Picking a vector DB in 2026: pgvector, Pinecone, Weaviate
Three serious vector DBs, three very different DNA. Here's the decision framework that held up across our 2026 projects.
Three serious vector DBs, three very different DNA. Here's the decision framework that held up across our 2026 projects.
Reviewed by:Dezső Mező· Founder · Engineer, DField Solutions· 22 Jan 2026
On most new RAG projects this is the first real architecture decision: where does the vector DB live. Get it wrong and you're not just out some money — you're out months migrating later. Three serious contenders: pgvector, Pinecone, Weaviate.
If you already run Postgres, pgvector is usually the right first pick. Extension, no new system, transactional data and embeddings in the same DB. Cost: effectively zero. Performance: comfortable up to ~1M vectors. IVFFlat + HNSW supported.
If you don't want to run a vector index and want an API call away, Pinecone makes sense. Multi-tenant serverless, metadata filtering, autoscale. Cost: scales up faster than you expected if sharding is wrong.
Weaviate plays a different game: native hybrid search (BM25 + vector + filter), GraphQL API, modules (reranker, generator). Self-host or managed. Stronger for document-centric use cases.
The typical mistake: jumping to Pinecone too early, then watching a 3-month bill kill the project. Start with pgvector and switch only when there's a concrete numeric reason.
There's no universal answer. There is a framework. Happy to look at your setup — 30 minutes is enough for a directional recommendation.

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Founder, DField Solutions
I've shipped production products from fintech to creator-tooling — for startups and enterprises, from Budapest to San Francisco.
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