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pgvector

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Vector similarity search for PostgreSQL

Free, no paid tier
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Tracked since 2026

The Bottom Line

Price
Free, no paid tier. Compare plans

Key facts

  • pgvector is an open-source PostgreSQL extension for vector similarity search, allowing users to store and query vectors directly within their database.
  • It supports various distance metrics (L2, inner product, cosine, L1, Hamming, Jaccard) and vector types (single-precision, half-precision, binary, sparse).
  • pgvector offers both exact and approximate nearest neighbor search, with approximate search enabled by HNSW and IVFFlat indexes for improved performance.

Pros

  • Native PostgreSQL
  • No separate service
  • Open source

Cons

  • PostgreSQL only
  • Limited compared to specialized DBs

What is pgvector?

pgvector is an open-source PostgreSQL extension for vector similarity search. Enables storing embeddings and performing similarity queries directly in your PostgreSQL database.

Available on: Linux, macOS, Windows

how we evaluate Source github.com ↗

Preview

Key Features

  • Vector storage
  • Similarity search
  • PostgreSQL native
  • Multiple indexes
  • Exact and approximate search
  • Open source

Pricing Plans

Pricing checked Oct 11, 2026

pgvector plans and prices, checked October 2026
PlanPriceDetails
Open Source

Free

  • Full source code access
  • Other license
  • Community support
+1 more
  • Self-hosted
View full pricing

Is pgvector worth the price?

Great value

pgvector's pricing is exceptionally generous, being entirely free and open-source.

This makes it an incredibly fair and accessible option for anyone needing vector similarity search. It's best for developers and organizations looking for a cost-effective, self-hosted solution with full control.

Full pgvector pricing: every plan, hidden costs and alternatives

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Still deciding?

Most buyers shortlist 2 or 3 tools before committing. Pull a side-by-side comparison or browse the full alternatives shortlist below.

All pgvector alternatives 7+ tools ranked, pricing + verdict per pick pgvector vs Elasticsearch Head-to-head: features, pricing, who wins pgvector vs Milvus Head-to-head: features, pricing, who wins pgvector vs Pinecone Head-to-head: features, pricing, who wins

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pgvector FAQ

How does pgvector enable vector similarity search?

pgvector is an open-source PostgreSQL extension that allows users to store embeddings and perform similarity queries directly within their existing PostgreSQL database. This integration means that vector operations can be managed alongside other data within the same database system.

Which teams would benefit most from using pgvector?

Teams already utilizing PostgreSQL for their data management will find pgvector particularly beneficial. It is ideal for developers who need to integrate vector similarity search capabilities without introducing a separate database service.

How is pgvector priced?

pgvector is free to use because it is an open-source PostgreSQL extension. There are no paid plans or subscription fees required to utilize its functionalities.

What kind of limitations does pgvector have compared to other vector databases?

pgvector is limited to PostgreSQL environments, meaning it cannot be used with other database systems. Its capabilities are also more constrained compared to specialized vector databases that offer a broader range of advanced features.

Can pgvector be used for building recommendation engines?

Yes, pgvector can be used to build recommendation engines by storing user or item embeddings and performing similarity searches. This allows for identifying items or users with similar characteristics directly within a PostgreSQL database.

How does pgvector compare to Milvus?

pgvector integrates vector similarity search directly into PostgreSQL, offering a native experience without a separate service. In contrast, Milvus is a specialized vector database designed independently of traditional relational databases.

Does pgvector require a separate service to function?

No, pgvector does not require a separate service because it operates as an extension within PostgreSQL. This allows for managing vector data and performing similarity searches directly within the existing PostgreSQL database instance.

Source: github.com

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