HandbookChaptersWhy do customers choose Langfuse?

Why do customers choose Langfuse?

Customers choose Langfuse because we are…

  • The most used open-source LLM Engineering platform (blog post)
  • Model and framework agnostic with 50+ integrations
  • Built for production & scale
  • Designed for complex agents and multi-step workflows
  • Offering a complete toolbox for AI Engineering
  • Incrementally adoptable, start with one feature and expand to the full platform over time
  • API-first, all features are available via API for custom integrations
  • Based on OpenTelemetry for interoperability
  • Easy to self-host

Langfuse is the most widely adopted LLM Engineering platform:

  • 19,446 GitHub stars
  • 23.1M+ SDK installs per month
  • 6M+ Docker pulls
  • Trusted by 19 of the Fortune 50 and 63 of the Fortune 500

Select customers include:

Open Source

  • Langfuse is open source.
  • You can self-host it, including the scalable observability backend that powers Langfuse Cloud without scalability limitations.
  • All product capabilities—tracing, evaluations, prompt management, experiments, annotation, the playground, and more—are MIT licensed without any usage limits.
  • We are transparent about what’s on the roadmap and how we operate.
  • We iterate with the community on GitHub because best practices for building great LLM applications are rapidly evolving. Please share your feedback and ideas with us.
  • Self-hosting keeps data within your infrastructure; Cloud offloads operational overhead. You can switch between OSS, Enterprise self-host, and Langfuse Cloud at any time—no feature flags to untangle, no vendor lock-in.

Extensive Integrations built on OpenTelemetry

Developer First

  • Langfuse is built for developers.
  • We are creating a technical product with great developer experience.
  • Langfuse is powerful yet simple, allowing you to build custom logic on top of it.
  • All data is accessible via public APIs and SDKs.
  • MCP Server for AI-native workflows and integrations.

Reliable Partner

Built for Complex Use Cases

  • We designed Langfuse with complex, nested LLM calls in mind—especially for agents and multi-step workflows.
  • Agent graphs provide visual representations of complex agent workflows, helping you understand and debug multi-step reasoning processes.
  • Langfuse enables hierarchical representations of your application in traces. Why are traces the core abstraction for LLM Engineering? Learn more in this webinar.
  • Multi-modal support for tracing text, images, audio, and other modalities.
  • MCP tracing for Model Context Protocol server interactions.
  • We go beyond Input/Output to include all the context of your app via metadata, tags, and sessions.

Comprehensive Platform

We are building the core development platform you need to build robust LLM applications. Langfuse offers four integrated pillars:

Built for Scale

ISO 27001SOC 2GDPRHIPAA

Security and Compliance

Public Metrics

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