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AI Tool Comparison
Langfuse vs Milvus
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
Milvus
Store and search billions of embeddings with a self-hosted open-source vector database, or run the same engine managed via Zilliz Cloud for RAG and semantic search at scale.
Developer Tools
freemium
Bottom Line
Langfuse edges ahead on rating (4.7 vs 4.5), but the right pick still comes down to which workflow you're running.
Choose Langfuse if…
LLM Observability
Choose Milvus if…
Developer Tools
| Attribute | Langfuse | Milvus |
|---|---|---|
| Category | Developer Tools | Developer Tools |
| Pricing | freemium | freemium |
| Pricing Detail | Free (50K units) / $29/mo Core / $199/mo Pro | Free (self-host) / Free tier Zilliz Cloud / from $99/mo Dedicated |
| Rating |
Key Features
Langfuse
- Full LLM call tracing
- Prompt version management
- User session tracking
- Cost and latency analytics
- Evaluation datasets
- Self-hostable
Milvus
- Billion-scale vector search
- GPU acceleration
- Distributed architecture
- Multiple index types
Pros
Langfuse
- •One of the best open-source options in LLM observability
- •Works with any LLM provider
- •Eval framework helps catch quality regressions early
Milvus
- •Scales extremely well
- •Mature and battle-tested
- •Cloud option (Zilliz)
Cons
Langfuse
- Setup requires SDK integration in your codebase
- Dashboard can feel complex for simple use cases
Milvus
- Heavier to operate
- Overkill for small projects