Elastic named a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms

Elastic has been named a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms for the third consecutive year, citing its cost-efficient storage for logs, metrics, and traces and AI-driven investigation capabilities as key differentiators. The company attributes its recognition to innovations like Elasticsearch's purpose-built engines and context engineering that enable full-fidelity telemetry storage and proactive AI-assisted root cause analysis.

Elastic named a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms Elastic has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Observability Platforms for the third consecutive year. In our view, the observability market looks different than it did when we first received that recognition. AI has accelerated telemetry volume growth in ways that were difficult to anticipate, putting pressure on storage costs and on the budgets of the teams managing them. Expectations for AI-assisted investigations have moved from conversational to operational and agentic. And the ecosystem has continued to evolve, with OpenTelemetry OTel https://www.elastic.co/observability/opentelemetry and Prometheus https://www.elastic.co/elasticsearch/prometheus-monitoring establishing themselves as standards that any serious platform needs to support natively, without a schema translation tax. We believe this recognition reflects how we have responded to those shifts. Here is what we believe got us here. High cost-efficiency for logs, metrics, and traces Telemetry volumes are exploding thanks to AI, driving up costs across the industry. The standard response is to store less: tighter retention policies, aggressive sampling, deprioritized data types. The problem with that approach is that it compounds. Every piece of telemetry dropped is context no longer available, and context is exactly what AI-driven investigations depend on. Elastic approaches this differently. Elasticsearch stores logs and traces up to 4x more efficiently https://www.elastic.co/observability-labs/blog/elasticsearch-logsdb-storage-evolution than standard indexing, and metrics up to 2.5x https://www.elastic.co/search-labs/blog/elasticsearch-columnar-metrics-engine-30x-faster-prometheus better than Prometheus. It runs two purpose-built engines under a single interface: a full-text search engine optimized for logs and traces, and a fully columnar engine optimized for metrics. Each engine is designed for the shape of the data it handles, which is where the efficiency gains come from. Both share the same query language, APIs, and dashboards, so teams work across all three signal types from a single interface without separate backends to maintain and without context switching between tools. Storing everything costs less than storing a fraction of it on a less efficient platform. Your logs have the answer. Elastic finds it. Logs are the richest signal in observability but often underutilized because they are unstructured and expensive to store at full fidelity. Most teams keep what storage budgets allow and treat log search as something done reactively, by experts, when an incident demands it. Elastic Streams https://www.elastic.co/elasticsearch/streams extracts structure, meaning, and operational context from raw logs automatically, turning a reactive, expert-only signal into a proactive one. It uses AI to surface Knowledge Indicators KIs without requiring engineers to know what to search for in advance, pulling out entities and dependencies from unstructured data so the relevant context is already available when an alert fires. AI-driven investigations with full context Elastic offers AI agents and machine learning https://www.elastic.co/observability/aiops ML built on the richest possible context for investigations and root cause analysis RCA . Because telemetry is stored efficiently enough that nothing needs to be dropped, and log data is structured well enough to be actionable, AI has complete context to work from. Underpinning all of this is the retrieval layer. Elasticsearch retrieves relevant logs, metrics, and traces semantically, not just by keyword match. The quality of that retrieval is what determines whether the AI finds the right context or a close approximation of it. This is where context engineering https://www.elastic.co/elasticsearch/context-engineering becomes essential: Elastic structures and enriches telemetry at ingest, tagging entities, extracting dependencies, and building service maps, so that the data is prepared for AI consumption before an alert ever fires. For teams that need to build their own investigation workflows, Elastic Agent Builder https://www.elastic.co/elasticsearch/agent-builder and Workflows https://www.elastic.co/elasticsearch/workflows provide the primitives to construct custom AI agents. Elastic also provides an open repository of Agent Skills https://github.com/elastic/agent-skills that can query Elasticsearch, execute ES|QL, and reason over results. Preconfigured anomaly detection and log categorization capabilities have been a core part of the platform for years. All these capabilities support agent-driven investigations and remediation. Future-proof with open standards Betting on a closed stack means migration work every time the ecosystem evolves. Every instrumentation change has an engineering cost in the form of new pipelines, schema translation, and data reconciliation. Within observability, OTel https://www.elastic.co/observability/opentelemetry is becoming the dominant instrumentation standard, while Prometheus https://www.elastic.co/elasticsearch/prometheus-monitoring is often the default for metrics. Elasticsearch is open by design, schema-neutral https://www.elastic.co/observability/infrastructure-monitoring one-datastore-all-formats-no-context-switching , and built on OTel from the ground up. With Elasticsearch, teams can ingest any data from any source, whether Prometheus, OTel, or any other format, store it natively, and query it as-is. Data stays in its native format with no translation layer and nothing lost in conversion. What this recognition means to us We believe being named a Leader in the Gartner Magic Quadrant for the third consecutive year reflects what our customers have told us: that observability should not force tradeoffs, teams should be able to store all their telemetry efficiently, to build on open standards rather than closed ones, and to ensure that AI investigations have the full context they need. Each of the four areas covered here connects to the others: efficiency makes complete context possible, complete context makes AI investigations reliable, and open standards ensure that investment is not lost when the industry moves on. We believe this recognition reflects our continued progress in all four areas. Read the full report The 2026 Gartner® Magic Quadrant™ for Observability Platforms https://www.elastic.co/resources/observability/analyst-report/gartner-magic-quadrant-observability-platforms is now available. Access the report to learn more about the observability market and why we believe Elastic was recognized as a Leader. Explore how Elastic Observability https://www.elastic.co/observability helps organizations investigate issues faster, monitor AI-powered applications, embrace open standards, and operate confidently at scale. Gartner, Magic Quadrant for Observability Platforms, Padraig Byrne, Martin Caren, D.B. Cummings, Neil Young, 15 July 2026. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. 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