Working SWE → AI engineer · Stop 11 of 15 lessons

Vector stores worth using in 2026

What this lesson covers

The vector store market has settled. There are maybe five choices worth considering in 2026, and the right one for your workload is usually obvious once you name the constraints.

The outline

1. The four constraints. Corpus size, query rate, filter complexity, operational cost. The answer falls out of these.
2. pgvector. Postgres with a vector column. Why it’s the correct answer for 80% of teams and where it stops being one.
3. LanceDB. Embedded, file-based, batteries-included. When “no server” is the right architecture.
4. Qdrant and Weaviate. Purpose-built vector databases and what you actually get for the ops burden.
5. Managed (Turbopuffer, Pinecone). The economics of paying someone else to run it.
6. Hybrid search. BM25 + vector — why this beats vector-only in almost every real corpus, and how each store supports it.

Coming soon

In outline.