vector-databases

Vector Databases

Store and search embeddings for RAG, semantic search, and similarity applications.

Comparison

Database Best For Filtering Scale Managed Option

Chroma Local dev, prototyping Yes < 1M No

FAISS Max speed, GPU, batch No Billions No

Qdrant Production, hybrid search Yes Millions Yes

Pinecone Fully managed Yes Billions Yes (only)

Weaviate Hybrid search, GraphQL Yes Millions Yes

Chroma

Embedded vector database for prototyping. No server needed.

Strengths: Zero-config, auto-embedding, metadata filtering, persistent storage Limitations: Not for production scale, single-node only

Key concept: Collections hold documents + embeddings + metadata. Auto-embeds text if no vectors provided.

FAISS (Facebook AI)

Pure vector similarity - no metadata, no filtering, maximum speed.

Index types:

Strengths: Fastest, GPU support, scales to billions Limitations: No filtering, no metadata, vectors only

Key concept: Choose index based on dataset size. Trade accuracy for speed with approximate search.

Qdrant

Production-ready with rich filtering and hybrid search.

Strengths: Payload filtering, horizontal scaling, cloud option, gRPC API Limitations: More complex setup than Chroma

Key concept: "Payloads" are metadata attached to vectors. Filter during search, not after.

Index Algorithm Concepts

Algorithm How It Works Trade-off

Flat Compare to every vector Perfect recall, slow

IVF Cluster vectors, search nearby clusters Good recall, fast

HNSW Graph of neighbors Best recall/speed ratio

PQ Compress vectors Memory efficient, lower recall

Decision Guide

Requirement Recommendation

Quick prototype Chroma

Metadata filtering Chroma, Qdrant, Pinecone

Billions of vectors FAISS

GPU acceleration FAISS

Production deployment Qdrant or Pinecone

Fully managed Pinecone

On-premise control Qdrant, Chroma

Resources

Source Transparency