Amazon OpenSearch Service as a Vector Database

Seamlessly combine vector embeddings with text-based keyword queries in a single search request and use 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, eliminates the need for multiple systems, and accelerates time-to-market for AI-powered applications like vector-driven search (hybrid, semantic, multi-modal, conversational, and others), recommendation systems, and AI chatbots, and other modern generative AI applications.

Scale 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. You can choose between precise control with managed clusters or automatic resource optimization with serverless to efficiently scale your vector workloads without incurring unnecessary costs. Both options ensure fast query responses across all storage levels while leveraging intelligent data lifecycle management to optimize costs as your workloads grow. OpenSearch Service's intuitive console and APIs make it straightforward to deploy, manage, and scale your vector database, reducing operational complexity.

Add, update, or delete vector embeddings in real-time without re-indexing or impacting query performance. This capability ensures that AI models and search applications remain responsive to dynamic data changes, making it ideal for use cases like e-commerce personalization or anomaly detection where data evolves frequently.

Amazon OpenSearch Service integrates with AWS services and third-party AI platforms to support modern generative AI applications. zero-ETL integration with Amazon DynamoDB and Amazon DocumentDB enables you to enhance your generative AI applications with vector search across operational data without building complex pipelines. Native two-way integration with Amazon Bedrock streamlines generative AI workflows, allowing you to connect foundation models to your knowledge base for efficient embedding generation and retrieval-augmented generation (RAG) applications. OpenSearch Service is the AWS recommended vector database for Amazon Bedrock. Developers can harness the power of Amazon SageMaker for model training and deployment or connect effortlessly to Amazon Titan or third-party models like OpenAI, Cohere, DeepSeek, and others through pre-built connectors. This enables secure, efficient, and scalable development while maximizing the value of your existing data and infrastructure investments.

A fully managed service that manages OpenSearch delivering enterprise reliability while leveraging open-source innovation. The global open-source community actively contributes to and enhances OpenSearch (now part of the Linux Foundation), driving continuous advancement while the managed service eliminates infrastructure management overhead. This approach provides high availability (99.99% SLA), automated scaling, patching, and updates, plus the flexibility and vendor neutrality of Apache 2.0 licensed technology. The open-source community also helps guide the project's direction, ensuring continuous innovation that benefits all users.

Enhance search experiences by combining traditional keyword search with vector similarity for improved relevance. Support natural language understanding, multi-modal queries (text, images, audio), and hybrid search capabilities to deliver contextually relevant results across diverse content types.

Power personalized recommendations at scale, using vector similarity to match user preferences across billions of items, delivering relevant suggestions in near real time.

Build trustworthy AI chatbots, assistants, and applications by connecting foundation models to your business data, enabling accurate, context-aware responses and task execution. Eliminate hallucinations and improve accuracy through vector-based information retrieval while maintaining fast response times for both simple queries and complex interactions.

Identify patterns and anomalies at scale by comparing vector similarities across large datasets, enabling real-time detection of potential fraud, counterfeits, or suspicious activities.