Kubernetes as AI’s operating system: 1.35 release signals

Why v1.35 reads like an AI-infrastructure release The changes that matter for AI/ML operations In-place Pod resize is stable Device allocation keeps moving toward a baseline capability KYAML becomes the default kubectl output format Why AI keeps pushing teams toward a shared operating layer Platform engineering implications Ecosystem note: Ingress NGINX retirement timeline Practical evaluation steps for v1.35 About the author Posted on February 23, 2026 by Angel Ramirez, CEO of Cuemby and CNCF Ambassador CNCF projects highlighted in this post Kubernetes has become the place where teams coordinate mixed production workloads: services, batch jobs, data pipelines, and ML training. The Kubernetes v1.35 (“Timbernetes”) release reinforces that trajectory with changes that reduce operational friction in scheduling, resource control, and configuration workflows. What stands out in v1.35 is practical: fewer restarts for resizing, new primitives for coordinated placement, and safer defaults for how teams generate and review manifests at scale. Taken together, these updates point to a Kubernetes control plane that’s adapting to bursty jobs, tightly coupled training runs, and continuously tuned inference services. Teams operating mixed clusters tend to feel the pressure first in placement efficiency, resize churn, and configuration review hygiene. The rest of this piece focuses on the v1.35 changes that ease those pressures and make AI/ML operations more predictable at scale. Kubernetes v1.35 introduces the workload API and workload-aware scheduling , along with an initial implementation of gang scheduling for “all-or-nothing” placement across a group of Pods. This helps distributed training and tightly coupled jobs avoid partial placement patterns that waste capacity and stall progress. If you want the deeper design context, the upstream proposal lives in the gang scheduling KEP. v1.35 graduates in-place Pod resource resize to Stable. CPU and memory adjustments can happen without restarting containers, which reduces churn in inference services that need fast tuning under load and improves recovery options for long-running workloads. The upstream spec is captured in the in-place update of Pod resources KEP.