Milvus vs Qdrant
| Feature | Milvus | Qdrant |
|---|---|---|
| Category | LLMs & AI Infra | LLMs & AI Infra |
| Sub-category | Vector DB | Vector DB |
| Maturity | stable | stable |
| Complexity | intermediate | intermediate |
| Performance tier | enterprise grade | enterprise grade |
| License | Apache-2.0 | Apache-2.0 |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 32.0K | 22.0K |
| Contributors | 500 | 150 |
| Commit frequency | daily | daily |
| Plugin ecosystem | none | none |
| Docs quality | good | good |
| Backing org | Zilliz | Qdrant |
| Funding model | vc_backed | vc_backed |
| Min RAM | 8 GB | 2 GB |
| Min CPU cores | 4 | 2 |
| Scaling pattern | distributed | horizontal |
| Self-hostable | Yes | Yes |
| K8s native | Yes | Yes |
| Offline capable | No | No |
| Vendor lock-in | none | none |
| Languages | Go, C++ | Rust |
| API type | REST, gRPC | REST, gRPC |
| Protocols | HTTP | HTTP |
| Deployment | docker, binary | docker, binary |
| SDK languages | python, javascript, go, rust | python, javascript, go, rust |
| Team size fit | solo, small, medium, enterprise | solo, small, medium, enterprise |
| First release | 2021 | 2021 |
| Latest version | — | — |
When to use Milvus
- ✓ RAG retrieval backend for LLM applications
- ✓ Product recommendation via embedding similarity
- ✓ Semantic document search across knowledge base
- ✓ Image similarity search for e-commerce
When to use Qdrant
- ✓ RAG retrieval backend for LLM applications
- ✓ Product recommendation via embedding similarity
- ✓ Semantic document search across knowledge base
- ✓ Image similarity search for e-commerce
Milvus anti-patterns
- ✕ Not for traditional OLTP/OLAP queries
- ✕ Heavy resource requirements
- ✕ Complex cluster management
Qdrant anti-patterns
- ✕ Not a general-purpose database
- ✕ No SQL support
- ✕ Needs separate storage for non-vector data