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Checkmk Cloud Now Delivers Full-Stack Monitoring and Observability

Checkmk expanded its SaaS platform, Checkmk Cloud. 

IT teams can see applications and infrastructure in context, pinpoint root causes faster, and resolve issues efficiently—without adding operational overhead. New capabilities include comprehensive SNMP coverage, application observability based on OpenTelemetry, and AI-powered alert analysis.

With Checkmk Relay, physical network devices such as NAS systems or printers can now be integrated directly into SaaS monitoring. The lightweight container collects monitoring data locally and transmits it mTLS-encrypted—without inbound traffic. Even highly segmented networks can be monitored securely, giving IT teams a complete and transparent view of their entire hybrid IT environment within a fully managed SaaS model.

Checkmk Cloud also expands visibility into the application layer. The platform captures application metrics via OpenTelemetry and Prometheus scraping, and brings it together with infrastructure metrics for cross-analysis in a single pane of glass. IT teams can immediately determine whether performance issues originate in the application or the underlying infrastructure, taking precise action to significantly reduce MTTR and minimize service impact. Custom or legacy applications, including Java or .NET workloads, can also be integrated via auto-instrumentation, without any code changes. Checkmk Cloud comes with pre-configured dashboards for RED signals—visualizing request rate, errors, and latency—so IT teams can quickly identify user-impacting problems and prioritize resolution.

Checkmk Cloud analyzes incoming alerts and translates them into clear, understandable explanations of potential root causes. IT teams receive actionable insights, reducing analysis time and enabling faster response in critical situations.

With “Explain with AI,” Checkmk Cloud analyzes incoming alerts and translates them into clear, understandable explanations of potential root causes. IT teams receive actionable insights, reducing analysis time and enabling faster response in critical situations. The function also generates readable incident summaries, improving documentation and cross-team communication.

Checkmk Cloud is ready to use within minutes, letting IT teams monitor applications, on-premises systems, containers, and multi-cloud environments without installation or maintenance. Automated discovery of new systems and pre-configured integrations reduce setup effort and simplify operations. The fully managed SaaS platform delivers 99.5 % availability and meets strict security and compliance standards, with data residency in the EU or the United States. Designed for organizations seeking enterprise-grade monitoring without the complexity of self-managed solutions, Checkmk Cloud focuses on rapid deployment, automated operations, and minimal administrative overhead.

The Latest

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 ...

Checkmk Cloud Now Delivers Full-Stack Monitoring and Observability

Checkmk expanded its SaaS platform, Checkmk Cloud. 

IT teams can see applications and infrastructure in context, pinpoint root causes faster, and resolve issues efficiently—without adding operational overhead. New capabilities include comprehensive SNMP coverage, application observability based on OpenTelemetry, and AI-powered alert analysis.

With Checkmk Relay, physical network devices such as NAS systems or printers can now be integrated directly into SaaS monitoring. The lightweight container collects monitoring data locally and transmits it mTLS-encrypted—without inbound traffic. Even highly segmented networks can be monitored securely, giving IT teams a complete and transparent view of their entire hybrid IT environment within a fully managed SaaS model.

Checkmk Cloud also expands visibility into the application layer. The platform captures application metrics via OpenTelemetry and Prometheus scraping, and brings it together with infrastructure metrics for cross-analysis in a single pane of glass. IT teams can immediately determine whether performance issues originate in the application or the underlying infrastructure, taking precise action to significantly reduce MTTR and minimize service impact. Custom or legacy applications, including Java or .NET workloads, can also be integrated via auto-instrumentation, without any code changes. Checkmk Cloud comes with pre-configured dashboards for RED signals—visualizing request rate, errors, and latency—so IT teams can quickly identify user-impacting problems and prioritize resolution.

Checkmk Cloud analyzes incoming alerts and translates them into clear, understandable explanations of potential root causes. IT teams receive actionable insights, reducing analysis time and enabling faster response in critical situations.

With “Explain with AI,” Checkmk Cloud analyzes incoming alerts and translates them into clear, understandable explanations of potential root causes. IT teams receive actionable insights, reducing analysis time and enabling faster response in critical situations. The function also generates readable incident summaries, improving documentation and cross-team communication.

Checkmk Cloud is ready to use within minutes, letting IT teams monitor applications, on-premises systems, containers, and multi-cloud environments without installation or maintenance. Automated discovery of new systems and pre-configured integrations reduce setup effort and simplify operations. The fully managed SaaS platform delivers 99.5 % availability and meets strict security and compliance standards, with data residency in the EU or the United States. Designed for organizations seeking enterprise-grade monitoring without the complexity of self-managed solutions, Checkmk Cloud focuses on rapid deployment, automated operations, and minimal administrative overhead.

The Latest

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 ...