Amazon OpenSearch Service - Managed Retrieval Engine

Build on a unified search foundation that combines lexical, vector, and hybrid retrieval in a single system for more relevant results — with indexing strategies like HNSW and IVF, and with vector quantization to balance accuracy, latency, and cost at scale. ML-powered auto-optimization and Serverless GPU reduce manual tuning and accelerate large-scale vector indexing, while multi-vector and automatic semantic enrichment improve recall without manual effort. Agentic search orchestrates multi-step retrieval workflows that adapt dynamically to queries. Explainable scoring to understand ranking decisions and fine-tune relevance with precision. For observability, analyze logs, traces, and metrics through unified dashboards with direct query capabilities for Amazon S3, Amazon CloudWatch, and Amazon Security Lake. Eliminate data movement and reduce storage costs while built-in machine learning detects anomalies and automates alerting for faster resolution.

Focus on building, not managing infrastructure. Amazon OpenSearch Service handles backups, patching, monitoring, and cluster maintenance, while Cluster Insights surfaces issues with prescriptive recommendations. OpenSearch Serverless eliminates capacity planning with automatic scaling that adapts to demand without downtime.

Ingest data in real time or batch through Amazon Kinesis, AWS Glue, or Zero-ETL integrations with Amazon S3 and Amazon DynamoDB, and enrich it with built-in ingestion pipelines. Model connectors, MCP server support, and integrations with Amazon Bedrock and Amazon SageMaker let you plug in the right AI model through configuration, not custom code. And with OpenSearch Agent Skills, you can build search applications, investigate logs, and migrate to OpenSearch Service right from your favorite agentic IDE, including Kiro, Claude, and Cursor.  

Build on OpenSearch, an open-source, community-driven project at the Linux Foundation with 1.7+ billion downloads and contributions from 3,000+ contributors across 500+ organizations.  Benefit from neutral governance and long-term sustainability with no single-vendor dependency. Start locally, then seamlessly transition to Amazon OpenSearch Service for production-scale deployment with built-in scalability, availability, and operational simplicity. Deploy and run across environments with full control of your data and architecture, backed by the Apache License, Version 2.0 for long-term access and extensibility. 

Run workloads efficiently with intelligent tiering that manages data across storage tiers based on access patterns, and memory- or disk-optimized vector search configurations that let you balance performance and cost per workload. Secure multi-tenant applications with role-based access control (RBAC) for fine-grained permissions, and index-level encryption for tenant data isolation. Achieve 99.99% availability with Multi-AZ with Standby deployments, automatic failover, and built-in redundancy for business continuity.

Enhance the accuracy and relevance of responses from large language models (LLMs) by incorporating Retrieval Augmented Generation (RAG) with Amazon OpenSearch Service as a knowledge base.

Store and search high-dimensional vectors for semantic and multimodal search across text, image, audio, and video data at any scale. Integrate with foundation models on Amazon Bedrock, Amazon SageMaker, and third-party providers to power intelligent search, chatbots, personalized recommendations, and AI-assisted analytics.

Deliver relevant, context-aware search results tailored to user intent and behavior. Combine lexical, vector, and hybrid retrieval with ML-powered relevance tuning to create search experiences that improve over time across e-commerce, media, and enterprise applications.

Centralize and analyze security and observability data for real-time threat detection, incident management, and improved application health. Gain unified visibility across logs, traces, and metrics to resolve issues faster and enhance system reliability.