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Choosing a vector database for RAG

Pinecone, Weaviate, Qdrant, Chroma, Zilliz, and MongoDB Atlas Vector Search all do "store embeddings, search them fast" — the real differences are self-hosting, ops burden, and how they fit your existing stack.

Crail Editorial · Published 2026-07-26 · Last verified 2026-07-27

Every vector database pitch sounds the same: fast approximate nearest-neighbor search over embeddings, at scale. The real decision is about deployment model, ops burden, and how well it fits into infrastructure you already run — not raw search quality, which is broadly comparable across the mainstream options today.

The deployment-model split

Open-source, self-hostable: Weaviate, Qdrant, Chroma, and Zilliz (managed Milvus, with Milvus itself fully open source) all let you run the database yourself — relevant if data residency, cost control at scale, or avoiding vendor lock-in matter to you. Crail tracks all four with a free tier and a real self-hosted deployment path.

Managed-only: Pinecone and MongoDB Atlas Vector Search don’t offer a self-hosted option — you’re on their infrastructure, full stop. That’s a reasonable tradeoff if you’d rather not operate the database at all, especially for MongoDB Atlas Vector Search if you’re already running MongoDB for your primary data store and want vectors alongside it rather than a separate system.

What to actually compare

  1. Does it fit your existing data stack? If you’re already on MongoDB, Atlas Vector Search avoids adding a new system entirely. If you’re greenfield, that constraint doesn’t apply and the field opens up.
  2. Hybrid search (vector + keyword + metadata filtering) support — RAG in practice almost always needs to filter by metadata alongside semantic similarity, not pure vector search in isolation.
  3. Self-hosting story, if you want one. Check not just “is it open source” but whether the self-hosted deployment is genuinely production-ready (clustering, backups, monitoring) or a single-node demo path.
  4. Agent-readiness, if you expect to wire this up programmatically rather than through a human-configured pipeline — see each vendor’s page on Crail for MCP support and API self-serve details.

Where to start

Compare current vendors, pricing, and agent-readiness scores on the Vector Databases & RAG Infrastructure category page — every listing includes whether it’s self-hostable, has a free tier, and publishes an MCP server.

FAQ

Do I need a dedicated vector database, or can I use my existing database's vector extension?

For a prototype or modest scale, an extension (e.g. Postgres pgvector) is often enough. Dedicated vector databases earn their keep at higher scale or when you need advanced filtering/hybrid search alongside vector similarity.

Is open source vs. managed the biggest decision here?

It's a major one — Weaviate, Qdrant, Chroma, and Zilliz (managed Milvus) all have genuine open-source self-hosted paths, while Pinecone and MongoDB Atlas Vector Search are managed-only. Self-hosting trades ops burden for cost control and data residency.