An open-source LLM engineering platform providing comprehensive tools for tracing, debugging, evaluating, and monitoring LLM applications to help teams build and ship reliable AI products from development to production.
Langfuse is an open-source platform designed for developers and teams building applications on top of large language models (LLMs). It helps them debug, monitor, and improve their LLM-powered features through detailed tracing, evaluation frameworks, prompt management, and cost analytics. Unlike some proprietary-only solutions, its open-source nature offers significant flexibility, including self-hosting options and a transparent development model driven by community contributions. The platform unifies the entire LLM development lifecycle, from initial experimentation with prompts to production monitoring and user feedback analysis. It primarily serves ML engineers, data scientists, and full-stack developers who require robust, specialized tooling to build and maintain reliable and scalable AI products.
Developers, LLM engineers, data scientists, and product teams building, monitoring, and iterating on applications powered by large language models.
Based on 0 reviews
2023
Berlin, Germany
Cloud Hobby
Includes 50,000 observations/month, 1 project, 3 team members, and 7-day data retention. Ideal for personal projects and initial exploration.
Free
Cloud Pro
Starts with 100,000 observations/month, then usage-based pricing. Includes unlimited projects, unlimited members, 30-day data retention, and Role-Based Access Control (RBAC).
$50/mo
Enterprise
For large-scale deployments, available as self-hosted or on a dedicated cloud. Includes unlimited observations, members, and projects, plus features like SSO, custom data retention, and dedicated support.
Custom
A strong choice from the creators of LangChain, offering exceptional, seamless integration if you are already heavily invested in that specific framework.
A comprehensive MLOps platform supporting LLM experimentation, often preferred by teams already using it for traditional ML model training and tracking.
A broader machine learning observability platform that has extended into LLMs, making it a good fit for teams wanting a single solution to monitor both traditional ML and LLM applications.
An open-source alternative focused on LLM evaluation and observability in a notebook environment, appealing to users who prioritize local, code-first analysis.
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