfuse

Langfuse is an open source LLM observability platform for tracing prompts, debugging AI chains, and monitoring inference costs in production. MIT licensed, self-hosted via Docker.

The problem it solves#

Langfuse overlaps with LangSmith, Helicone, and vendor-specific LLM monitoring when the need is traces, prompts, evaluation, and production AI debugging.

Yes. Langfuse documents Docker-based self-hosting and production options using components such as Postgres, ClickHouse, Redis or Valkey, and object storage.

LLM applications fail in ways traditional logging does not explain well. A bad answer may involve prompt changes, retrieved context, model selection, latency, tool calls, cost, or user feedback. Without a dedicated observability layer, teams debug production AI behavior by stitching together application logs, provider dashboards, and spreadsheet evaluations.

Langfuse gives teams a shared place to inspect LLM traces, evaluate outputs, and manage prompt behavior. That matters when an AI feature moves from prototype to production and the team needs evidence for why a response changed or why quality dropped.

Langfuse vs LangSmith

Langfuse is the better fit when an AI team wants LLM tracing, prompt tracking, datasets, scoring, and evaluations with a documented self-hosted path. LangSmith is stronger for teams already centered on LangChain and a managed vendor workflow. Choose Langfuse when observability data needs to stay closer to your infrastructure; choose LangSmith when hosted convenience and LangChain-native operations are the priority.

Langfuse describes itself as MIT licensed on its official site, but the OSA item record lists NOASSERTION. Confirm the current license and enterprise add-on terms before applying.