Comparison
Langfuse vs LangSmith
Langfuse and LangSmith are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Langfuse
Langfuse comes up when the question is fundamentally about infrastructure.
A startup self-hosts Langfuse on a single VM and instruments their multi-tenant LLM app with the Python SDK.
When you would reach for LangSmith
LangSmith comes up when the question is fundamentally about infrastructure.
A LangChain app with one line of setup: every chain run shows up in the LangSmith trace UI with input, output, intermediate steps, and per-step costs.
Frequently asked
What is the difference between Langfuse and LangSmith?
Langfuse: Langfuse is an open-source LLM observability platform with tracing, prompt management, evaluation, and a self-host option. Popular default for teams who want LangSmith-equivalent tooling without the SaaS lock-in. LangSmith: LangSmith is LangChain's commercial LLM observability and evaluation platform. It captures traces (LangChain-native and OTel), runs evaluations, manages prompt versions, and supports dataset curation.
When should I use Langfuse vs LangSmith?
Langfuse is the right concept when you are focused on infrastructure. LangSmith applies when you are focused on infrastructure.
Are Langfuse and LangSmith the same thing?
No. Langfuse is infrastructure; LangSmith is infrastructure. They are related but address different parts of the AI stack.