AWS vector solutions: Build agentic AI where your data lives

This article explains AWS vector solutions for building agentic AI applications, emphasizing adding vector search to existing data stores rather than migrating data to new services.

• Vectors represent data as high-dimensional embeddings enabling semantic search, RAG, and knowledge graphs for AI agents
• Amazon OpenSearch Service is the default for new workloads, combining lexical, vector, and hybrid search with high throughput and low latency
• Amazon S3 Vectors provides cost-optimized storage for billion-scale vector indexes with pay-per-query pricing, reducing costs up to 90%
• Amazon DynamoDB delivers single-digit millisecond vector search at any scale with serverless management and support for trillions of vectors
• Amazon ElastiCache for Valkey enables microsecond latency semantic caching and real-time recommendation engines
• Amazon Aurora PostgreSQL combines SQL-native vector search with relational queries for structured data workloads
• Amazon Neptune uniquely combines graph traversal with vector similarity for GraphRAG and multi-hop reasoning
• Decision model helps select the right vector engine based on latency, cost, and access pattern requirements

AWS vector solutions enable organizations to build intelligent agentic AI applications by leveraging existing data infrastructure without data migration or managing separate vector stores.