Langfuse
Self-HostedOpen-source LLM observability, tracing & evaluation platform
Overview
Langfuse is an open-source tool for debugging, monitoring, and optimizing LLM applications. It offers tracing for LLM workflows, prompt management with versioning, and evaluation metrics to measure performance. Integrates with LangChain, LlamaIndex, OpenAI, and more. Deploy via Docker Compose (simplest setup), Kubernetes (scalable), or use the managed cloud tier. Self-hosting keeps your LLM data private, while team features support collaboration on prompt iterations and evaluation.
Key Features
- LLM Tracing & Debugging
- Prompt Management & Versioning
- Evaluation Workflows & Metrics
- Framework Integrations (LangChain, LlamaIndex)
- Team Collaboration
Frequently Asked Questions
? Is Langfuse hard to install?
Langfuse is easy to install via Docker Compose—just follow the official docs for a one-command setup. For scalable deployments, Kubernetes configurations are available. Even users with basic Docker knowledge can get it running quickly.
? Is it a good alternative to PromptLayer?
Yes—Langfuse is a strong alternative: it provides open-source self-hosting (missing in PromptLayer’s proprietary setup) plus built-in evaluation workflows and framework integrations that match or exceed PromptLayer’s features.
? Is it completely free?
The self-hosted core version of Langfuse is 100% free and open-source (MIT license). The managed cloud tier has paid plans for advanced features like higher usage limits, priority support, and enterprise-grade security.
Top Alternatives
Tool Info
Pros
- ⊕ Privacy-focused self-hosting option
- ⊕ Open-source core with MIT license
- ⊕ Seamless integration with popular LLM frameworks
- ⊕ Real-time insights into LLM performance
Cons
- ⊖ Requires technical setup for self-hosting (Docker/K8s)
- ⊖ Cloud version has paid tiers for advanced features
- ⊖ Steeper learning curve for users new to LLM observability