An open-source observability and analytics platform for LLM applications, helping developers trace, debug, evaluate, and monitor their AI products from development to production with detailed insights into performance and quality.
Langfuse provides a comprehensive suite of tools specifically designed for the lifecycle of Large Language Model (LLM) applications. It empowers engineering teams and data scientists to gain deep visibility into their AI systems by capturing detailed traces of every API call, prompt, and generation. Users can debug complex chains, monitor production performance for latency and cost, and evaluate model or prompt changes against predefined datasets. Its unique value proposition lies in its open-source core, which offers ultimate flexibility and data privacy through self-hosting, while a managed cloud version provides a quick and scalable setup. Langfuse bridges the crucial gap between development debugging and production monitoring, making it an essential MLOps tool for building reliable LLM products.
AI/ML Engineers, Data Scientists, and software development teams building, deploying, and maintaining applications that leverage Large Language Models (LLMs).
Based on 0 reviews
2023
Berlin, Germany
Self-Hosted
Open-source community edition. Unlimited usage, requires your own infrastructure for hosting. Community support.
Free
Hobby
Cloud-hosted plan. Includes up to 10,000 observations/mo, 1 project, 2 team seats, and 7-day data retention.
Free
Pro
Cloud-hosted plan for growing projects. Includes 50,000 observations/mo, 5 projects, 5 seats, prompt management, and 30-day data retention.
$59/mo
Team
Cloud-hosted plan for teams. Includes 500,000 observations/mo, unlimited projects, 20 seats, SSO, roles & permissions, and 90-day data retention.
$499/mo
A strong alternative from the creators of LangChain, offering very tight integration with that ecosystem but is a closed-source product.
A broader ML observability platform that supports LLMs, making it a good choice for enterprises needing to monitor both traditional ML and generative AI models.
A direct competitor focused on simplicity and ease of use for LLM monitoring, which may be preferable for teams wanting a less complex solution.
An attractive option for companies already heavily invested in the Datadog ecosystem who want to centralize all application and infrastructure monitoring in one place.
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