Reduce the cost of uploading, storing, and querying vectors by up to 90% while maintaining sub-second query performance. Transform your economics of storing millions to billions of vectors by moving away from costly storage options and paying only for what you use. Efficiently scale massive amounts of vectors without infrastructure management, organizing data using vector indexes that accommodate evolving workloads with zero provisioning. Designed for vector-driven AI use cases, S3 Vectors offers a practical balance of performance and efficiency.
Generate fine-grained vector embeddings to gain deeper understanding from unstructured data including images, videos, audio, and text. Elastically scale for vector search applications to improve granularity based on semantic similarity. Whether analyzing news content, indexing sports highlights, or working with medical images and genomic data, S3 Vectors supports high-volume workloads with consistent query performance and flexible scaling.
Use S3 Vectors for large, long-term vector data that doesn't require the high-throughput performance of in-memory vector databases. While Amazon OpenSearch Service delivers the high-QPS (query per second), low-latency vector search needed for real-time applications, S3 Vectors complements this by providing a cost-optimized data foundation with query performance optimized for long-term storage and infrequent access of data. You also benefit from a storage architecture with strong consistency guarantees, ensuring subsequent queries always include your most recently added data.
Leverage built-in connectivity with Amazon OpenSearch Service for vector search at optimized cost-performance and Amazon Bedrock Knowledge Bases for enhanced RAG applications at reduced costs. Access Amazon Bedrock within Amazon SageMaker Unified Studio to build inference-driven applications using existing project profiles, creating an integrated, scalable, and shareable AI development environment for enhanced team collaboration.