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Checkmk 2.4 Integrates OpenTelemetry and Synthetic Testing

Checkmk today announced the release of version 2.4, introducing powerful new features  designed to enhance full-stack monitoring. With integrated OpenTelemetry metrics and synthetic testing, IT teams gain end-to-end visibility across all layers of the IT stack — from infrastructure and applications to end-user experience. These capabilities enable faster, more proactive issue resolution and significantly reduce mean time to resolution (MTTR). Version 2.4 also introduces features such as quick setup for cloud workload monitoring and a redesigned Notification Hub, which reduce administrative overhead and lighten the load on overburdened IT teams. With this release, Checkmk addresses two of the biggest challenges in modern IT: rising system complexity and the ongoing shortage of skilled professionals.

Checkmk’s integration of OpenTelemetry allows IT teams to look inside their applications and monitor performance, availability, and potential failure points—right from the application code, all within a single platform. The built-in OpenTelemetry collector ingests data directly or via Prometheus endpoints, translates it into actionable metrics, and maps them to the relevant hosts. This provides clear visibility into not just what is failing, but where and why, enabling faster root cause analysis and targeted fixes — even for previously unidentified issues.

Checkmk 2.4 also introduces enhanced synthetic monitoring capabilities, making it easier for teams to create tests that simulate user behavior and assess availability, performance, and functionality from the end-user perspective. First introduced in version 2.3, synthetic testing is now fully integrated into the Checkmk interface. Test robots that simulate user behavior can be uploaded via the web UI and centrally configured and managed. These managed robots can be cloned, customized, and automatically deployed to Linux or Windows test nodes using the Checkmk Agent Bakery. New features also support synthetic testing in isolated offline environments, and KPI monitoring allows teams to track and analyze individual process steps within each test.

Checkmk 2.4 introduces a range of enhancements that boost usability, increase automation, and improve efficiency — while reducing administrative overhead. 

Highlights include:

  • Quick Setup: Cloud monitoring in minutes - Checkmk’s new Quick Setup feature streamlines and accelerates cloud monitoring configuration across AWS, Azure, and GCP. A guided, step-by-step process handles complex setup tasks in the background and verifies system connections, enabling administrators to achieve full cloud visibility quickly and reliably.
  • Notification Hub: Simplified alert configuration - The new Notification Hub streamlines the configuration, management, and fine-tuning of alerting workflows through an intuitive interface and improved user guidance. Key settings are centralized and accessible with just a few clicks, while real-time status messages and troubleshooting tips help users stay informed and respond quickly. Usability features such as search, slide-outs, and drop-down menus make setup more efficient. A newly added guided mode walks beginners through the configuration process step-by-step — saving time and reducing the risk of misconfiguration.
  • Dynamic host management: Automated control of Kubernetes clusters - In dynamic environments like Kubernetes or virtualized systems, hosts are constantly being created and removed. Checkmk detects these changes in real time, automatically adds new hosts to the monitoring system, and reliably removes those that no longer exist. Designed for maximum scalability, the dynamic host management feature ensures stable, high-performance monitoring — even with hundreds of changes per minute.

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Checkmk 2.4 Integrates OpenTelemetry and Synthetic Testing

Checkmk today announced the release of version 2.4, introducing powerful new features  designed to enhance full-stack monitoring. With integrated OpenTelemetry metrics and synthetic testing, IT teams gain end-to-end visibility across all layers of the IT stack — from infrastructure and applications to end-user experience. These capabilities enable faster, more proactive issue resolution and significantly reduce mean time to resolution (MTTR). Version 2.4 also introduces features such as quick setup for cloud workload monitoring and a redesigned Notification Hub, which reduce administrative overhead and lighten the load on overburdened IT teams. With this release, Checkmk addresses two of the biggest challenges in modern IT: rising system complexity and the ongoing shortage of skilled professionals.

Checkmk’s integration of OpenTelemetry allows IT teams to look inside their applications and monitor performance, availability, and potential failure points—right from the application code, all within a single platform. The built-in OpenTelemetry collector ingests data directly or via Prometheus endpoints, translates it into actionable metrics, and maps them to the relevant hosts. This provides clear visibility into not just what is failing, but where and why, enabling faster root cause analysis and targeted fixes — even for previously unidentified issues.

Checkmk 2.4 also introduces enhanced synthetic monitoring capabilities, making it easier for teams to create tests that simulate user behavior and assess availability, performance, and functionality from the end-user perspective. First introduced in version 2.3, synthetic testing is now fully integrated into the Checkmk interface. Test robots that simulate user behavior can be uploaded via the web UI and centrally configured and managed. These managed robots can be cloned, customized, and automatically deployed to Linux or Windows test nodes using the Checkmk Agent Bakery. New features also support synthetic testing in isolated offline environments, and KPI monitoring allows teams to track and analyze individual process steps within each test.

Checkmk 2.4 introduces a range of enhancements that boost usability, increase automation, and improve efficiency — while reducing administrative overhead. 

Highlights include:

  • Quick Setup: Cloud monitoring in minutes - Checkmk’s new Quick Setup feature streamlines and accelerates cloud monitoring configuration across AWS, Azure, and GCP. A guided, step-by-step process handles complex setup tasks in the background and verifies system connections, enabling administrators to achieve full cloud visibility quickly and reliably.
  • Notification Hub: Simplified alert configuration - The new Notification Hub streamlines the configuration, management, and fine-tuning of alerting workflows through an intuitive interface and improved user guidance. Key settings are centralized and accessible with just a few clicks, while real-time status messages and troubleshooting tips help users stay informed and respond quickly. Usability features such as search, slide-outs, and drop-down menus make setup more efficient. A newly added guided mode walks beginners through the configuration process step-by-step — saving time and reducing the risk of misconfiguration.
  • Dynamic host management: Automated control of Kubernetes clusters - In dynamic environments like Kubernetes or virtualized systems, hosts are constantly being created and removed. Checkmk detects these changes in real time, automatically adds new hosts to the monitoring system, and reliably removes those that no longer exist. Designed for maximum scalability, the dynamic host management feature ensures stable, high-performance monitoring — even with hundreds of changes per minute.

The Latest

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...