Langfuse: Open-Source LLM Observability You Can Self-Host

Most LLM observability tools are SaaS products that send your prompts and outputs to a third-party server. For companies with data privacy requirements — healthcare, finance, legal — that's a dealbreaker. Langfuse is the open-source alternative you can run entirely on your own infrastructure.

What Is Langfuse?

Langfuse is an open-source LLM engineering platform that provides:

It works with any LLM framework — LangChain, LlamaIndex, OpenAI SDK, Anthropic SDK, or raw HTTP calls.

Self-Hosting with Docker

Run `docker-compose up` and you have a full observability stack on your own server.

Integrating with Python

The `@observe()` decorator automatically captures inputs, outputs, latency, and token usage.

LangChain Integration

Every LangChain step — prompts, LLM calls, tools, retrievers — appears as a nested trace in Langfuse.

Prompt Management

Langfuse lets you manage prompts as versioned artefacts with metadata:

When you update a prompt in the Langfuse UI, your application picks up the new version without a code deploy. You can roll back instantly if quality drops.

Evaluations

Set up automated evaluators to score every response:

For LLM-as-judge evaluation:

Cost Tracking

Langfuse automatically calculates cost per trace based on token usage and model pricing. You get dashboards showing:

This is essential for managing LLM API budgets at scale.

Langfuse vs LangSmith

Choose Langfuse if: data privacy matters, you use multiple frameworks, or you want self-hosted.

Choose LangSmith if: you're all-in on LangChain and want tightest integration.

Getting Started

Observability isn't optional for production AI — it's how you catch hallucinations, measure quality, and make confident improvements.

Talk to us if you need help setting up LLM observability for your team.