pgvector

Name: pgvector
Brand: pgvector

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Vector similarity search for PostgreSQL

Data & Databases Vector Databases Developer Tools

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Tracked since2026

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The Bottom Line

Entry price

Free, no paid tier

Biggest pro

Native PostgreSQL

Biggest con

PostgreSQL only

TL;DR - pgvector

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.

Pricing: Free forever

Best for: Individuals & startups

What is pgvector?

Editorial review

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

LCLouis CorneloupUpdated May 26, 2026 · how we evaluateSourcegithub.com ↗

Pros & Cons

Pros

Native PostgreSQL
No separate service
Open source

Cons

PostgreSQL only
Limited compared to specialized DBs

Preview

Key Features

Vector storageSimilarity searchPostgreSQL nativeMultiple indexesExact and approximate searchOpen source

Pricing Plans

Pricing checked Aug 19, 2026

Open Source

Free

Full source code access
Other license
Community support
Self-hosted

Calculate your cost View full pricing

Is pgvector worth the price?

100/100

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.

Hidden Costs & Gotchas

Infrastructure costs for hosting PostgreSQL

Operational overhead for maintenance

No dedicated vendor support

Scaling costs for large datasets

How pgvector Compares to Competitors

Unlike managed vector databases like Pinecone or Weaviate, which typically start with free tiers but quickly scale to hundreds or thousands of dollars per month based on usage, pgvector is fundamentally free. This makes it a significantly more cost-effective choice for those willing to manage their own infrastructure, as competitors often charge for vector storage, queries, and indexing.

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Best pgvector Alternatives

Top alternatives based on features, pricing, and user needs.

View full list →

ElasticsearchFreemium

Distributed search and analytics

4.5

PineconeFreemium

Managed vector database for semantic search and RAG

4.6

OpenSearchFreemium

Open-source search and analytics suite

4.4

WeaviateFreemium

Open-source vector database with ML

4.4

MilvusFree

Open-source vector database for AI

4.7

QdrantFreemium

Vector database for similarity search

4.5

ChromaPaid

Open-source vector database for AI applications

4.2

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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.

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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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