Vector Database Comparison: Pinecone vs. pgvector vs. Weaviate for RAG

Pinecone is a fully managed vector database — no infrastructure to run, fast to set up, and built specifically for vector search at scale with features like metadata filtering and namespaces out of the box.

The trade-off is cost and vendor lock-in: Pinecone's pricing scales with index size and query volume, and at meaningful production scale it's typically the most expensive of the three. It's the right default when you want to ship a RAG POC (see our RAG POC guide) fast without standing up new infrastructure, or when your team has no appetite for operating a database.

pgvector is a PostgreSQL extension that adds vector similarity search to a database you may already be running. If your application data already lives in Postgres, pgvector lets you store embeddings alongside your relational data and query both together — no separate system to provision, secure, or back up.

The trade-off is that you own the ops: indexing strategy, scaling, and tuning are your responsibility, and very large-scale vector search (tens of millions of vectors with tight latency requirements) is an area where purpose-built vector databases still have an edge. For small-to-mid scale RAG — the range most production systems we cover in our LLM integration guide actually operate at — pgvector is often the most cost-effective and operationally simplest choice.

Weaviate is an open-source vector database that can be self-hosted or used as a managed cloud service, with built-in support for hybrid search (combining vector and keyword search), modules for various embedding models, and GraphQL-style querying.

It sits between Pinecone and pgvector operationally: more features and flexibility than pgvector, but more to configure and operate than Pinecone's fully managed offering, unless you use Weaviate Cloud. It's a strong fit for teams that want hybrid search out of the box or need to self-host for data residency reasons, without building that tooling themselves.

If you're already on Postgres and your scale is moderate (think hundreds of thousands to a few million vectors), start with pgvector — it removes a whole system from your infrastructure rather than adding one.

If you want to move fast on a POC with zero infrastructure setup and cost isn't the binding constraint yet, Pinecone gets you there fastest — and migrating off it later, once scale and cost justify it, is a well-understood path.

If you need hybrid search (keyword + semantic) as a first-class feature, or have data residency requirements that rule out a fully managed third-party service, Weaviate is worth the extra setup.

In all three cases, the vector database is rarely the bottleneck in RAG quality — chunking strategy and retrieval evaluation (covered in the RAG POC guide) matter more to answer quality than which of these three you pick.