Powering next gen
AI apps with Postgres

Build and scale transformative LLM applications with vector indexes and similarity search in Postgres

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Fast and Accurate

Scale your AI apps to millions of users with Neon

Fully open-source

Scales to10M+rows

Speed up your queries with HNSW

Query execution time (ms) at 99% recall

13.2ms

262.3ms

HNSWIVFFlat

HNSW indexes bring 20x the speed for 99% accuracy to graph-based approximate nearest neighbor search in your Postgres database.

20xFaster than IVFFlat

Uses HNSW

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Simple to use,
scales automatically

Store vector embeddings and perform similarity search

CREATE EXTENSION vector;
CREATE TABLE items (id BIGSERIAL PRIMARY KEY, embedding VECTOR(3));
INSERT INTO items (embedding) VALUES ('[1,2,3]'), ('[4,5,6]');
SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 1;

Store embeddings and perform vector similarity search in Postgres with pgvector.Learn moreabout pgvector

Benefits

Vector search with Neon

Use the power of HNSW indexes to unlock new levels of efficiency in high-dimensional vector similarity search in Postgres

Reliable & actively maintained
The pgvector extension is open-source and actively maintained
Amazing scalability
Grow your vector stores without impacting search performance
Blazingly fast search
Use HNSW indexes for fast and scalable vector similarity search in Postgres
Highly compatible
Use Neon with pgvector in your Postgres and LangChain projects