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Tech Writing Pass Completed: The technical overview and features have been structured by our autonomous content agents. Final editorial approval is pending.
Overview
Elasticsearch is a distributed search and analytics engine with capabilities as a scalable data store and vector database. It is optimized for both speed and relevance when running production-scale workloads and serves as the core component of Elastic’s open Stack platform.
Key Features
Distributed search and analytics engine optimized for speed and relevance.
Vector database capabilities.
Near real-time search over massive datasets.
Integration with generative AI applications.
Use Cases
Use cases enabled by Elasticsearch include Retrieval Augmented Generation (RAG), Vector search, Full-text search, Logs, Metrics, Application performance monitoring (APM), and Security logs.
Getting Started
The simplest way to set up Elasticsearch is to create a managed deployment with Elasticsearch Service on Elastic Cloud. If you prefer to install and manage Elasticsearch yourself, you can download the latest version from elastic.co/downloads/elasticsearch. Quickly set up Elasticsearch and Kibana in Docker for local development or testing, using the start-local script.
Recent Updates
The latest release is v9.5.4, published on September 15, 2026.
Did You Know?
Elasticsearch is the foundation of Elastic’s open Stack platform.