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.”
The Latest
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...