| Kubernetes 1.35 Adds In-Place Pod Resize |
| Tuesday, 06 January 2026 | |||
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The Cloud Native Computing Foundation (CNCF) has announced the release of Kubernetes 1.35, with improvements including vertical scaling via in-place pod resize. Kubernetes is a portable, extensible, open source platform for managing containerized workloads and services. Kubernetes provides service discovery and load balancing to automate load balancing and network traffic distribution. It also provides storage orchestration, and will automatically mount a storage system of your choice, both local and cloud-based. Other advantages of Kubernetes include offers automated rollouts and rollbacks, and bin packing so you provide Kubernetes with a cluster of nodes that it can use to run containerized tasks and it will make the best use of your resources.
Kubernetes 1.35 has the codename "Timbernetes", the world tree release. The world tree theme is inspired by Yggdrasil, the tree of life. The developers say that like any great tree, Kubernetes grows ring by ring and release by release. The main improvement in this version is the vertical scaling enabled by the in-place pod resize. You've always been able to horizontally scale Kubernetes workloads so long as they are stateless. You can spin up more nodes and add additional identical copies of the containerized app. Until now, if you wanted to increase the the memory or CPU capacities of an existing active node, the only way was to create a new pod and move the workload over. The new in-place updates for Pod resources allows users to adjust CPU and memory resources without restarting Pods or Containers. The developers say the new in-place functionality allows for smoother, nondisruptive vertical scaling, improves efficiency, and can also simplify development. Another improvement is to workload aware scheduling. This is described as the first of a tranche of related improvements that together aim to improve scheduling and management of workloads. The team says the effort will span over many releases, with an eventual goal of seamless workload scheduling and management in Kubernetes including, but not limited to, preemption and autoscaling. Kubernetes v1.35 introduces the Workload API that you can use to describe the desired shape as well as scheduling-oriented requirements of the workload. It comes with an initial alpha implementation of gang scheduling that instructs the kube-scheduler to schedule gang Pods in the all-or-nothing fashion. This aims to make sure that a group of interrelated pods, for example, AI/ML training jobs, either happen simultaneously or not at all. There's also improved scheduling of identical Pods (that typically make a gang) to speed up the process thanks to the opportunistic batching feature. A full list of improvements is available on the Kubernetes website.
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