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Elastic Named a Leader in 2025 IDC MarketScape for Worldwide Observability Platforms

Elastic has been named a Leader in the IDC MarketScape: Worldwide Observability Platforms 2025 Vendor Assessment (doc #US53004325, November 2025).

According to the IDC MarketScape report, “Elastic's overarching strength is an open standards–first architecture that reduces tooling white space by natively ingesting OpenTelemetry data, correlating across signals, and exposing pipeline and retention controls that help teams move from detection to decision without re-instrumenting estates or duplicating data flows across hybrid and multicloud.”

IDC also highlights in its report that users should choose Elastic “...when an open standards observability platform with Prometheus and OpenTelemetry alignment, RUM/APM correlation, and petabyte-scale retention controls is needed.”

Elastic Observability is the OpenTelemetry (OTel) native, AI-driven platform that unifies operational and business data, empowering SRE teams to detect, investigate and resolve problems faster – at scale. It enables customers to ingest and correlate OTel data while reducing complexity across hybrid and multi-cloud environments, and offers strong interoperability with zero-code auto-instrumentation across major languages to accelerate onboarding. The platform also provides enterprise-grade support and improves governance in diverse, large-scale environments.

“Elastic links technical performance to customer experience and business context out of the box,” said Shannon Kalvar, research director at IDC. “The Elastic platform’s extensibility, combined with broad connector coverage and role-appropriate views, supports shared context across development and operations while maintaining cost governance levers at scale.”

Recent Elastic Observability innovations include the EDOT for OTel enterprise-grade support, and Streams, an agentic AI-powered solution that rethinks how teams work with logs to enable faster incident investigation and resolution. With Streams, SREs no longer need to spend time wrangling data before they can be investigators.

“Our mission is to help every team move from reactive troubleshooting to proactive, intelligent operations that make digital experiences fast, reliable and resilient,” said Santosh Krishnan, senior vice president of Software Engineering at Elastic. “We believe this IDC MarketScape recognition underscores our continued investment in redefining observability, including Streams to derive value from your signals faster and Agent Builder to build AI agentic workflows.”

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Elastic Named a Leader in 2025 IDC MarketScape for Worldwide Observability Platforms

Elastic has been named a Leader in the IDC MarketScape: Worldwide Observability Platforms 2025 Vendor Assessment (doc #US53004325, November 2025).

According to the IDC MarketScape report, “Elastic's overarching strength is an open standards–first architecture that reduces tooling white space by natively ingesting OpenTelemetry data, correlating across signals, and exposing pipeline and retention controls that help teams move from detection to decision without re-instrumenting estates or duplicating data flows across hybrid and multicloud.”

IDC also highlights in its report that users should choose Elastic “...when an open standards observability platform with Prometheus and OpenTelemetry alignment, RUM/APM correlation, and petabyte-scale retention controls is needed.”

Elastic Observability is the OpenTelemetry (OTel) native, AI-driven platform that unifies operational and business data, empowering SRE teams to detect, investigate and resolve problems faster – at scale. It enables customers to ingest and correlate OTel data while reducing complexity across hybrid and multi-cloud environments, and offers strong interoperability with zero-code auto-instrumentation across major languages to accelerate onboarding. The platform also provides enterprise-grade support and improves governance in diverse, large-scale environments.

“Elastic links technical performance to customer experience and business context out of the box,” said Shannon Kalvar, research director at IDC. “The Elastic platform’s extensibility, combined with broad connector coverage and role-appropriate views, supports shared context across development and operations while maintaining cost governance levers at scale.”

Recent Elastic Observability innovations include the EDOT for OTel enterprise-grade support, and Streams, an agentic AI-powered solution that rethinks how teams work with logs to enable faster incident investigation and resolution. With Streams, SREs no longer need to spend time wrangling data before they can be investigators.

“Our mission is to help every team move from reactive troubleshooting to proactive, intelligent operations that make digital experiences fast, reliable and resilient,” said Santosh Krishnan, senior vice president of Software Engineering at Elastic. “We believe this IDC MarketScape recognition underscores our continued investment in redefining observability, including Streams to derive value from your signals faster and Agent Builder to build AI agentic workflows.”

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As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...