What is Elastic
Elastic is a technology company that built the Elastic Stack—a set of products comprising Elasticsearch (a distributed search and analytics engine), Kibana (a visualization and management UI), Logstash (a server-side data collection pipeline), Beats (lightweight data collectors), and Elastic Agent (a unified agent for collecting logs, metrics, and security data). Elastic offers three major solutions: Elasticsearch, Elastic Observability, and Elastic Security, all of which can be deployed via Elastic Cloud Serverless, Elastic Cloud Hosted, or self-managed.
What is Elasticsearch
Elasticsearch is the search database for AI, a distributed, RESTful search and analytics engine capable of efficiently storing data in various forms. It is the core component of the Elastic Stack, built on top of the open-source search library Apache Lucene. Elasticsearch stores, indexes, and queries large-scale structured and unstructured data through a RESTful HTTP API, supporting full-text search, vector search, hybrid search, and real-time analytics at scale.
The difference between Elastic and Elasticsearch
Elastic is the company, and Elasticsearch is the search and analytics engine built by Elastic. The Elastic Stack is the collective name for all of Elastic's products, including Elasticsearch, Elastic Security, Elastic Observability, Jina AI, Kibana, Streams, Elastic Context Engine, Elastic Workflows, Logstash, Beats, and Elastic Agent. All Elastic Stack products can be deployed via Elastic Cloud Serverless, Elastic Cloud Hosted, or self-managed on your own infrastructure.
The search database for AI
Elasticsearch can, in oneThe engine stores data in various forms economically, including documents, vectors, logs, metrics, traces, images, geospatial data, and more; it can search data anywhere; it supports vector search based on dense embeddings, semantic search using natural language processing, and retrieval-augmented generation (RAG) pipelines that ground large language model (LLM) outputs with retrieved documents at inference time. Traditional databases return rows or keyword matches, while a search database provides ranked, pre-assembled context, with the engine handling relevance, reranking, and token budgets.
Key capabilities
Elasticsearch supports vector search based on dense vector embeddings and semantic search, the latter using natural language processing to retrieve results by meaning rather than exact keyword matching. Elasticsearch supports hybrid search by combining BM25 keyword retrieval with vector retrieval and reranking, and also supports native machine learning model inference as well as RAG pipelines that provide grounded context to LLMs at inference time. Overall, Elasticsearch can serve as the retrieval and context layer for AI agents, handling knowledge base queries, memory retrieval, and tool calls, and using continuously updated data.
Performance and ranking information provided by official materials: query speeds up to 30 times faster than Prometheus and Mimir; ranked as the number one search engine on DB-Engines; uses BBQ to cut vector memory by about 95%; filtered vector search up to 8 times faster than OpenSearch; achieves full-fidelity telemetry data retention at half the cost. Proficio achieved a 34% increase in investigation speed through automated triage, with estimated savings of $1 million.
Unified data platform
Elastic provides a unified data platform covering the complete process from data collection and storage to retrieval, visualization, and agent orchestration, helping development teams deliver large-scale, context-driven AI applications faster. The platform provides out-of-the-box best-in-class capabilities in the AI data stack—chunking, embedding models, hybrid retrieval, and reranking—without the need to assemble and maintain a pipeline made up of multiple separate tools. The platform can run in any environment an organization needs: cloud, on-premises, or Serverless.
How Elastic uses AI
Elasticsearch supports vector search, semantic search, and hybrid search (BM25 keyword retrieval combined with vector retrieval and reranking), supports native machine learning model inference, and RAG pipelines that provide grounded context to LLMs. Elasticsearch serves as the retrieval and context layer for AI agents, handling knowledge base queries, memory retrieval, and tool calls, and using continuously updated data.
How Elastic differs from other platforms
The Elastic Stack unifies Elasticsearch, Elastic Observability, and Elastic Security in a single platform. Data only needs to be indexed once to drive search, monitoring, and threat detection, without duplicating infrastructure. Elasticsearch, as a search database built for AI, stores all data forms—structured, unstructured, vector, and telemetry—in a single engine; it federates queries in place across data lakes, data warehouses, and object storage via ES|QL; and it retrieves ranked, token-efficient context for models and agents. Elasticsearch ranks first among search engines and vector databases in DB-Engines, and filtered vector search is up to 8 times faster than OpenSearch.
Three major solutions
Elastic offers three solutions: Elasticsearch for enterprise search, vector search, vector databases, and RAG; Elastic Observability for logs, metrics, traces, and APM; and Elastic Security for SIEM, endpoint protection, and native automation. Kibana is the visualization and management UI shared by all solutions, and data is collected through Elastic Agent, Beats, or Logstash. All solutions are available on Elastic Cloud Serverless, Elastic Cloud Hosted, and self-managed.
Other capabilities
Elastic's night-shift AI SRE can detect and investigate across the entire technology stack, with full context. Cross-project search (CPS) is now generally available, providing a unified view across Serverless projects while data remains in place. Elastic was named a Leader in the 2026 Gartner® Magic Quadrant™ for Enterprise AI Search. The company states that 75% of Fortune 100 companies choose Elastic, citing "better, cheaper, faster."
Open source and licensing
Elasticsearch uses dual licensing: the source code is released under both AGPL v3 (an OSI-approved open source license) and the Elastic License 2.0 (a source-available license that restricts certain commercial uses). Elasticsearch is built on Apache Lucene, which is open source software under the Apache 2.0 license. Most developers can use Elasticsearch for free under AGPL; the Elastic License 2.0 applies to deployments that exceed the scope of use permitted by AGPL.
Free trial and downloads
Elastic Cloud offers a 14-day free trial with no credit card required; the trial includes Elastic Cloud Serverless and Elastic Cloud Hosted, allowing evaluation of both managed deployment modes. Elastic Cloud is available in Serverless or Hosted form with no local installation; developers can configure a managed cluster at elastic.co/cloud without downloading anything. For self-managed and local deployments, Elasticsearch, Kibana, Elastic Agent, Logstash, and Beats are all available from the Elastic downloads page as packages, Docker images, and Kubernetes Helm charts. All self-managed products support Linux, macOS, Windows, Docker, and Kubernetes.
