Best Vector Database for RAG
Store and query embeddings for semantic search and RAG. Ranked by which tools AI agents actually pick when developers build — measured live across Claude, GPT, Gemini.
As of Jun 24, 2026, the vector database AI agents pick most is Pinecone at 30%, measured across Claude, GPT, Gemini.
| # | Tool | Pick Rate | Default Rate | Shortlist |
|---|---|---|---|---|
| 1 | Pinecone | 30% [24%–37%] | 0% | 74% |
| 2 | pgvector | 13% [9%–18%] | 21% | 37% |
| 3 | Chroma | 1% [0%–3%] | 2% | 24% |
| 4 | Weaviate | 0% [0%–2%] | 2% | 47% |
| 5 | Qdrant | 0% [0%–2%] | 0% | 40% |
What is a vector database?
A vector database stores embeddings — numeric representations of text, images, or other data — and finds the nearest ones to a query for semantic search and retrieval-augmented generation (RAG). Options range from fully managed services to open-source engines you self-host, plus extensions that add vector search to a database you already run.
Vector search is the retrieval layer under most AI apps, so latency, recall, metadata filtering, and cost directly shape product quality. The big early decision is whether to add a new service at all or extend the Postgres you already have — and once embeddings and pipelines are built around one store, moving is real work.
How to choose
What separates the Vector Database options.
Managed services remove ops; self-hosted engines give control and can cut cost at scale.
pgvector adds vector search to Postgres you already run; dedicated stores specialize for scale and features.
Metadata filtering and hybrid (keyword + vector) search matter for real retrieval quality.
Index size, query latency, and pricing model diverge sharply between options.
Best vector database for your use case
| If you need… | Reach for | Why |
|---|---|---|
| Already on Postgres, keep it simple | pgvector | Adds vector search to your existing Postgres — no new service. |
| Fully managed and serverless | Pinecone | Hands-off, scalable managed vector search. |
| Open-source with hybrid search | Weaviate / Qdrant | Self-hostable engines with strong filtering and hybrid search. |
| Local dev and prototyping | Chroma | Lightweight and easy to run locally for early RAG work. |
Vector Database: incumbents vs new entrants
The space contrasts managed, serverless vector services with open-source engines you self-host — and the 'just use Postgres' option that keeps everything in one database.
The managed, serverless vector database many reach for first.
The Postgres extension that adds vector search without a new service — a popular default.
Open-source vector engine with hybrid search and modules.
Open-source, performance-focused vector database with rich filtering.
Lightweight, developer-friendly store popular for local RAG and prototyping.
Why AI agents decide this category
Ask an agent to build RAG and it has to choose where the embeddings live. The open-ended cut is revealing here: agents often reach for a managed name by reflex, but when asked the broader question, the 'just use Postgres with pgvector' answer competes hard. Whoever the agent defaults to gets built into the retrieval layer of the next AI app.
Frequently asked questions
What is a vector database?
A vector database stores embeddings — numeric representations of data — and retrieves the nearest ones to a query, powering semantic search and retrieval-augmented generation (RAG). Some are managed services, some open-source, and some are extensions to an existing database.
What is the best vector database for RAG?
Pinecone is the go-to managed option; pgvector is the simplest if you're already on Postgres; Weaviate and Qdrant lead the open-source, self-hosted side. The ranking above shows which AI agents pick.
pgvector vs Pinecone — which should I use?
pgvector adds vector search to a Postgres database you already run, keeping everything in one place; Pinecone is a fully managed, serverless vector service. Choose by whether you want zero new infrastructure or a hands-off managed store.
Do I need a dedicated vector database?
Not always — if you already run Postgres, pgvector handles many workloads. Dedicated stores like Pinecone, Weaviate, or Qdrant pay off at larger scale or when you need advanced filtering and hybrid search.
Is there an open-source vector database?
Yes — Weaviate, Qdrant, and Chroma are open-source and self-hostable, and pgvector is an open-source Postgres extension.