
Checkmk connects infrastructure monitoring with application observability to deliver a unified view of system and application performance with the launch of version 2.5.
Metrics from both layers can now be analysed and triaged from a unified interface enabling IT teams to directly identify dependencies across the entire IT stack—from legacy systems to microservices—and resolve issues faster.
Building on this unified approach, Version 2.5 expands the platform with additional functions that enable IT teams to reduce operational effort and respond quicker to incidents. Among these key additions, Checkmk integrates Checkmk Relay for lightweight, maintenance-free monitoring of remote networks and enhances Checkmk Cloud with AI-powered alert analysis.
Jan Justus, CEO of Checkmk, said, "In today’s hybrid IT environments, applications span on-premises, cloud, and container platforms, creating blind spots and extra overhead from using multiple tools. That’s why we extended Checkmk to an integrated observability platform that provides full context into applications and infrastructure. By eliminating tool-switching, IT teams can pinpoint and resolve issues directly, reducing MTTR and lowering total cost of ownership."
The platform collects application metrics via OpenTelemetry (OTLP) or Prometheus and automatically links them to the corresponding infrastructure hosts for cross-analysis—without additional middleware. A powerful data backend enables near real-time analysis of high-volume metrics, letting IT teams cross-analyze issues such as rising response times with CPU, I/O, or network bottlenecks. This turns application anomalies into actionable insights for faster troubleshooting.
Visibility is not limited to modern architectures. Custom or legacy applications, including Java and .NET workloads, can be integrated via auto-instrumentation without modifying source code. OpenTelemetry also extends visibility into AWS, GCP, and Azure services beyond vendor APIs.
Pre-configured dashboards track key application health signals using the RED metrics: Rate (requests), Errors (failed requests), and Duration (latency). This enables faster detection of user-impacting incidents and prioritization of resolution.
Checkmk 2.5 introduces Checkmk Relay for lean and highly secure monitoring in remote offices and segmented networks. The lightweight container transmits locally collected data outbound only (Zero Inbound) and mTLS-encrypted to the central Checkmk instance, eliminating the need for inbound ports or complex firewall rules.
In smaller environments, Checkmk Relay can replace full remote sites, significantly reducing operational effort. For Checkmk Cloud users, it also extends monitoring to SNMP devices such as NAS systems and printers, enabling full-stack monitoring across hybrid IT environments.
AI-powered alert analysis
For users of the Saas edition Checkmk Cloud, the “Explain with AI” feature accelerates alert analysis by translating alerts into clear assessments of potential root causes. Administrators receive immediately actionable insights, helping them respond faster in critical situations. The function also generates readable incident summaries, improving documentation and cross-team communication.
Checkmk 2.5 delivers enhanced visibility across Microsoft Azure environments with new dashboards powered by integrated metadata. This enables faster, more actionable insights across even highly complex cloud infrastructures. By natively mapping Azure tags into Checkmk labels, the platform provides seamless navigation across multi-subscription environments and improves data retrieval performance by up to 60%. New dedicated checks for Redis Cache, Cosmos DB, and NAT Gateways extend coverage across critical cloud services, closing critical visibility gaps and ensuring end-to-end visibility across the digital environment.
Checkmk 2.5 also enhances virtualization support. New checks for Proxmox provide visibility into CPU and memory overcommitment and cluster health via Corosync, allowing resource bottlenecks to be identified early. In Hyper-V, VM configurations and integration services can now be monitored, improving virtual machine availability. Support for Podman is also extended.
The Latest
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 ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...
Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...