Comparing Vector Databases: Pinecone vs. Weaviate vs. Milvus
Introduction
Vector databases store embeddings for semantic search and AI workloads. This comparison examines Pinecone, Weaviate, and Milvus.
| Feature | Pinecone | Weaviate | Milvus |
|---|---|---|---|
| Deployment | Fully managed SaaS | SaaS & self-hosted | Self-hosted (Docker/K8s) |
| Scalability | Automatic scaling | Horizontal scaling | Horizontal scaling |
| Query Types | Approximate nearest neighbors (ANN) | ANN, hybrid search | ANN, scalar filtering |
| Data Models | Vector only | Vector + rich metadata | Vector + metadata |
| APIs | REST, gRPC, Python/JS SDKs | GraphQL, REST, Go/JS SDKs | gRPC, Python/Go/Java SDKs |
| Cost | Pay-per-usage | Subscription or self-hosted cost | Self-hosted infrastructure cost |
| Integrations | LangChain, LlamaIndex | OpenAI, LangChain, eclectic SDKs | Deep Lake, MelodyML |
| Authentication | API key | API key, OIDC | Token-based |
Summary
- Pinecone: Best for fully managed, low-maintenance vector similarity.
- Weaviate: Great for hybrid search and metadata-rich queries.
- Milvus: Excellent open-source option for large-scale, on-prem deployments.