# Scale pgvector with binary quantization on Amazon Aurora PostgreSQL

DevFeed: [Scale pgvector with binary quantization on Amazon Aurora PostgreSQL](<https://devfeed.tech/articles/scale-pgvector-with-binary-quantization-on-amazon-aurora-postgresql-4710.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/database/scale-pgvector-with-binary-quantization-on-amazon-aurora-postgresql/>)

Author: Steve Dille

Published: 2026-08-18T16:37:22Z

Content type: tutorial

Language: en

Sources: [AWS Database Blog](<https://devfeed.tech/sources/aws-database-blog.md>)

Topics: [Amazon Aurora](<https://devfeed.tech/topics/amazon-aurora.md>), [PostgreSQL](<https://devfeed.tech/topics/postgresql.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [configuration](<https://devfeed.tech/topics/configuration.md>), [postgresql clusters](<https://devfeed.tech/topics/postgresql-clusters.md>), [Database](<https://devfeed.tech/topics/database.md>)

Tags: [advanced-300](<https://devfeed.tech/tags/advanced-300.md>), [ai](<https://devfeed.tech/tags/ai.md>), [amazon-aurora](<https://devfeed.tech/tags/amazon-aurora.md>), [cache](<https://devfeed.tech/tags/cache.md>), [compression](<https://devfeed.tech/tags/compression.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [latency](<https://devfeed.tech/tags/latency.md>), [postgresql](<https://devfeed.tech/tags/postgresql.md>), [postgresql-compatible](<https://devfeed.tech/tags/postgresql-compatible.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [rds-for-postgresql](<https://devfeed.tech/tags/rds-for-postgresql.md>), [search](<https://devfeed.tech/tags/search.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>), [validation](<https://devfeed.tech/tags/validation.md>), [vector](<https://devfeed.tech/tags/vector.md>)

## AI overview

This practical guide explains how to use binary quantization with reranking in pgvector to scale HNSW vector search on Amazon Aurora PostgreSQL. It covers index-size reduction, performance and recall tradeoffs, sizing, validation, and suitable operating conditions for datasets ranging from 5 million to 100 million vectors.

## Source excerpt

Learn how to use binary quantization with reranking (HNSW+BQ) in pgvector to scale vector search to hundreds of millions or billions of vectors on Amazon Aurora PostgreSQL, with practical guidance on index sizing, recall validation, and the scenarios where the approach works best.