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:

• Flat: Exact search, small datasets (< 10K)
• IVF: Inverted file, medium datasets (10K - 1M)
• HNSW: Graph-based, good recall/speed tradeoff
• PQ: Product quantization, memory efficient for billions

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

• Chroma: https://docs.trychroma.com
• FAISS: https://github.com/facebookresearch/faiss
• Qdrant: https://qdrant.tech/documentation/
• Pinecone: https://docs.pinecone.io