AWS vector search lets you build agentic AI without moving your data
Add vector search to databases you already use—no migration, no new service.
AWS is doubling down on vector search as the retrieval foundation for agentic AI—systems that plan, reason, and take multi-step actions. Rather than forcing you into a new database, AWS's first principle is simple: add vector capabilities where your data already lives. That means existing Amazon databases, object stores, and search engines can now handle semantic search, RAG (retrieval-augmented generation), hybrid search (combining lexical and meaning-based matching), and GraphRAG (pairing vectors with knowledge graphs for multi-step reasoning). No data migration, no extra sync hops, and no new programming tools to learn—just your current operational data stores now serving vector workloads.
For net-new workloads with no existing data store, AWS offers a decision model spanning six purpose-built vector solutions. The benefits are practical: keeping vectors co-located with source data reduces cross-service latency and lets applications run faster. Use cases include real-time recommendation systems, anomaly and fraud detection on high-dimensional data, and multimodal content discovery across text, images, audio, and video. By grounding frontier models in trusted, up-to-date organizational knowledge, AWS aims to cut hallucinations and deliver context-aware, personalized AI experiences—without the heavy lift of standing up a separate vector database and syncing data to it.
- Add vector search to existing AWS data stores—no migration or data duplication required.
- Supports RAG, semantic search, hybrid search, and GraphRAG for accurate, context-rich agentic AI.
- Six purpose-built vector solutions cover net-new workloads like recommendations, fraud detection, and multimodal search.
Why It Matters
Enterprise teams can build grounded, agentic AI faster by reusing existing AWS data infrastructure, cutting migration costs and latency.