Search and relevance
Build search applications with structured, unstructured, and vector data in one platform, including hybrid search, semantic search, and relevance tuning.
Elastic is a search, analytics, and AI platform built around Elasticsearch for structured, unstructured, and vector data, with flexible deployment options.
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.
Build search applications with structured, unstructured, and vector data in one platform, including hybrid search, semantic search, and relevance tuning.
Store and query structured data, unstructured content, vectors, and graphs, with ES|QL for joins and analytics.
Use the platform as a vector database for dense and sparse vectors, with support for scaling to large deployments.
Run real-time analytics and geospatial queries with spatial indexing, distance sorting, and area filters.
Deploy on Elastic Cloud, self-managed, on-premises, or across clouds, with hosted, serverless, and self-managed pricing options.
Use Agent Builder to create, test, and scale context-driven AI agents with your data, models, and tools.
Build customer-facing or internal search experiences that combine semantic search, vector search, query rules, synonyms, and faceted browsing for relevant results.
Use the platform for monitoring applications and infrastructure, including ingesting metrics and querying them to resolve issues faster.
Support security operations with SIEM, XDR, automation, and AI-assisted investigation and response workflows.
Create context-aware agents and conversational search experiences that use retrieval, ranking, security controls, and your own data sources.
Run local or cloud-based deployments when teams need a quick start, a free trial, or a managed environment that can scale with demand.
Yes. The source states that Elasticsearch and Kibana are open source under the AGPL license.
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.
Yes. Elastic describes Elasticsearch as a scalable vector database for storing and searching vector embeddings, alongside search and analytics features.
Elastic says you can start locally, use a free cloud trial, or talk to an expert for complex deployments.
The pricing page says Elastic Cloud is available as hosted, serverless, and self-managed options for search, observability, and security.
Traffic data is for reference only.
| Jun | 1285892 |
|---|---|
| Jul | 1211984 |
| Aug | 1137732 |
Traffic analytics are not available yet.
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