Private Nodes complete vCluster’s tenancy spectrum: tenants connect their own nodes to a centrally managed control plane for full isolation, custom runtimes (CRI/CNI/CSI), and consistent performance, ideal for AI/ML, HPC, and regulated environments. Learn how it works and what’s next with Auto Nodes.

Is your Kubernetes cluster slowing down under load? etcd doesn’t scale well with multi-tenancy or 30k+ objects. This blog shows how virtual clusters offer an easier, safer way to isolate tenants and scale your control plane, no sharding required.

In this blog, we explore why covering the full Kubernetes tenancy spectrum is essential, and how vCluster’s upcoming Private Nodes feature introduces stronger isolation for teams running production, regulated, or multi-tenant environments without giving up Kubernetes-native workflows.

OpenAI’s outage revealed what happens when etcd breaks at scale. This post explains why sharding isn’t enough, and how vCluster offloads API load with virtual control planes. Benchmark included.

KubeCon + CloudNativeCon India 2025 is back in Hyderabad on August 6–7! With so many exciting sessions, it can be hard to choose. Here are 5 standout talks you shouldn't miss, from real-world Kubernetes meltdowns to scaling GitOps at Expedia, and even why Kubernetes is moving to NFTables.

NVIDIAScape (CVE-2025-23266) is a critical GPU-related vulnerability that allows attackers to break out of containers and gain root access. While some respond by layering in virtual machines, this blog walks through a better approach, how vNode uses container-native sandboxing to neutralize such attacks at the kernel level without sacrificing performance. Includes a step-by-step replication of the exploit, and a demo of how vNode prevents it.

Shared clusters are cost-effective, but when it comes to building and testing Kubernetes controllers, they create bottlenecks, from CRD conflicts to governance issues. This blog breaks down the trade-offs between shared, local, and dedicated clusters and introduces virtual clusters as the scalable solution for platform teams.

As AI and ML workloads scale rapidly, GPUs have become essential, and expensive resources. But most teams underutilize them. This blog dives into how GPU sharing in Kubernetes can help platform teams increase efficiency, cut costs, and better support AI infrastructure.

AI’s rapid growth is fueling a silent climate problem: idle infrastructure. This blog explores why multi-tenancy is key to scaling AI sustainably and how vCluster helps teams reduce waste while moving faster.

Managing AI workloads on bare metal Kubernetes with GPUs presents unique challenges, from weak namespace isolation to underutilized resources and operational overhead. This blog explores the pitfalls of namespace-based multi-tenancy, why running a separate cluster per team is expensive, and how vCluster enables secure, efficient, and autonomous GPU sharing for AI teams.

Running AI or ML workloads on Kubernetes? This tutorial walks you through setting up a GPU enabled GKE cluster, from configuring GPU quotas and node pools to testing workloads and optimizing for multi-team GPU usage with vCluster.

Discover why enterprises in 2025 are shifting from traditional VMware based virtual machines to modern, Kubernetes-native architectures. Learn how adopting Kubernetes closer to bare metal simplifies infrastructure, reduces costs, and enhances scalability and efficiency.