What LLM providers does Langfuse support?

OpenAI, Anthropic, Google, Cohere, Azure OpenAI, and any provider through the generic SDK.

Can I self-host Langfuse?

Yes. Deploy with Docker on any infrastructure. Your data never leaves your servers.

Tracing follows each request through your AI pipeline — showing every LLM call, retrieval step, and processing stage with inputs and outputs.

Does Langfuse track costs?

Yes. Automatic cost calculation for every LLM call based on token usage and model pricing.

What is Prompt Management?

Version, test, and deploy prompts through the Langfuse dashboard without code changes. A/B test prompt variations.

Does Langfuse work with LangChain?

Yes. Native integrations with LangChain, LlamaIndex, and OpenAI SDK for automatic tracing.

Can I evaluate LLM outputs?

Yes. Manual and automated evaluation with custom scoring functions, LLM-as-judge, and human annotation.

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Langfuse — Open-Source LLM Observability for AI Applications

Langfuse is an open-source platform for monitoring, debugging, and optimizing large language model applications. It provides tracing, prompt management, evaluation, and cost tracking — the observability layer that every production AI application needs but few have.

The AI Observability Gap

Building an AI feature is easy. Understanding why it sometimes gives wrong answers, costs too much, or takes too long is hard. Langfuse traces every LLM call through your application, showing inputs, outputs, latency, cost, and quality scores — turning black-box AI into debuggable software.

Core Features

Traces follow a request through your entire AI pipeline — retrieval, prompt construction, LLM calls, post-processing. Each step shows inputs, outputs, latency, token usage, and cost. Prompt Management versions and deploys prompts without code changes. Evaluation scores track output quality over time.

Why Open Source Matters

Your AI traces contain sensitive user data and proprietary prompts. Self-hosting Langfuse keeps this data on your infrastructure. The open-source model also means no vendor lock-in and community-contributed integrations.

Langfuse vs Helicone

Helicone is a proxy-based LLM logger — simpler to set up but less flexible. Langfuse provides deeper tracing through complex AI pipelines with prompt management and evaluation. Helicone is better for quick logging. Langfuse is better for production AI systems.

Getting Started

Self-host Langfuse or use the cloud version through SaaSOffers
Integrate the SDK (Python or JS) into your AI application
Start seeing traces, costs, and quality metrics
Iterate on prompts using the management dashboard

Pricing

Self-hosted is free and open-source. Langfuse Cloud has a free tier with 50K observations per month. Pro at $59/month adds higher limits and team features.