Operations | Monitoring | ITSM | DevOps | Cloud

In almost all industries, a standard set of KPIs helps to guide teams on whether they are doing the right things in the right ways, with the right outcomes. In software development, this has evolved significantly with industry-standard frameworks like DORA metrics (DevOps Research and Assessment), which have been validated across thousands of organizations worldwide. Some development frameworks, such as Agile, have some KPIs baked directly into them.

IT infrastructure is now hyper-distributed. We are in a scale-in-seconds era and that means, a typical IT landscape is spread across on-premises data centers, public clouds (AWS, Azure, GCP), containerized environments, and edge locations. With many components comes more points of failure. A single server outage can cascade into customer-facing incidents, SLA violations, and revenue loss measured in thousands per minute.

Claude is one of the go-to AI-native code editors for developers. Because it’s a simple chatbot interface housed inside a familiar CLI, it provides a pretty smooth path between traditional IDEs and agentic AI. But what’s actually happening behind the scenes when you ask it to write code, generate a test, or debug an issue? Who and what is it talking to behind the scenes? Can I prevent data leakage or do I need to add another layer to my tin foil hat?

Modern enterprises operate within a digital ecosystem of staggering complexity - spanning on-premises systems, private and public clouds, APIs, containers and SaaS platforms. Business-critical services often rely on a mix of legacy infrastructure and modern applications, each producing huge volumes of metrics, log messages, traces and events.

Kubernetes v1.35 dropped a couple of weeks ago, and while the headlines focus on gang scheduling and in-place resizing going GA, there’s a bigger story here that every platform team needs to understand: Kubernetes is finally acknowledging that cluster utilization is fundamentally broken. At Komodor, we work with hundreds of organizations running Kubernetes at scale.

As AI workloads shift from training to massive-scale inference, SRE teams are about to feel even more pressure. GPU-heavy computing is breaking the assumptions today’s clusters were built on, while enterprises are beginning to trust autonomous operations and cost pressure is pushing consolidation across the cloud-infrastructure stack.