Why it matters
- The only major LLM observability platform that is both fully open source (MIT) and self-hostable — critical for teams with data residency requirements.
- Framework-agnostic: trace any LLM app with three lines of Python, regardless of the framework.
- Built-in prompt management with versioning means your prompts are versioned, testable, and rollback-able like code.
- Evaluation pipeline lets you systematically measure output quality on real production data.
Key capabilities
- Distributed tracing: Capture nested traces of every LLM call, retrieval, tool use, and agent step.
- Prompt management: Version, compare, and deploy prompts from the Langfuse UI with rollback support.
- Datasets and evaluation: Create evaluation datasets from production traces; run automated evals with custom scorers.
- User feedback: Collect thumbs up/down or custom scores from end users and attach to traces.
- Cost tracking: Per-request token counts and cost in USD across all LLM providers.
- Latency analytics: P50/P95/P99 latency for each model and trace type.
- Sessions: Group related traces into conversation sessions for user-level analysis.
- SDK support: Python and TypeScript/JavaScript SDKs; OpenAI drop-in proxy; LangChain/LlamaIndex integrations.
Technical notes
- License: MIT — fully open source at github.com/langfuse/langfuse
- Self-hosted deployment: Docker Compose; Kubernetes Helm chart; Railway/Render one-click deploy
- Backend: Next.js + Prisma; PostgreSQL + ClickHouse for analytics
- SDKs: Python (
pip install langfuse); JS/TS (npm install langfuse) - Integrations: LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, LiteLLM, Haystack, Instructor
- Langfuse Cloud pricing: Free (50K obs/mo); Team $59/mo; Enterprise custom
- Founded: 2023 by Max Deichmann and Marc Klingen; Berlin/San Francisco; backed by YC
Ideal for
- Teams with strict data privacy requirements who need self-hosted LLM observability with no data leaving their infrastructure.
- Engineering teams who want systematic prompt versioning and production quality monitoring for LLM apps.
- Companies building multi-step LLM pipelines who need to debug failures by replaying exact production traces.
Not ideal for
- Teams exclusively using LangChain who want zero-config setup — LangSmith requires one env variable.
- Small hobby projects where the operational overhead of self-hosting isn't worth it.
- Teams primarily needing model training experiment tracking — Weights & Biases is better for that.
See also
- LangSmith — LangChain's native observability platform, cloud-only but tighter LangChain integration.
- Helicone — Lightweight LLM proxy with observability, simpler setup.
- Weights & Biases — ML experiment tracking with W&B Weave for LLM tracing.