This path is for you if every retrieval demo names a different database and they sound interchangeable. It separates an in-memory library, a dedicated engine, pgvector, and a managed platform. No part is live on this page yet. Start with Practical AI for what retrieval is, then come back to pick a product.
- 1 Do you need a vector database for AI search, or is a vector library enough? Choose an embedded library for batch corpora that fit in memory. Choose a vector database when you need live CRUD, metadata filters, sharding, or multi-tenant access. Scheduled · November 15, 2026
- 2 How to add vector search to the PostgreSQL database you already have with pgvector Enable pgvector, store embeddings beside rows, build HNSW, and filter tenants in the same SQL. Prefer this until tens of millions of vectors or extreme write rates force a specialist store. Scheduled · November 21, 2026
- 3 How to choose a managed vector database such as Pinecone, Weaviate, or Qdrant Map managed vector engines to workload: serverless ease, Rust performance, modular vectorizers, distributed scale, or lightweight local demos. Match features to real filters and write rates. Scheduled · November 26, 2026
- 4 How hybrid search and metadata filters make AI document search more accurate Combine dense and sparse retrieval with RRF, and apply metadata filters early. Exact IDs and tenant ACLs are not optional polish. Scheduled · November 29, 2026
- 5 Vector Database Cost, Sizing, and Infrastructure for Beginners Plan vector capacity from dimensions, count, and index overhead. Use quantization when RAM is the constraint. Treat infra sizing as a first-class design step. Scheduled · December 2, 2026
- 6 How to choose a vector database setup for a side project or a company Climb the vector stack by corpus size, write pattern, filter needs, and team capacity. Measure retrieval quality before you migrate. Scheduled · December 5, 2026