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AI Tool Comparison
Langfuse vs LangSmith
A side-by-side breakdown to help you pick the right tool for your workflow.
Langfuse
Trace and score LLM application runs so teams can debug agent behavior and track cost per user or session.
Developer Tools
freemium
LangSmith
Debug, test, and monitor LLM applications and agents in production with LangChain's observability platform, billed by trace volume and seats.
Developer Tools
freemium
Bottom Line
Langfuse edges ahead on rating (4.7 vs 4.6), but the right pick still comes down to which workflow you're running.
Choose Langfuse if…
LLM Observability
Choose LangSmith if…
LLM Observability
| Attribute | Langfuse | LangSmith |
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Pricing | freemium | freemium |
| Pricing Detail | Free (50K units) / $29/mo Core / $199/mo Pro | Free (5K traces) / $39/seat/mo Plus / Enterprise custom |
| Rating |
Key Features
Langfuse
- Full LLM call tracing
- Prompt version management
- User session tracking
- Cost and latency analytics
- Evaluation datasets
- Self-hostable
LangSmith
- Full LLM call tracing
- LangChain native integration
- Evaluation datasets
- Automated regression testing
- Prompt playground
- Team collaboration
Pros
Langfuse
- •One of the best open-source options in LLM observability
- •Works with any LLM provider
- •Eval framework helps catch quality regressions early
LangSmith
- •Native LangChain integration provides trace depth that third-party tools can't match
- •Evaluation dataset workflow is among the most mature in the LLM observability category
- •Playground lets you test chains interactively before deploying changes
Cons
Langfuse
- Setup requires SDK integration in your codebase
- Dashboard can feel complex for simple use cases
LangSmith
- Less useful for non-LangChain stacks compared to framework-agnostic alternatives
- Free tier trace limits hit quickly in production