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.

Can you really save millions on Kubernetes infrastructure without compromising performance? Yes, with vCluster. In this blog, we break down how virtual clusters reduce control plane overhead, unlock higher node utilization, and simplify multi-tenancy, all while maintaining lightning-fast performance.

Running multiple tenants on a single Kubernetes cluster can be complex and risky. In this post, Liquid Reply explores how vCluster offers a secure and cost-efficient solution by isolating workloads through lightweight virtual clusters.

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.

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.

Traditional multi-tenancy in Kubernetes often leads to complex RBAC, noisy neighbors, and security headaches. This post breaks down why platform teams struggle with namespace-based isolation, and how virtual clusters offer a better path forward.

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.

What about Rancher? Does vCluster work on Rancher? How do we manage virtual clusters on Rancher? Hey, what about Rancher? These are just a few of the questions we have heard over the last couple of years at KubeCon, on the interwebs, and everywhere in between. The answer was alwa...