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AI Tool Comparison
Langfuse vs Ragas
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
Ragas
Score RAG and agent outputs on faithfulness and context relevance, and generate synthetic test datasets when none exist — free open-source, maintained by Vibrant Labs (formerly ExplodingGradients).
Developer Tools
free
Bottom Line
Langfuse edges ahead on rating (4.7 vs 4.4), but the right pick still comes down to which workflow you're running.
Choose Langfuse if…
LLM Observability
Choose Ragas if…
Developer Tools
| Attribute | Langfuse | Ragas |
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Pricing | freemium | free |
| Pricing Detail | Free (50K units) / $29/mo Core / $199/mo Pro | Free and open source (Apache 2.0) |
| Rating |
Key Features
Langfuse
- Full LLM call tracing
- Prompt version management
- User session tracking
- Cost and latency analytics
- Evaluation datasets
- Self-hostable
Ragas
- RAG-specific metrics
- Faithfulness and relevancy scoring
- Synthetic test data generation
- Framework integrations
Pros
Langfuse
- •One of the best open-source options in LLM observability
- •Works with any LLM provider
- •Eval framework helps catch quality regressions early
Ragas
- •Purpose-built for RAG eval
- •Actionable metrics
- •Open source
Cons
Langfuse
- Setup requires SDK integration in your codebase
- Dashboard can feel complex for simple use cases
Ragas
- Focused only on evaluation
- Needs reference setup