Vector Databases Compared in 2026: Which to Choose
Vector databases compared in 2026: pgvector, Pinecone, Weaviate, Qdrant, Milvus. Features, costs, and how to choose for RAG and semantic search.
A vector database stores embeddings — the numerical representations of text, images, and audio — and retrieves similar ones by distance. It is the storage layer for RAG, semantic search, and recommendation systems.
How vector databases work
- Convert data into embeddings with a model.
- Store vectors with metadata (source, timestamp, filters).
- At query time, embed the query and find nearest neighbors.
- Return matches plus metadata for grounding or display.
Main options
- Postgres + pgvector: add vectors to your existing DB — no new infra.
- Managed SaaS (Pinecone, Qdrant Cloud): fast setup, pay-as-you-go.
- Self-hosted (Weaviate, Milvus, Qdrant): control and cost optimization.
- Embedded (LanceDB, sqlite-vec): zero-ops for local or edge apps.
How to choose
- Already on Postgres with small scale? Start with pgvector.
- Need scale and managed ops? Use a SaaS provider.
- Privacy or data-locality? Self-host.
- Prototype or on-device? Use embedded options.
Hidden costs
- Embedding generation costs for every item and query.
- Index size and memory for HNSW graphs.
- Ops time for self-hosted clusters.
- API call costs for managed providers at high QPS.
Benchmark before you commit
FAQ
Do I need a vector database for RAG?+
Not always. Small corpora can use in-memory similarity or Postgres + pgvector. A dedicated vector DB pays off at scale and high query volume.
Is Postgres pgvector good enough?+
For many apps, yes. It avoids extra infrastructure. Dedicated vector DBs win on scale, hybrid search, and advanced indexing.
How much does a vector database cost?+
Managed SaaS charges by volume and query. Self-hosted costs are infra plus ops. Embedding generation is often the overlooked line item.
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