Langfuse
AnalyticsFreemiumVisit Site
An open-source observability and evaluation platform for LLM applications — tracing, prompt management, evals, and cost monitoring.
Overview
Langfuse gives LLM and agent applications the tracing and evaluation layer they need to be debuggable: full execution traces, prompt management and versioning, dataset-based evals, and cost and latency analytics. Being open-source and self-hostable is a real advantage for enterprises with data-residency or privacy constraints — traces of AI interactions can contain sensitive content, and keeping them inside your own boundary matters. It is framework-agnostic and a credible default for the observability slot in an enterprise AI stack.
Pros & Cons
Pros
- Full tracing for LLM and agent applications
- Open-source and self-hostable for data residency
- Prompt management, evals, and cost analytics
- Framework-agnostic
Cons
- Self-hosting adds operational work
- Eval design is still real effort
- Trace volume can grow storage needs at scale
Workflows that use Langfuse
Get a new AI workflow each week — many feature Langfuse and other tools in this category.