Improve the performance of generative AI workloads on Amazon Aurora with Optimized Reads and pgvector

This article discusses how using Amazon Aurora Optimized Reads with pgvector can significantly improve the performance of generative AI workloads involving vector similarity search on large datasets that exceed the memory capacity of a database instance.

Specifically, the article covers:

• How Optimized Reads uses local NVMe SSD storage to cache data evicted from memory, reducing reliance on slower network storage and improving read latency and throughput
• Benchmark results showing Optimized Reads instances providing up to 9x higher query throughput and 75-80% lower cost per query compared to instances without Optimized Reads for a 1 billion vector dataset
• Details on the benchmark setup, instance configurations, and performance metrics
• How Optimized Reads allows scaling vector workloads further on the same instance size before upgrading
• Conclusion that Optimized Reads offers a high-performance, cost-effective solution for vector similarity search workloads exceeding instance memory