AWS vector solutions: Build agentic AI where your data lives
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AWS introduced vector solutions for agentic AI, emphasizing that vector search should be added to existing data stores rather than requiring new infrastructure. The article outlines use cases like RAG, semantic/hybrid search, GraphRAG, recommendations, anomaly detection, and multimodal discovery. It presents a decision model: Amazon OpenSearch Service is the default for new workloads balancing search, scale, and integration; Amazon S3 Vectors offers cost-efficient storage for billion-scale indexes; DynamoDB provides single-digit millisecond vector search at any scale. Other supported services include Aurora PostgreSQL, ElastiCache for Valkey, Neptune, and OpenSearch Serverless, which autoscales faster and offers cost savings. Customer examples include Adobe scaling Acrobat AI Assistant, BMW using S3 Vectors for natural-language queries over 20 petabytes, and Globant using DynamoDB vector search. The overall message is that AWS lets customers build agentic AI where their data already lives, avoiding data migration and duplication.
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