Vector Database Comparison
A comprehensive comparison of the leading vector databases in 2024.
Best for Prototyping
Chroma
Zero setup, runs locally
Best Managed Service
Pinecone
Zero ops, enterprise ready
Best Self-Hosted
Qdrant
Fast, great filtering
| Feature | Pinecone | Weaviate | Qdrant | Milvus | Chroma | pgvector |
|---|---|---|---|---|---|---|
| Type | Managed only | Open + Managed | Open + Managed | Open source | Open source | Extension |
| Self-host | ✗ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Free Tier | Yes (limited) | Yes | Yes | — | Unlimited | Free |
| Index Type | Proprietary | HNSW | HNSW | Multiple | HNSW | IVFFlat/HNSW |
| Filtering | Good | Excellent | Excellent | Good | Basic | Full SQL |
| Hybrid Search | ✓ | ✓ | ✓ | ✓ | ✗ | With PG FTS |
| Language | — | Go | Rust | Go | Python | C |
| Max Dimensions | 20,000 | 65,535 | 65,535 | 32,768 | No limit | 2,000 |
Pinecone
Pros
- Zero infrastructure management
- Excellent developer experience
- Enterprise-grade reliability
- Great documentation
Cons
- Cannot self-host
- Can get expensive at scale
- Vendor lock-in
- Limited free tier
Best for: Teams wanting managed infrastructure with enterprise support.
Weaviate
Pros
- Powerful hybrid search
- Built-in vectorization modules
- GraphQL API
- Active community
Cons
- Steeper learning curve
- More complex configuration
- Resource intensive
Best for: Applications needing hybrid search and built-in ML capabilities.
Qdrant
Pros
- Excellent performance (Rust)
- Advanced filtering capabilities
- Easy to deploy
- Growing rapidly
Cons
- Newer, smaller community
- Fewer integrations
- Less mature ecosystem
Best for: Self-hosted deployments prioritizing performance and filtering.
Chroma
Pros
- Dead simple to start
- Runs in-memory or persistent
- Great Python integration
- Perfect for prototypes
Cons
- Limited production features
- No hybrid search
- Basic filtering
- Single-node only
Best for: Learning, prototyping, and small-scale applications.
pgvector
Pros
- Uses existing PostgreSQL
- Full SQL capabilities
- Familiar tooling
- No new infrastructure
Cons
- Lower dimension limit
- Not as performant at scale
- Limited to 2000 dimensions
Best for: Teams already using PostgreSQL who want to add vector search.
Not sure which to choose?
Use our interactive selector to get a personalized recommendation based on your specific needs.
Try the Vector DB Selector →