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Tracked since2026
0 reviews trackedThe 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?
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
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 Jul 10, 2026
Open Source
Free
- Full source code access
- Other license
- Community support
- Self-hosted
Reviews
$99Free with your review
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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