Scaling PostgreSQL to 100M+ Vectors: Production Optimization Guide

Scaling PostgreSQL to 100M+ Vectors: Production Optimization Guide When your AI application needs to scale beyond prototype datasets, PostgreSQL's vector capabilities become crucial infrastructure. This guide documents production-tested optimizations that achieve enterprise-scale performance. Production achievement: 100 million vectors, 2-5ms query latency, 15,000 QPS sustained performance. This represents real operational success with PostgreSQL's vector extensions at scale. Let's break down exactly how this works. The 100M Vector Reality Check Here's what actually running AI at scale looks like. Not benchmarks on empty databases real production systems under load. System: AWS RDS r6g.8xlarge (32 vCPUs, 256GB RAM) Dataset: 100M documents, 1536-dimensional embeddings Storage: 4TB total (2TB docs + 2TB indexes) Query Performance: Vector search (k=10): 3.2ms average, 15,000 QPS Vector search (k=100): 12.1ms average, 4,000 QPS Hybrid search: 8.5ms average, 6,500 QPS Document insert: 1.8ms