Elastic named a Leader in the 2026 Gartner® Magic Quadrant™ for Enterprise AI Search

Elasticsearch has proven itself as a comprehensive platform for enterprise AI, supporting over 18,000 organizations across geographies and industries. We believe  that strength is reflected in the Gartner Critical Capabilities results, where Elastic ranked first in AI Automation and second across all other use cases, from Deep Research and Context Engineering to AI Augmentation, Digital Workspace, and Enterprise Applications.

Enterprise AI is only as good as the context it retrieves. Hallucinations, irrelevant results, and growing token costs are increasingly a result of limited or missing context, rather than the choice of model. Elasticsearch is purpose-built to solve this: a high-performance retrieval engine that combines full-text, semantic, and hybrid search to surface the most relevant context for any AI workload.

Our takeaway from the report is that Elasticsearch’s ability to bring structured and unstructured data together with powerful search and retrieval capabilities is a key strength for delivering precise, grounded context to AI applications and agents. This aligns with our AI strategy: making the platform the context layer that agents and assistants are built on, rather than providing end-user assistant experiences.

Gartner evaluated vendors across six Use Cases in the Critical Capabilities report. Elastic ranked first in AI Automation with a score of 2.77 out of 3.0, the highest score in the report.

AI Automation reflects a vendor's ability to support AI agents that operate autonomously: retrieving context, reasoning over it, and taking action without constant human intervention. Elastic's strength here is grounded in three capabilities working together. Elasticsearch retrieves context with high precision across any type of data. Elastic Agent Builder and Elastic Workflows give developers the primitives to construct custom agentic pipelines connecting to enterprise data sources. With native Jina embedding and reader models, including OCR models, customers can read and index almost any input data with high precision at lower costs. And the open Agent Skills repository provides a growing library of prebuilt skills that agents can use to query Elasticsearch from Claude, Codex, Gemini, and more; execute ES|QL; and reason over results. Together these capabilities allow organizations to build and deploy AI automation that is reliable enough to run in production.

One of the less obvious advantages of building on Elasticsearch is the ecosystem around it. Elastic’s open source roots have helped foster an active global community of engineers with experience using the platform, which can make it easier for organizations to find talent with relevant skills and reduce onboarding time.

For organizations evaluating long-term platform risk, this matters. Proprietary platforms can increase dependency on a single vendor. A platform with deep open source roots, a large global community, and the flexibility to deploy on premises or in the cloud of choice gives organizations more options over time.

To us, this recognition comes on the back of significant product momentum. In October 2025, Elastic acquired Jina AI to deepen its capabilities in vector search, and Jina AI models, readers, and rerankers are now available to Elastic customers. Recent platform improvements also include the launch of Elastic Agent Builder, the new Elasticsearch Vector Database serverless offering, and vector efficiencies that reduce the cost and complexity of running AI workloads at scale.

Looking ahead, Elastic's roadmap includes a native Context Engine, advances in data federation, and improvements to built-in evaluation and model capabilities, all focused on making retrieval more accurate and easier to operationalize for enterprise AI use cases.

We believe being named a Leader in the Gartner Magic Quadrant for Enterprise AI Search, based on our positioning for Completeness of Vision and Ability to Execute, reflects what our customers have told us: that enterprise AI requires a retrieval foundation that is accurate, mature, and built to scale.

In our view, the Critical Capabilities results reinforce this. Elastic ranked first in AI Automation and second across every other use case. In our opinion, Elastic consistently outperformed or matched the best alternatives in every category.

We believe this recognition reflects the work our teams and our customers have done together to make AI search a production reality.

The 2026 Gartner® Magic Quadrant™ for Enterprise AI Search is now available. Access the report to learn more about the enterprise AI search market and why we believe Elastic was recognized as a Leader.

Explore how Elastic helps organizations build and deploy enterprise AI search at scale.