Vector & retrieval
pgvector
Vector similarity search inside Postgres. Usually the right first answer if you already run Postgres.
- Category
- Vector & retrieval
- Pricing
- Open source
- Runs
- Self-hosted
- Interface
- Postgres extension
- Source
- Open source
What pgvector is
pgvector adds a vector column type and similarity search to Postgres. Because vectors live beside your relational data, filtering by tenant, permission, or date is an ordinary WHERE clause and joins work normally — no second system to sync, no consistency gap to reason about. It is supported by every managed Postgres worth using.
Best for
Anyone already running Postgres with up to a few million vectors, which is a larger share of real applications than the category implies.
Consider something else if
At very large scale, or when you need hybrid search and advanced index tuning out of the box, a purpose-built store pulls ahead.
pgvector alternatives
The closest options in vector & retrieval, on the axes that actually separate them.
| Tool | Best for | Pricing | Runs |
|---|---|---|---|
| pgvectoropen source | Anyone already running Postgres with up to a few million vectors, which is a larger share of real applications than the category implies. | Open source | Self-hosted |
| Qdrantopen source | Filter-heavy retrieval — multi-tenant, permissioned, or time-scoped — where naive filtering degrades results. | Open source | Hosted or self-hosted |
| Chromaopen source | Prototyping retrieval, notebooks, and small applications where a service is overkill. | Open source | Hosted or self-hosted |
| Weaviateopen source | Corpora containing identifiers, product codes, or proper nouns, where pure vector search visibly misses. | Open source | Hosted or self-hosted |
| Pinecone | Teams who want retrieval to be somebody else's operational problem and are content to pay for that. | Usage-based | Hosted |
| turbopuffer | Large corpora where only a fraction is queried regularly, and per-tenant indexes that are mostly idle. | Usage-based | Hosted |
Choosing within vector & retrieval
Whether Postgres is already enough
Ask this first and take the answer seriously. At small and medium scale the extension removes a service, a sync problem, and a second consistency model, at very little cost in capability.
Metadata filtering
Real queries are almost never pure similarity — they are similarity within a tenant, a date range, or a permission scope. How well a store combines filters with vector search is the difference that shows up in production.
Questions
What is pgvector?
pgvector adds a vector column type and similarity search to Postgres. Because vectors live beside your relational data, filtering by tenant, permission, or date is an ordinary WHERE clause and joins work normally — no second system to sync, no consistency gap to reason about. It is supported by every managed Postgres worth using. It is open source and self-hosted.
What are the alternatives to pgvector?
The closest alternatives are Qdrant, Chroma, Weaviate, Pinecone, turbopuffer. They sit in the same category — vector & retrieval — and differ mainly on hosting model, pricing shape, and how much they abstract away.
Is pgvector the right choice?
Anyone already running Postgres with up to a few million vectors, which is a larger share of real applications than the category implies. The main caveat: At very large scale, or when you need hybrid search and advanced index tuning out of the box, a purpose-built store pulls ahead.
Whatever you build on, the model is the line item that scales. See what each one costs per million tokens, or price your own workload.