3.1. Designing Vector Store Solutions
💡 First Principle: A vector store's job is to make "find semantically similar content" fast at scale — it trades exact lookup efficiency (what traditional databases optimize for) for approximate nearest-neighbor search across high-dimensional vector spaces. Every vector store design decision either optimizes or compromises that core function.
| Vector Store Option | Managed? | Scale | Hybrid Search | Best For |
|---|---|---|---|---|
| OpenSearch Serverless | Fully | Auto-scale | ✅ | Production RAG, large corpora |
| Aurora pgvector | Managed | Instance-bound | ✅ (SQL) | Existing RDS workloads |
| Bedrock Knowledge Bases | Abstracted | Auto | ⚠️ Limited | Rapid RAG prototyping |
| Redis/ElastiCache | Managed | Memory-bound | ❌ | Semantic caching, small corpora |
The stakes of getting vector store design wrong are significant: under-provisioned indexes become retrieval bottlenecks as your corpus grows; incorrect metadata schema makes filtering impossible without full scans; wrong choice of vector store backend locks you into a service with the wrong throughput/cost profile for your access pattern.
⚠️ Common Misconception: Bedrock Knowledge Bases and a custom OpenSearch vector store are equivalent architectures. Bedrock Knowledge Bases is a fully managed, end-to-end RAG pipeline — it handles ingestion, chunking, embedding, storage, and retrieval as a single managed service. Custom OpenSearch gives per-component control but requires you to build and operate every piece.
