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Reviewed · Updated 2026-06-15

Pgvector

Vector database for storing embeddings and performing high-speed similarity searches.

Reviewed by the Conversion Gems editorial team ·
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Pricing
Paid
Best for
Developers
Category
AI Development
The bottom line

The default vector-search choice if you already use Postgres; it removes a whole piece of infrastructure for most RAG workloads.

8.3
Our score
8.3 / 10
Conversion Gems editorial verdict
Free (open-source extension)
Features8/10
8 - many distance metrics, exact and approximate search, HNSW/IVFFlat indexes and vector types.
Value10/10
10 - free and removes a whole piece of infrastructure.
Ease of use7/10
7 - easy for Postgres users, though index tuning takes thought.
Ecosystem9/10
9 - huge: managed providers, SDKs and framework support.
Support6/10
6 - open-source community plus the broad Postgres ecosystem.
What it really is

An open-source PostgreSQL extension that adds a vector type and similarity search - turning the Postgres you already run into a vector database.

Our take

It is the pragmatic choice for vector search: keep embeddings beside your relational data, with one connection string, one backup strategy, and SQL joins/filters/transactions instead of a separate vector DB. The trade-off is that it is an extension, not a managed service - you generate embeddings elsewhere and tune indexes yourself - and at extreme scale dedicated vector databases can pull ahead.

Best for
Developers who already run Postgres and want vector search
RAG systems wanting embeddings beside relational data
Teams avoiding the cost and ops of a separate vector database
Not good for
Teams wanting a fully managed, vector-first database with SLAs
Extreme-scale workloads where specialized vector DBs perform better
Non-developers - it is a database extension, not an app
Friction report
Time to value
Fast for Postgres users: 'CREATE EXTENSION vector', add a vector column and index; it is preinstalled on many managed Postgres providers.
Scale breakpoint
It does not create embeddings (you bring a model), and at very large scale HNSW build times, memory and recall tuning matter - some reach for pgvectorscale or a dedicated DB.
Walled garden
No lock-in - open-source, runs in standard Postgres, and widely supported by hosted providers.

Frequently Asked Questions

Alternatives

Step up

Pinecone or Qdrant - managed, vector-first databases for elastic scale and SLAs.

Lighter alternative

An in-memory library like FAISS - simple for small, local similarity search without a database.

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Tags

#VectorDatabase#RAG#SemanticSearch

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