Langfuse
Open-source LLM observability platform for tracing, debugging, and evaluating AI application performance in production and development.
Langfuse is an open-source LLM engineering platform founded in 2023 by Clemens Rawert, Marc Klingen, and Max Deichmann that provides production observability, prompt management, and evaluation tooling for AI applications. Teams instrument their LLM calls with the Langfuse SDK and get full trace visibility - seeing exactly what prompts were sent, what models responded, latency at each step, token usage, and cost per request. The evaluation module supports automated scoring of LLM outputs using custom rubrics, LLM-as-judge, or human annotation workflows. Langfuse reached $3M ARR in early 2025, has 7,000+ GitHub stars, and is used by teams at Snowflake, Mistral, and PostHog. Self-hosted deployment is free and fully feature-complete.
Key Features
- Full LLM call tracing - log prompts, completions, latency, token count, and cost per request
- Prompt management - version, deploy, and A/B test prompts directly from the Langfuse dashboard
- Evaluation pipelines - score LLM outputs with automated rubrics, LLM-as-judge, or human annotation
- Dataset management - build and run regression test suites against stored input/output pairs
- SDKs for Python and TypeScript with native integrations for LangChain, LlamaIndex, and OpenAI
- Self-hosted Docker deployment - fully feature-complete with zero data leaving your infrastructure
Use Cases
- AI engineers debugging complex multi-step agent pipelines by tracing each LLM call in the chain
- Product teams running A/B tests on prompt variations and measuring their effect on output quality
- ML teams building evaluation datasets and automated scoring to catch regressions before deployment
- Enterprise AI teams deploying self-hosted Langfuse to keep sensitive LLM request data on-premises
Pros
- Self-hosted open-source version is fully featured - no premium features locked behind cloud pricing
- Prompt management and evaluation in one platform eliminates the need for separate observability tooling
- Native integrations with LangChain, LlamaIndex, and OpenAI SDK require minimal instrumentation code
Cons
- Cloud plan at $59/month scales up quickly for high-volume production applications generating many traces
- Evaluation tooling requires teams to define their own quality rubrics - no out-of-box scoring for most domains
- Self-hosting requires DevOps capacity to manage Docker infrastructure, updates, and storage growth
Langfuse Alternatives
Explore similar tools and alternatives
Looking for alternatives to Langfuse? Here are some similar tools you might like:
Braintrust
AI evaluation and testing platform for LLM applications with experiment tracking, human annotation, automated scoring, and production tracing.
Promptfoo
Open-source LLM testing framework that evaluates AI model outputs against test cases - used by developers to prevent regressions before deploying AI applications.
Weights & Biases
ML experiment tracking, model monitoring, and dataset versioning platform - used by OpenAI, Toyota, and 1,000+ organizations to ship better models faster.
Ready to try Langfuse?
Visit the official website to explore all features and get started with Langfuse today.
Reviews
0 reviews for Langfuse
Based on 0 reviews
Share your experience
Log in to write a review for Langfuse
Ito
Only code review that runs your code. Provides runtime analysis with evidence (logs, video, screenshot) to show how code changes application actually work. Back-end, front-end, api, integration.
Cursor
AI-native code editor built on VS Code with built-in AI chat, autocomplete, and codebase understanding.
GitHub Copilot
AI pair programmer by GitHub/OpenAI that suggests code completions, functions, and entire files in your IDE.
Have an AI Tool?
List your AI tool for free, or go featured for top placement in your category - and reach thousands of potential users.
Submit Your Tool