# Elasticsearch

> Distributed, RESTful search and analytics engine for full-text, vector, and hybrid search, observability, and security data.

Elasticsearch is a distributed search and analytics engine built by Elastic and built on Apache Lucene. It stores structured, unstructured, and vector data and exposes it over a RESTful HTTP API. It is available as Elastic Cloud Serverless, Elastic Cloud Hosted, or a self-managed download.

## What It Is

Elasticsearch is the core component of the Elastic Stack. It indexes and queries large volumes of data, supporting full-text search, vector search, hybrid search, and real-time analytics. Elastic describes it as a "search database for AI" that serves as a retrieval and context layer for AI agents, including knowledge base queries and memory retrieval. The same engine also powers Elastic Observability and Elastic Security.

## How the Search and AI Workflow Works

Data is ingested through APIs, language clients, ingest pipelines, Elastic Agent, Beats, Logstash, or built-in connectors. Users can then query with Query DSL or the piped ES|QL language, including lookup joins and geospatial functions. Semantic search uses dense and sparse vectors, with hybrid retrieval that combines keyword and vector results and reranking. Jina AI models, now part of Elastic, supply embeddings and rerankers, and Elastic Agent Builder is offered for building context-aware agents.

## Storage, Scale, and Deployment

The site describes hot-tier local storage and searchable snapshots on object storage, cross-cluster search for federated queries, and the same behavior from a laptop to large clusters. Self-managed installs run on Linux, macOS, Windows, Docker, and Kubernetes. The code is dual-licensed under AGPL v3 and the Elastic License 2.0, according to Elastic's FAQ.

## Where It Fits

Typical uses listed by Elastic include application and ecommerce search, RAG, log analytics, application and infrastructure monitoring, LLM observability, SIEM, and threat hunting. Clients are available for Java, Python, Go, Ruby, Rust, .NET, and PHP, and Elastic cites 350+ integrations.

## Features
- Full-text, semantic, and hybrid search with reranking
- Vector database for dense and sparse embeddings
- Real-time analytics engine
- ES|QL piped query language
- Geospatial search and analytics
- Searchable snapshots and data tiers
- Cross-cluster search
- Jina AI inference models
- Elastic Agent Builder
- Elastic Workflows
- Language clients and REST API
- Kibana visualization and dashboards

## Integrations
AWS, Google Cloud, Microsoft Azure, Kubernetes, Apache, OpenTelemetry, Kibana, Logstash, Beats, Elastic Agent, Jina AI

## Platforms
WEB, API, LINUX, MACOS, WINDOWS, DEVELOPER_SDK

## Pricing
Open Source

## Links
- Website: https://www.elastic.co/elasticsearch
- Documentation: https://www.elastic.co/docs
- Repository: https://github.com/elastic/elasticsearch
- EveryDev.ai: https://www.everydev.ai/tools/elasticsearch