Operations | Monitoring | ITSM | DevOps | Cloud

Kubernetes has become a standard foundation for modern containerized applications, but operating clusters still requires significant engineering work. In the CNCF’s 2025 annual cloud native survey, published in January 2026, 82% of container users reported running Kubernetes in production. As adoption matures, the question for many teams is no longer whether to use Kubernetes, but how much of the operational burden they want to own.

Consider a platform team that spends four months building a thorough migration plan. Its pilot, a stateless order-status API, runs on AWS within three weeks. Six months later, that API is still the only workload in the cloud. In this scenario, the blockers are not exotic technical failures.

Heroku is not shutting down. On February 6, 2026, Heroku CPO Nitin T Bhat announced that the platform was moving to a sustaining engineering model: new feature development has stopped, but Heroku remains actively supported and production-ready. There is no announced EOL date or migration deadline, existing apps can keep running, credit-card customers can continue using the service, and existing Enterprise customers can renew. What changed is the roadmap, not immediate availability.

DevOps platform feature lists increasingly look alike. CI/CD, multi-cloud, observability, GitOps, and AI-friendly automation can all appear as checkboxes while the implementation burden still falls on your team. The useful signal appears when you ask how each capability actually works, what evidence a vendor can show, and which parts your team still has to build. Can pipelines authenticate with a scoped machine identity? Can workloads reach cloud APIs without static keys?

Most teams that say they have an enterprise cloud strategy mean that they have workloads running on a hyperscaler and a set of compliance controls documented somewhere. That is a cloud deployment, but it is not necessarily an enterprise cloud operating model. Google Cloud’s 2025 platform engineering research with ESG describes platform engineering as a strategic discipline tied to delivery, governance, and operational outcomes, rather than simply another infrastructure cost.

Your pipeline works. The build passes, the image lands in the registry, and the job turns green. Then someone asks why production still runs the old version, and you spend the next two hours determining whether the problem is a deployment configuration, a runner, an approval rule, or the runtime itself. That is the wrong-tool tax.

Control Plane is now a technology partner of Akamai Cloud. You can add regions from Akamai Cloud to your Global Virtual Cloud in Control Plane. And by leveraging Control Plane’s Universal Cloud Identity, workloads running on Akamai Cloud regions can still consume any service of AWS, GCP, or Azure with no credentials embedded in the workload.

Kubernetes has changed the way teams deploy and scale applications, but many organizations overlook a critical aspect of running it well: resource utilization. Kubernetes excels at scheduling and scaling workloads. Making sure those workloads actually use the resources they reserve still falls on operations teams.

Most DR plans have a fatal assumption baked in, namely that the cloud provider stays up. Your runbook says “failover to us-west-2.” That works until the provider’s control-plane services degrade and the credentials, APIs, or DNS management your failover workflow depends on become unreliable. Single-provider DR is disaster deferral.

Multi-cloud architecture protects engineering organizations from three compounding liabilities that single-cloud stacks accumulate silently: a narrowing hiring pool, data residency exposure that surfaces the moment you sign a regulated enterprise customer, and eroded pricing power at exactly the point your cloud spend is accelerating fastest. Redundancy is a side benefit, not the reason to adopt it. The redundancy argument for multi-cloud is the wrong argument to make early.