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Elastic

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Elastic is a search, analytics, and AI platform built around Elasticsearch for structured, unstructured, and vector data, with flexible deployment options.

What is Elastic?

Elastic is a search, analytics, and AI platform built around Elasticsearch. It brings together search, vector storage, analytics, and security capabilities so teams can build applications that work with structured, unstructured, and vector data in one system.

The site positions Elastic for search applications, observability, security operations, and agentic AI workflows. It also offers Elastic Cloud in hosted and serverless forms, plus self-managed deployment, so teams can choose how they run the platform based on operational control and scale needs.

What can Elastic do?

Search and relevance

Build search applications with structured, unstructured, and vector data in one platform, including hybrid search, semantic search, and relevance tuning.

Unified datastore

Store and query structured data, unstructured content, vectors, and graphs, with ES|QL for joins and analytics.

Vector storage and retrieval

Use the platform as a vector database for dense and sparse vectors, with support for scaling to large deployments.

Analytics and geospatial search

Run real-time analytics and geospatial queries with spatial indexing, distance sorting, and area filters.

Flexible deployment models

Deploy on Elastic Cloud, self-managed, on-premises, or across clouds, with hosted, serverless, and self-managed pricing options.

Agent building

Use Agent Builder to create, test, and scale context-driven AI agents with your data, models, and tools.

Use Cases

“Search applications”

Build customer-facing or internal search experiences that combine semantic search, vector search, query rules, synonyms, and faceted browsing for relevant results.

“Observability”

Use the platform for monitoring applications and infrastructure, including ingesting metrics and querying them to resolve issues faster.

“Security operations”

Support security operations with SIEM, XDR, automation, and AI-assisted investigation and response workflows.

“Agentic AI”

Create context-aware agents and conversational search experiences that use retrieval, ranking, security controls, and your own data sources.

“Evaluation and deployment”

Run local or cloud-based deployments when teams need a quick start, a free trial, or a managed environment that can scale with demand.

Frequently Asked Questions

Is Elasticsearch open source?

Yes. The source states that Elasticsearch and Kibana are open source under the AGPL license.

Do I need separate products for text, vector, and hybrid search?

No. Elastic says its BM25 textual search, vector database, semantic search, and reciprocal rank fusion (RRF) hybrid scoring all come ready to use with Elasticsearch.

Is Elastic a vector database?

Yes. Elastic describes Elasticsearch as a scalable vector database for storing and searching vector embeddings, alongside search and analytics features.

How can I try Elastic?

Elastic says you can start locally, use a free cloud trial, or talk to an expert for complex deployments.

What deployment options does Elastic Cloud offer?

The pricing page says Elastic Cloud is available as hosted, serverless, and self-managed options for search, observability, and security.

Quick Facts

Category
Search and AI platform
Primary product
Elasticsearch
Company
Elastic
Deployment options
Hosted, serverless, self-managed, local
Source domain
elastic.co
Common workflows
Search, observability, security, agentic AI

Elastic Traffic Analysis

Traffic data is for reference only.

Monthly Visits
1.1M
Global Rank
#42,114
User Bounce Rate
40.8%
Avg. Visit Duration
02:54
Pages per Visit
4.14
Domain Rating
90

Traffic Trends

Traffic Sources

Traffic analytics are not available yet.

Top Regions

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