Open Source Search Engine - Amazon OpenSearch Service - AWS (original) (raw)

Securely unlock real-time search, monitoring, and analysis of business and operational data

Introduction to OpenSearch Service

Amazon OpenSearch Service securely unlocks real-time search, monitoring, and analysis of business and operational data for use cases like application monitoring, log analytics, observability, and website search.

Increase operational excellence

Increase operational excellence by using a popular open source solution, managed by AWS.

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Audit and secure your data

Audit and secure your data with a data center and network architecture with built-in certifications.

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Systematically detect potential threats

Systematically detect potential threats and react to a system’s state through machine learning, alerting, and visualization.

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Optimize time and resources

Optimize time and resources for strategic work.

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Improve search relevance

Improve search relevance, provide nearest neighbor search, and power generative AI applications.

Managed OpenSearch

Run search workloads simply without the hassle of configuring, managing, and scaling OpenSearch clusters. OpenSearch Service provides the power of open-source search with interactive millisecond response times. Additionally, Amazon OpenSearch Serverless enables search and log analytics without provisioning and adjusting resources, automatically scaling resources based on your application's needs.

Learn more about Amazon OpenSearch Serverless

Price performant and scalable vector search for generative AI

The Vector Engine for Amazon OpenSearch Serverless powers generative AI applications with scalable vector storage and search for ML-augmented experiences. You can query billions of embeddings combined with text keywords in milliseconds, connect to foundation models, update embeddings in near real-time, and elevate customer experiences with accurate responses based on business data.

Learn more about Vector Engine for OpenSearch Serverless

Fully managed data ingestion and transformation

Amazon OpenSearch Ingestion enables ingesting, transforming, and routing data at scale to OpenSearch domains and OpenSearch Serverless collections. OpenSearch Ingestion automatically scales to handle demanding workloads, reduces storage costs through deduplication and sampling, enforces data quality with built-in processors and schemas, protects sensitive data through redaction, and routes data based on compliance requirements.

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Comprehensive security for search and log analytics

Use robust security capabilities to audit and secure search and log analytics data, meeting requirements for authentication, authorization, encryption, audit trails, and regulatory compliance. OpenSearch Service offers granular access control, network isolation, secure programmatic access, compliance enablement across industry standards, and security analytics through log collection, normalization, and comparison.

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Integration with other AWS services

Simply integrate with other AWS services to deliver expanded search and analytics functionality. This includes seamless integration with Amazon DynamoDB and Amazon DocumentDB databases for advanced search on stored data, as well as frictionless search across Amazon Simple Storage Service (Amazon S3) to reduce the need for separate data indexing. The service further integrates with AWS model hosting services like Amazon Bedrock and Amazon SageMaker, enabling vector embeddings generation and storage, and AWS CloudTrail and AWS CloudWatch for enhanced cluster management and security auditing.

Download the solution brief

Visit our Features page for a full list of OpenSearch Service features and benefits.

Real-time log analytics and observability

Centralize and analyze security and observability data for real-time threat detection, incident management, and improved application health, enhancing system visibility and customer experiences.

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Boost user engagement and conversions with the OpenSearch Service fast, personalized search. Quickly deliver relevant data across applications, websites, and data lake catalogs for an enhanced user experience.

Efficient and price-performant vector search and management for ML- and AI-coded vectors for anomaly fraud detection and collaborative recommendations, as well as supporting search over different media types like image, audio, or video.

Retrieval Augmented Generation (RAG) for generative AI applications

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

Read the O'Reilly Media report on Large Language Models (LLMs) for semantic search and generative AI.

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