Storing and searching millions of vectors efficiently requires specialized databases. Traditional databases use B-trees for exact lookups; vector databases use approximate nearest neighbor (ANN) algorithms for similarity search.

Why Not Just Use PostgreSQL?

You can store vectors in PostgreSQL with pgvector, and for small-to-medium datasets (<1M vectors), this works well. But dedicated vector databases offer:

• Faster search at scale: Optimized ANN indexes that search billions of vectors in milliseconds
• Filtering: Combine vector similarity with metadata filters efficiently
• Managed infrastructure: Auto-scaling, replication, backups

Top Vector Databases Compared

ANN Index Types

• HNSW (Hierarchical Navigable Small World): Best all-around. Fast queries, good recall, but memory-intensive. Used by most modern vector DBs.
• IVF (Inverted File Index): Clusters vectors and searches relevant clusters. Lower memory but requires training.
• ScaNN: Google's ANN library. Excellent for large-scale deployments.

Choosing the Right Database

Consider these factors:

1. Scale: <100K vectors? pgvector or ChromaDB. Millions? Pinecone or Qdrant. Billions? Milvus.
2. Latency requirements: Real-time search needs HNSW. Batch processing can use IVF.
3. Filtering complexity: Need complex metadata filters? Qdrant and Weaviate excel here.
4. Deployment preference: Managed (Pinecone) vs. self-hosted (Qdrant, Milvus).
5. Budget: Open-source options are free but require ops expertise.