Not All Vector Databases Are the Same
The vector database ecosystem has exploded since 2022. Choosing the right one depends on your scale, operational model, and existing infrastructure.
Managed / Cloud-native:
- Pinecone: Fully managed, serverless option available, no infrastructure to operate. Excellent for teams that want to move fast without managing infrastructure. Proprietary — no self-hosting option.
- Weaviate Cloud: Managed version of the open-source Weaviate. Supports hybrid search (vector + keyword BM25) natively.
Open-source / Self-hosted:
- Milvus: The most feature-rich open-source vector database. Supports IVF, HNSW, and GPU-accelerated indexing. Designed for billion-scale datasets. Requires Kubernetes for production deployment.
- Qdrant: Written in Rust for high performance. Excellent filtering support — you can combine vector similarity with metadata filters efficiently. Good developer experience.
- Weaviate: Open-source with a GraphQL API. Built-in support for multiple modalities (text, images). Supports hybrid search out of the box.
- Chroma: Lightweight, embedded, Python-native. Ideal for prototyping and local development. Not designed for production scale.
Vector extensions on existing databases:
- pgvector (PostgreSQL): Adds vector similarity search to PostgreSQL. If you already run Postgres, this is the lowest-friction path. Performance doesn't match dedicated vector databases at large scale, but it's excellent for datasets under ~1 million vectors.
- Redis Vector Search: Adds vector search to Redis. Good for low-latency use cases where data already lives in Redis.
Decision framework:
Are you prototyping?
→ Chroma (local) or pgvector (if you have Postgres)
Do you need managed infrastructure?
→ Pinecone (simplest) or Weaviate Cloud
Do you need self-hosted at scale?
→ Milvus (billion+ vectors) or Qdrant (strong filtering)
Do you need hybrid vector + keyword search?
→ Weaviate or Elasticsearch with vector support