LangfuseFreemium
An open-source observability and evaluation platform for LLM applications — tracing, prompt management, evals, and cost monitoring.
Analytics comparison
Langfuse vs LangSmith
Pricing, pros, cons, and ideal use cases — side by side.
LangSmithFreemium
An observability and evaluation platform for LLM and agent applications — tracing, datasets, automated evals, and production monitoring in one place.
At a glance
| Langfuse | LangSmith | |
|---|---|---|
| Pricing | FreemiumOpen-source and free to self-host. Langfuse Cloud offers free and paid managed tiers. | FreemiumFree developer tier. Paid Plus and Enterprise plans add seats, longer trace retention, and deployment options. |
| Category | Analytics | Analytics |
| Ideal for | Teams running LLM apps and agents in productionEnterprises needing self-hosted observabilityEngineering orgs standardizing AI tracing and evals | Teams running agents in productionEnterprises needing eval and monitoringEngineering orgs standardizing AI observability |
Pros & cons
Langfuse
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
LangSmith
Pros
- End-to-end tracing for complex agent runs
- Datasets and automated evals in CI
- Production monitoring for quality and cost
- Framework-agnostic
Cons
- Most seamless within the LangChain ecosystem
- Trace volume can drive cost at scale
- Eval design is still real work
Which should you choose?
Langfuse is built around teams running llm apps and agents in production; LangSmith leans more toward teams running agents in production. Shortlist the one whose strengths line up with your biggest constraint.