vector engine for OpenSearch Serverless

Vector Engine for OpenSearch Serverless

Overview Amazon OpenSearch Service as a Vector Database allows you to build vector-driven search and enterprise AI applications with a scalable, secure, and high-performance vector database.

Features

• High-performance search across billions of vectors: Seamlessly combines vector embeddings with text-based keyword queries in a single search request. It utilizes advanced nearest neighbor algorithms like ANN (across HNSW and IVF) and exact k-NN vector search with auto-scaling to deliver low-latency similarity searches across billions of vectors. This reduces system complexity and accelerates time-to-market for AI-powered applications such as hybrid, semantic, multi-modal, conversational search, recommendation systems, AI chatbots, and other modern generative AI applications.
• Cost-optimized scalability, deployment flexibility, and ease-of-use: Scales to billions of high-dimensional vectors while optimizing storage costs with disk-based vector storage and intelligent data lifecycle management. OpenSearch Service simplifies vector database operations, offering an easy-to-use interface for both fully managed and serverless configurations. Users can choose between precise control with managed clusters or automatic resource optimization with serverless to efficiently scale vector workloads without unnecessary costs. Both options ensure fast query responses across all storage levels and leverage intelligent data lifecycle management for cost optimization. Its intuitive console and APIs facilitate straightforward deployment, management, and scaling.
• Real-time updates for enhanced responsiveness: Supports adding, updating, or deleting vector embeddings in real-time without re-indexing or impacting query performance. This ensures AI models and search applications remain responsive to dynamic data changes, making it suitable for use cases like e-commerce personalization or anomaly detection.
• Seamless integration with AWS services and third-party AI models: Integrates with various AWS services and third-party AI platforms to support modern generative AI applications. Features include zero-ETL integration with Amazon DynamoDB and Amazon DocumentDB for vector search across operational data, and native two-way integration with Amazon Bedrock for streamlining generative AI workflows (e.g., connecting foundation models to knowledge bases for embedding generation and RAG applications). It is the AWS recommended vector database for Amazon Bedrock. Developers can also leverage Amazon SageMaker for model training and deployment or connect to Amazon Titan or third-party models like OpenAI, Cohere, and DeepSeek via pre-built connectors.
• Fully managed and open source for increased reliability and innovation: Delivered as a fully managed service that ensures enterprise reliability while benefiting from open-source innovation.

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