AWS DynamoDB Vector Search vs S3 Vectors: Two Tools for Different AI Workloads

AWS recently launched DynamoDB Vector Search, its latest addition to a growing list of at least seven services with vector storage capabilities, prompting questions about overlap with S3 Vectors, which became generally available earlier this year. The key distinction lies not in the services themselves but in the type of data they are designed to handle. DynamoDB Vector Search is built for operational data — such as user profiles, product catalogs, and fraud detection signals — that changes frequently, requires low-latency access, and already lives within transactional databases. S3 Vectors, by contrast, targets knowledge assets like PDFs, documentation, and support articles used in retrieval-augmented generation (RAG) pipelines, with AWS claiming up to 90% cost savings over specialized vector databases for such workloads. Rather than competing, the two services address fundamentally different use cases within the broader AI application stack.