Every RAG pipeline needs somewhere to store and search embeddings — the question is whether that somewhere is a dedicated managed service, an extension on the Postgres you already run, or a self-hosted engine you control end to end. This page compares the two most common starting points, Pinecone and pgvector, head to head. Weaviate is the third real option: a self-hosted (or managed) engine built specifically for vector and hybrid search, worth considering when you need built-in hybrid (keyword + vector) search or want to avoid both Pinecone's per-query pricing and Postgres's scaling ceiling.
If you already run Postgres and your vector volume is modest, start with pgvector — it's effectively free, keeps your data in one place, and is good enough for most early-stage RAG pipelines. Move to Pinecone once scale or query load outgrows what Postgres can comfortably handle and you'd rather pay for a managed service than tune indexes yourself. Consider Weaviate instead of either when you specifically need self-hosted control over your data (for compliance or cost reasons at scale) or built-in hybrid search that combines keyword and vector relevance out of the box — it's a heavier lift to operate than pgvector, but a more purpose-built alternative to Pinecone if you don't want a third-party managed dependency.
For small-to-mid scale RAG pipelines, pgvector is genuinely good enough for production — many teams run it for years without hitting its limits. Once query volume or embedding count grows large enough that index performance becomes the bottleneck, a dedicated engine like Pinecone or Weaviate starts to pay for itself.
pgvector has no separate cost beyond your existing Postgres instance, since it's an extension. Pinecone is a usage-based managed service billed on index size and query volume, so it adds a dedicated line item — one that scales with your traffic, which is the tradeoff for not managing infrastructure yourself.
Weaviate is worth considering when you need hybrid search (keyword plus vector relevance) built in, or when you want a purpose-built vector engine that you self-host rather than depend on a third-party managed service. It takes more operational effort than pgvector but is more specialized for vector workloads than adapting Postgres.
Yes. Since both store embeddings with metadata, migrating means re-indexing your vectors into Pinecone and pointing your retrieval layer at the new service. It's straightforward technically — the harder part is usually deciding when the migration is worth the operational shift, which is exactly the kind of call we help teams make.
Tell us your scale and stack and we'll recommend Pinecone, pgvector, or Weaviate in a 30-minute call.