Langfuse is an open source LLM observability platform for tracing prompts, evaluating output quality, and monitoring inference costs across AI applications in production.
The Problem
LLM applications fail in non-obvious ways. A prompt change that improves one use case silently degrades another. An AI chain that performs well in testing produces hallucinations or cost spikes at scale. Without structured tracing and evaluation, debugging production AI applications requires reading logs and guessing which step in a multi-step chain went wrong.
How Langfuse Solves It
Langfuse captures every prompt, completion, and tool call as a structured trace, linking them across multi-step chains. Teams can tag traces, run evaluations (automated or human), and track inference costs per user, session, or feature. Integrations cover LangChain, LlamaIndex, OpenAI SDK, Vercel AI SDK, and custom HTTP clients. MIT licensed; self-host via Docker.
Key Features
- Full trace capture for LLM chains: every prompt, completion, tool call, and latency logged as a structured event
- Evaluation framework for scoring outputs (automated evals and human labeling queues)
- Cost tracking by model, user, session, and feature for AI spend attribution
- Prompt management with versioning and A/B testing for controlled prompt changes
- Integrations for LangChain, LlamaIndex, OpenAI, Anthropic, Vercel AI SDK, and OpenAI-compatible APIs
- MIT licensed; deploy via Docker Compose or use Langfuse Cloud
Who It's For
Langfuse is best for engineering teams building production LLM applications who need to debug prompt failures, track quality regressions across model upgrades, and attribute AI infrastructure costs to specific features or user cohorts.
Compared to Datadog LLM Observability
Unlike Datadog's LLM observability module, Langfuse is open source, self-hostable, and purpose-built for LLM tracing with prompt management and evaluation workflows. Datadog integrates LLM observability into a broader infrastructure monitoring platform; Langfuse is a dedicated tool with deeper evaluation and prompt iteration features at a fraction of the cost.
