Full-text, vector, and hybrid search
Combine full-text queries, filters, scoring, dense or sparse vectors, semantic retrieval, and hybrid search to retrieve results using both terms and meaning.
Elasticsearch
Freemium
Elasticsearch is a distributed, RESTful search and analytics engine for storing, retrieving, and analyzing structured, unstructured, time-series, geospatial, and vector data. It supports search applications, observability, security analytics, and AI retrieval workflows across managed cloud and self-managed deployments.
What is Elasticsearch?
Elasticsearch is a distributed, RESTful search and analytics engine for storing and retrieving structured, unstructured, time-series, log, event, geospatial, and vector data. It combines real-time search with analytics and retrieval capabilities for search applications, observability, security analytics, and AI workflows.
The platform supports full-text, filtered, fuzzy, semantic, vector, and hybrid search, with scoring, ranking, and reranking options. It also provides geospatial queries and analytics, including geo-distance, polygons, mapping, and geo-hex analysis.
Elasticsearch can be used through Elastic Cloud Serverless, Elastic Cloud Hosted, or self-managed deployments. Data can be connected through APIs, language clients, ingest pipelines, and integrations, while Kibana provides tools to explore, visualize, and build dashboards from Elasticsearch data.
What can Elasticsearch do?
Relevance tuning and reranking
Use ranking and reranking techniques to refine result order for search experiences where relevance depends on more than a direct keyword match.
Real-time analytics and columnar storage
Aggregate and transform data for analytics, including high-cardinality data, while columnar storage and searchable snapshots support different performance and storage needs.
Geospatial search and analytics
Work with location data using geo-distance queries, polygons, mapping, and geo-hex analytics for location-aware search and analysis.
Data ingestion and developer access
Connect systems through APIs, ingest pipelines, language clients, and integrations; official clients include Java, Python, and Go, with raw API access available.
Multiple deployment models
Run Elasticsearch as a fully managed Serverless service, a Hosted deployment with infrastructure managed by Elastic, or self-managed software on local, Kubernetes, on-premises, or cloud infrastructure.
Use Cases
“Ecommerce and support search”
Build search experiences that help customers find products, documentation, or support information using full-text, filters, semantic retrieval, and relevance controls.
“Search-driven applications”
Create application features that retrieve information from structured, unstructured, and vector data through a common search platform and developer APIs.
“Observability and log analytics”
Centralize logs and operational data for searching, exploration, dashboards, infrastructure monitoring, application performance monitoring, and incident analysis.
“Security analytics and threat investigation”
Search and analyze security data for SIEM workflows, threat detection, investigation, and response using real-time data and Kibana visualizations.
“AI retrieval and agent workflows”
Store and search embeddings, combine semantic and keyword retrieval, and support retrieval-augmented or agent workflows with vector search and reranking.
Frequently Asked Questions
What is Elasticsearch used for?
Elasticsearch is used for search applications, real-time analytics, log and observability workflows, security analytics, geospatial analysis, vector retrieval, and AI-driven search or agent workflows.
How can Elasticsearch be deployed?
It is available through Elastic Cloud Serverless, Elastic Cloud Hosted, and self-managed deployments. Self-managed Elasticsearch can run locally, through Kubernetes, or with an organization’s own orchestration on on-premises or cloud infrastructure.
What kinds of data can Elasticsearch store?
The source describes support for structured and unstructured data, time-series data, logs, events, geospatial data, and vector embeddings.
How do applications connect to Elasticsearch?
Applications and data sources can connect through APIs, ingest pipelines, integrations, and language clients, including Java, Python, and Go clients. Raw API access is also available.
What is the difference between Serverless and Hosted Elasticsearch?
Serverless is fully managed by Elastic and automatically scales with search and indexing load. Hosted provides more control over hardware configuration, cluster size, node count, and versions while Elastic manages the infrastructure.
Quick Facts
- Category
- Distributed search and analytics engine
- Search modes
- Full-text, fuzzy, semantic, vector, hybrid, filtered, and reranked search
- Data types
- Structured, unstructured, time-series, logs, events, geospatial, and vector data
- Deployment options
- Elastic Cloud Serverless, Elastic Cloud Hosted, and self-managed
- Developer access
- REST APIs, ingest pipelines, language clients, and Kibana
- Cloud providers
- AWS, Google Cloud, and Azure for Elastic Cloud offerings
Elasticsearch Alternatives
OpenSearch
opensearch.org
OpenSearch is a community-driven, Apache 2.0-licensed open source search and analytics suite for ingesting, searching, visualizing, and analyzing data. It supports application search, observability, security analytics, and machine learning workflows.
Vespa.ai
vespa.ai
Vespa is an AI search platform for building search, retrieval-augmented generation, recommendation, personalization, and agent applications over text, vectors, tensors, and structured data. It is designed for developer teams that need configurable ranking and distributed operation at production scale.
Ducky
ducky.ai
Ducky is a fully managed AI search and retrieval platform for developers building RAG-powered product features across text, images, PDFs, and structured data.
turbopuffer
turbopuffer.com
turbopuffer is an object-storage-native search engine for vector, full-text, hybrid, and regex search. It helps AI, search, and data teams retrieve content from large document collections with filtering, ranking, and scalable namespace-based storage.
Evlat
evlat.kalaomer.com
Evlat is a quiet macOS status strip for monitoring Claude Code, Codex, and Antigravity sessions, plus long-running commands. It highlights sessions that need your response and shows activity from local or SSH-connected machines.
Bluejay
getbluejay.ai
Bluejay is a QA platform for testing, monitoring, and improving AI voice and chat agents before and after deployment.