Kubernetes Was Built to Schedule Pods. GPUs Broke the Model.
Kubernetes GPU scheduling is the AI bottleneck. The scheduler was built for pods, not accelerators, and the utilization numbers show it.
Independent reporting on Kubernetes, cloud-native infrastructure, virtualization, storage, and security. For engineers and platform teams.
Kubernetes GPU scheduling is the AI bottleneck. The scheduler was built for pods, not accelerators, and the utilization numbers show it.
Internal developer platform ROI is a finance problem before it is a build problem. DORA shows most teams already have a platform, so prove the return.
Bundling is not unifying. Multicluster Kubernetes management means one control plane across providers, and three vendors get there differently.
Kubernetes cost optimization is not a dashboard problem. Your bill is set by requests, node sizing, and cluster sprawl long before FinOps sees it.
The containerd vs CRI-O debate settled years ago. Both are CNCF Graduated projects, and Docker left the cluster path back in Kubernetes 1.24.
Enterprise auditors rarely stop at your SBOM. A software supply chain audit now asks for signatures, provenance, and build attestations.
An AI pilot to production gap is an infrastructure problem, not a model problem. Data gravity, inference cost, and SLO budgets decide who ships.
Kubernetes for AI breaks the patterns that work for web apps. GPUs, model load times, and a CPU scheduler change the architecture.
Sovereign cloud, data sovereignty, and data residency are three different promises. Here is what each guarantees and what each one leaves uncovered.
Most digital sovereignty strategy stops at data residency. The real test is whether your contract lets you leave, and most contracts fail it.
Independent analysis of cloud-native infrastructure, Kubernetes and data center economics. No vendor spin.