Which vector database should I use for RAG?
Below a few million vectors, the extension in the database you already run — usually pgvector — is the right answer. A dedicated vector database earns its operational cost at scale, not at the start.
Surveyed 17 September 2026
Add vector search
Open in Tools →- 01PostgreSQL
Open-source SQL database with JSON support, full-text search, and extensions such as PostGIS and pgvector.
Developer Tools - 02Supabase
Open-source Firebase alternative combining a Postgres database with auth, realtime, storage, and edge functions.
Developer Tools - 03Pinecone
Serverless managed vector database for storing and querying embeddings at scale, common as a RAG retrieval layer.
Developer Tools - 04Neon
Postgres run serverlessly, where databases branch, scale themselves, and provision instantly.
Developer Tools - 05Redis
In-memory key-value store used for caching, queues, session storage, and real-time data workloads.
Developer Tools - 06MongoDB
Document database with flexible JSON schemas, offered as a managed cloud service through Atlas.
Developer Tools
A curated selection in editorial order. Use the fit and evidence to judge it for your task. Something missing?
What to look for
- 01Do you actually need a separate service? One less system to operate is worth a lot below a few million vectors.
- 02Can you filter by metadata and vector in one query? Splitting them wrecks both recall and latency.
- 03Does it support hybrid search? Pure semantic retrieval misses exact identifiers, names, and error codes.
Common questions
- Is pgvector good enough for production RAG?
- For most applications, yes. With an HNSW index it handles millions of vectors at low latency, and keeping embeddings beside your relational data makes filtered queries straightforward.
- Why does my RAG miss obvious results?
- Usually chunking or pure-semantic retrieval. Chunks that split a fact across boundaries never match well, and embeddings alone are poor at exact terms — add keyword search alongside.
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