Operations | Monitoring | ITSM | DevOps | Cloud

In the cloud computing industry, we hear the word “scaling” a lot. We talk about scaling up resources to meet demand, scaling our teams, and scaling our platforms. What tends to get lost is whether your costs are scaling in proportion to the value you’re delivering. If those two metrics don’t move in tandem, it’s likely you’re leaving money on the table. It’s not enough to simply use the cloud.

The air in the operations world is thick with AI and LLMs. EVERY vendor is rushing to slap an “AI-powered” badge on their product. But here’s the uncomfortable truth: In high-stakes Kubernetes operations, one bad AI recommendation can destroy months of trust-building in an instant. We aren’t building a chatbot to suggest recipes. We are building systems that, armed with kubectl permissions, have the potential to take down production with a single, wrong command.

If you’ve already worked through cloud cost optimization strategies, the fundamentals aren’t new. CloudZero’s State of Cloud Cost report shows that cloud cost optimization is now a priority for most organizations. We’ve also covered these foundations in depth, including how cloud cost optimization works in practice and how FinOps teams approach cost accountability. What’s less discussed is what happens next. Cloud environments don’t stand still. Architectures change.

In the last few months we've made some changes to VMs that finally allow installing and running Windows on them. MS Paint on Cycle is finally a reality. To make Windows VMs work, we had to add a few things to the platform to support it. As always with Windows, there are some quirks, gotchas, and pain points. But in the guide below, I'll show you how we solved these issues in our recent platform update, and how to install and run a Windows Server 2025 VM on Cycle with full network connectivity.

Recently, we introduced autonomous validation as a new approach to CI/CD that brings adaptive, context-aware intelligence into the delivery pipeline. As AI increases both the volume and reach of code changes, teams are seeing more failures, longer queues, and rising maintenance costs. Traditional pipelines simply weren’t built for this level of velocity or variability.