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Checkmk Announces Out-of-the-Box Monitoring of GKE Autopilot

Checkmk successfully completed a thorough review process with Google and is now an allowlisted GKE Autopilot partner workload. This makes Checkmk a Google Cloud partner that can collect detailed performance and health insights of Google Kubernetes Engine (GKE) Autopilot clusters. GKE Autopilot is a serverless, fully managed and automated operating mode of GKE. Instead of manually configuring or managing clusters as with GKE, Google Cloud handles these tasks for GKE Autopilot. However, the high level of automation comes with limitations. For example, elevated privileges are required to collect and read metrics needed for holistic Kubernetes monitoring. This privileged access is exactly what Checkmk, as one of just a few GKE Autopilot partners and monitoring solution providers worldwide, offers. As a result, Checkmk provides a clear, at-a-glance view of all critical GKE data via the pre-configured Kubernetes dashboard. "The GKE Autopilot partnership is another important building block in our Kubernetes monitoring strategy. It allows us to provide our users with full visibility when monitoring their Autopilot environment. At the same time, it underlines our ambition to continuously expand Checkmk's Kubernetes monitoring based on our customers' requirements," explains Martin Hirschvogel, VP Product at Checkmk.

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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 ...

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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 ...

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Checkmk Announces Out-of-the-Box Monitoring of GKE Autopilot

Checkmk successfully completed a thorough review process with Google and is now an allowlisted GKE Autopilot partner workload. This makes Checkmk a Google Cloud partner that can collect detailed performance and health insights of Google Kubernetes Engine (GKE) Autopilot clusters. GKE Autopilot is a serverless, fully managed and automated operating mode of GKE. Instead of manually configuring or managing clusters as with GKE, Google Cloud handles these tasks for GKE Autopilot. However, the high level of automation comes with limitations. For example, elevated privileges are required to collect and read metrics needed for holistic Kubernetes monitoring. This privileged access is exactly what Checkmk, as one of just a few GKE Autopilot partners and monitoring solution providers worldwide, offers. As a result, Checkmk provides a clear, at-a-glance view of all critical GKE data via the pre-configured Kubernetes dashboard. "The GKE Autopilot partnership is another important building block in our Kubernetes monitoring strategy. It allows us to provide our users with full visibility when monitoring their Autopilot environment. At the same time, it underlines our ambition to continuously expand Checkmk's Kubernetes monitoring based on our customers' requirements," explains Martin Hirschvogel, VP Product at Checkmk.

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 ...