AWS is introducing Amazon S3 Vectors, a specialized storage offering that can cut the cost of uploading, storing, and querying vectors by up to 90% compared to using a vector database. This move is likely to be of interest to those running generative AI or agentic AI applications in the cloud. Machine learning models typically represent data as vectors, which are stored in specialty vector databases or databases with vector capabilities for similarity search and retrieval at scale. AWS proposes that enterprises use a new type of S3 bucket, Amazon S3 Vector, which eliminates the need for provisioning infrastructure for a vector database. AWS has integrated S3 Vectors with Amazon Bedrock Knowledge Bases, Amazon SageMaker Unified Studio, and Amazon OpenSearch Service, ensuring efficient use of resources even as datasets grow and evolve. The OpenSearch integration provides flexibility for enterprises to store rarely accessed vectors to save costs. Developers can dynamically shift these vectors to OpenSearch for real-time, low-latency search when needed.