Query billion-scale vectors with SQL: Integrating Amazon S3 Vectors and Aurora PostgreSQL

This article explains how to integrate Amazon S3 Vectors with Aurora PostgreSQL to query billion-scale vector embeddings using SQL, combining vector similarity search with relational data filtering.

• S3 Vectors provides cost-effective storage for massive embedding collections; Aurora PostgreSQL handles complex relational queries and metadata
• AWS Lambda serves as translation layer between Aurora and S3 Vectors APIs using native aws_lambda extension
• Query billion-vector datasets with sub-second cold performance and sub-100ms warm query latency
• Combine vector similarity results with SQL filters (price, stock, tenant) in single queries
• Security uses IAM role separation, VPC isolation, and no stored credentials
• Trade-off: eventual consistency instead of ACID guarantees; Lambda adds 100-500ms latency versus Aurora pgvector's single-digit milliseconds
• S3 Vectors costs $0.06/GB versus Aurora's $0.10/GB for storage; pay-per-query model favors infrequent use
• CloudFormation template provided for infrastructure deployment; complete source code available on GitHub

This integration enables production applications to scale vector search independently from transactional workloads while maintaining familiar SQL interfaces for complex queries combining embeddings and relational data.