AWS Vector Databases – Part 3 : Choosing the Right Vector Database on AWS

This is where everything comes together. 👉 In case you missed it: Part 1 → Embeddings, Dimensions, and Similarity Search Part 2 → Search Patterns, Filtering, and Chunking By now, you understand the fundamentals and how retrieval works. The real question is: 👉 Which one should you actually use? Since we’re in early 2026, the landscape has shifted a bit. With the maturity of S3 Vectors and the arrival of Graviton5-powered instances, high-scale vector search is now both cheaper and faster than ever. The 2026 TL;DR Just starting? → Aurora pgvector (if you like SQL) or OpenSearch Serverless On a budget? → S3 Vectors (massive cost savings at scale) Need speed? → MemoryDB or ElastiCache Valkey Don’t overthink it → most RAG systems are engine-agnostic 👉 Pick what matches your current stack and ship. Summary Comparison Service Model Strength Scaling Bedrock KB OpenSearch Serverless Distributed Full-text + semantic search Automatic (OCU) Yes Aurora pgvector Relational SQL + vector hybrid quer