Vector databases: when to use what

The first vector DB you pick will probably be wrong. The second will be okay. This lesson saves you from picking the wrong third.

Vector DBs are like databases for "find similar" instead of "find exact". Different ones suit different scales and stacks.

Top 2026 vector DB choices:

• ChromaDB: simplest local dev. Great for prototypes and modest production loads.
• FAISS: Facebook's in-memory library. Fast, library-only (you wrap it).
• pgvector: PostgreSQL extension. Best if you already use Postgres (Supabase).
• Qdrant: production-grade open source. Self-host or cloud.
• Weaviate: production-grade open source. Native hybrid search.
• Pinecone: hosted, easy, paid.
• Milvus: massive-scale open source.

Pick by deployment style (local vs cloud), scale (1K vs 1B vectors), and stack (already on Postgres? use pgvector).

Local Chroma quickstart:

import chromadb
client = chromadb.Client()
col = client.create_collection("docs")
col.add(ids=["1"], documents=["GeekHub is for developers"], metadatas=[{"src": "site"}])
res = col.query(query_texts=["who is GeekHub for?"], n_results=1)
print(res)

That is RAG retrieval in 5 lines.