tribe29, the creators of Checkmk, announced Checkmk 2.0, the most significant update to their all-in-one IT monitoring solution in the company's history. Checkmk 2.0 adds powerful new capabilities that allow users to quickly and easily establish monitoring across applications, networks, services, and servers in distributed, heterogeneous environments. First released 2008, Checkmk is available through a range of free, open source and enterprise monitoring offerings designed for organizations of all sizes. Checkmk's popularity is largely due to the breadth of monitoring coverage that can be rapidly achieved through out-of-the-box autodiscovery of relevant services. Checkmk combines an unparalleled collection of over 1,900 official plugins together with backward compatibility with legacy Nagios plugins. Checkmk 2.0 extends this breadth of coverage with new or improved plugins for public cloud services, Kubernetes, Proxmox, Nutanix, Jenkins, Jira, Graylog, Elasticsearch, Couchbase, RabbitMQ, Redis, MongoDB, and more. This coverage is taken further with a new Check API for simplifying the extension of Checkmk's coverage into new or bespoke systems. Checkmk 2.0 features a complete redesign of the user interface with advanced dashboarding used to simplify the initial user experience. New dashboards use updated vizualization to presented new users with an easily grokkable view of key monitoring metrics, while power users have a consistent path to drill down into substantial detail Checkmk offers. For DevOps driven environments there are two new features to integrate with cloud and container tooling. The Prometheus integration provides the ability to integrate metrics from platforms such as Kubernetes by allowing Checkmk to surface the wealth of information that Prometheus provides, bridging the DevOps and data center gap. In addition, the new REST API allows platform engineers to integrate existing tooling, and create brand-new tools, by allowing the full range of Checkmk capabilities within a standardized API. While Checkmk has always offered network device monitoring, integration with the popular ntop open source network flow monitoring solution allows users to analyse traffic flows and view what is happening on the network in detail directly in Checkmk. This provides a more powerful “single pane” observability solution for tracing problems across application, compute and network activity. Meanwhile, users of the Enterprise Edition will benefit from new performance optimizations in the Checkmk Micro Core (CMC). "With Checkmk 2.0 we have delivered a wide range of new features, and made monitoring even easier to set up and use - even for newcomers to monitoring," said Jan Justus, CEO of tribe29. "We have made many improvements in this release, including those that help users operate in cloud and container environments. At the same time, our optimization work means that the system is more efficient than ever." Checkmk 2.0 is available as free, open source and enterprise editions.
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 ...