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: Summary Comparison Service Model Strength Scaling Bedrock KB OpenSearch Serverless Distributed Full-text + semantic search Automatic (OCU) Yes Aurora pgvector Relational SQL + vector hybrid queries Serverless v2 Yes S3 Vectors Object storage Massive scale, lowest cost Fully serverless Yes MemoryDB In-memory Ultra-low latency Cluster-based No Valkey (ElastiCache) In-memory Semantic caching Cluster-based No Neptune Analytics Graph Relationships + vectors Automatic Yes DocumentDB Document JSON + vector workloads Instance-based Yes Kendra Managed Enterprise search Fully managed N/A Decision Matrix Scenario Recommended Alternative General-purpose RAG OpenSearch Serverless Aurora pgvector Existing PostgreSQL app Aurora pgvector DocumentDB