Amazon S3 Vectors, the first cloud object storage with native support to store and query vectors, is now generally available. S3 Vectors delivers purpose-built, cost-optimized vector storage for AI agents, inference, Retrieval Augmented Generation (RAG), and semantic search at billion-vector scale. S3 Vectors is designed to provide the same elasticity, durability, and availability as Amazon S3 and reduces the total costs to upload, store, and query vectors by up to 90%. With general availability, you can store and query up to two billion vectors per index and elastically scale to 10,000 vector indexes per vector bucket. Infrequent queries continue to return results in under one second, with more frequent queries now resulting in latencies around 100 milliseconds or less. Your application can achieve write throughput of 1,000 vectors per second when streaming single-vector updates into your indexes, retrieve up to 100 search results per query, and store up to 50 metadata keys alongside each vector for fine-grained filtering in your queries.
With S3 Vectors you get a new bucket type—a vector bucket—that is optimized for durable, low-cost vector storage. Within vector buckets, you organize your vector data with vector indexes and get a dedicated set of APIs to store, access, and query vectors without provisioning any infrastructure. By default, S3 Vectors encrypts all vector data in a vector bucket with server-side encryption using S3-managed keys (SSE-S3) or optionally, you can use AWS Key Management Service (SSE-KMS) to set a default customer-managed key to encrypt all new vector indexes in the vector bucket. You can now also set a dedicated customer-managed key per vector index, helping you build scalable multi-tenant applications and meet regulatory and governance requirements. You can also tag vector buckets and indexes for attribute-based access control (ABAC) as well as to track and organize costs using AWS Billing and Cost Management.
S3 Vectors integrates with Amazon Bedrock Knowledge Bases to reduce the cost of using large vector datasets for RAG. When creating a Knowledge Base in Amazon Bedrock or Amazon SageMaker Unified Studio, you can choose an existing Amazon S3 vector index or create a new one using the Quick Create workflow. With Amazon OpenSearch Service, you can optimize costs for hybrid search workloads by configuring OpenSearch to automatically manage vector storage in S3.
S3 Vectors is now generally available in 14 AWS Regions, expanding from 5 Regions in preview. To learn more, visit the product page, S3 pricing page, documentation, and AWS News blog.
Categories: marketing:marchitecture/storage,marketing:marchitecture/artificial-intelligence,general:products/amazon-s3
Source: Amazon Web Services
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