Articles

Hybrid AI That Works: Network Isolation, Data Gravity, and Workload Placement in the Real World

For the past two years, the AI infrastructure debate has been framed as binary: go all-in on on-prem GPU estates or stay all-in on the cloud. Neither approach is sustainable at enterprise scale. The winning pattern is intelligent placement—keep sensitive or data-heavy jobs local, burst elastic workloads into the cloud. Success depends on strict isolation, careful placement, and scheduling that is cost-aware from the start.

Practical strategies for securing shared GPU environments with Kubernetes-native isolation, hardware partitioning, and operational best practices

Sharing GPU access across teams maximizes hardware ROI, but multitenant environments introduce critical performance and security challenges. This guide explores proven workload isolation strategies, from Kubernetes RBAC and network policies to NVIDIA MIG and time-slicing, that enable you to build secure, scalable GPU clusters. Learn how to prevent resource contention, enforce tenant boundaries, and implement operational safeguards that protect both workloads and data in production AI infrastructure.

Why AI workloads, GPU infrastructure, and platform scale are forcing Kubernetes teams to rethink multi-tenancy

AI workloads and GPU infrastructure are exposing the limits of traditional Kubernetes multi-tenancy. In 2026, platform teams need a new layer.

Lessons in Intentional Tenancy and Security at Scale from a Cloud Platform Director

If a workload needs isolation, give it its own cluster. It sounds safe, but at scale, this logic breaks down. Learn why consistency, not separation, is the real security challenge in modern Kubernetes environments.

How to safely share expensive GPU infrastructure across teams without sacrificing performance or security

GPUs don't support native sharing between isolated processes. Learn four approaches for running multitenant GPU workloads at scale without performance hits.

How vCluster’s Private Nodes, Auto Nodes, and Standalone releases redefine multi-tenancy for modern Kubernetes platforms.

From hardware-isolated clusters to dynamic autoscaling and fully standalone control planes, vCluster’s latest launch series completes the future of Kubernetes multi-tenancy. Discover how Private Nodes, Auto Nodes, and Standalone unlock new levels of performance, security, and flexibility for platform teams worldwide.

Bootstrapping Kubernetes from scratch, no host cluster, no external dependencies.

Kubernetes multi-tenancy just got simpler. With vCluster Standalone, you can bootstrap a full Kubernetes control plane directly on bare metal or VMs, no host cluster required. This walkthrough shows how to install, join worker nodes, and run virtual clusters on a single lightweight foundation, reducing vendor dependencies and setup complexity for platform and infrastructure teams.

Eliminating the “Cluster 1 problem” with vCluster Standalone v0.29 – the unified foundation for Kubernetes multi-tenancy on bare metal, VMs, and cloud.

vCluster Standalone changes the Kubernetes tenancy spectrum by removing the need for external host clusters. With direct bare metal and VM bootstrapping, teams gain full control, stronger isolation, and vendor-supported simplicity. Explore how vCluster Standalone (v0.29) solves the “Cluster 1 problem” while supporting Shared, Private, and Auto Nodes for any workload.

Dynamic, isolated, and cloud-agnostic autoscaling for every virtual cluster.

vCluster Auto Nodes brings dynamic, Karpenter-powered autoscaling to any environment, public cloud, private cloud, or bare metal. Combined with Private Nodes, it delivers true isolation and elasticity for Kubernetes, letting every virtual cluster scale independently without cloud-specific limits.

Kubernetes pods scale elastically, but node scaling often stops at the provider boundary. Auto Nodes extend Private Nodes to bring elasticity and portability to isolated clusters across clouds, private datacenters, and bare metal.

Pods autoscale in Kubernetes, but nodes don’t. Outside managed services, teams fall back on brittle scripts or costly overprovisioning. With vCluster Platform 4.4 + vCluster v0.28, Auto Nodes close the gap, bringing automated provisioning and elastic scaling to isolated clusters across clouds, private datacenters, and bare metal.

Kubernetes has pods and deployments covered, but when it comes to nodes, scaling breaks down across clouds, providers, and private infrastructure. Auto Nodes change that.

Kubernetes makes workloads elastic until you hit the node layer. Managed services offer partial fixes, but hybrid and isolated environments still face scaling gaps and wasted resources. vCluster Auto Nodes close this gap by combining isolation, just-in-time elasticity, and environment-agnostic portability.

A technical walkthrough of Private Nodes in vCluster v0.27 and how they enable true single-tenant Kubernetes clusters.

Private Nodes in vCluster v0.27 take Kubernetes multi-tenancy to the next level by enabling fully isolated, dedicated clusters. In this deep dive, we walk through setup, benefits, and gotchas, from creating a vCluster with Private Nodes to joining worker nodes and deploying workloads. If you need stronger isolation, simpler lifecycle management, or enterprise-grade security, this guide covers how Private Nodes transform vCluster into a powerful single-tenant option without losing the flexibility of virtual clusters.

Dedicated, tenant‑owned nodes with a managed control plane, full isolation without running separate clusters.

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.

Rethinking Kubernetes scale: avoid the risks of etcd sharding with virtual clusters built for performance, stability, and multi-tenant environments.

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.

Why covering the full Kubernetes tenancy spectrum is critical, and how Private Nodes bring stronger isolation to vCluster

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.

Why sharding etcd doesn’t scale, and how virtual clusters eliminate control plane bottlenecks in large Kubernetes environments.

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.

How vCluster Balances Kubernetes Cost Reduction With Real-World Performance

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.

Unlocking Secure and Scalable Multi-Tenancy in Kubernetes with Virtual Clusters

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.

How shared clusters fall short, and why virtual clusters are the future of controller development.

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.

How Kubernetes can make GPU usage more efficient for AI/ML teams through MPS, MIG, and smart scheduling.

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.

Why Namespace Isolation Falls Short for GPU Workloads, and How Multi-Tenancy with vCluster Solves It

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.

Enable multi-tenant Kubernetes service mesh with zero sidecars and seamless Istio integration using Ambient Mode and vCluster.

The v0.25 release of vCluster brings native support for Istio’s Ambient Mesh, enabling shared service mesh capabilities across multiple virtual clusters without sidecars. This update dramatically reduces resource overhead, simplifies operations, and boosts scalability in multi-tenant Kubernetes environments.

Virtual clusters with container native isolation on bare-metal

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.

Why Moving from VMware to Kubernetes-native Infrastructure is Critical for Modern Enterprises

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.

There's something new on the Ranch

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...

When we first launched vCluster in 2021, our mission was clear: make Kubernetes multi-tenancy easier, safer, and more cost-efficient. Since then, we've helped organizations around the globe manage Kubernetes with greater flexibility and security. But as Kubernetes usage expanded,...

Multi-tenancy in Kubernetes has been an ongoing challenge for organizations looking to optimize their cloud-native infrastructure. Over the years, the approach to multi-tenancy has evolved, from simple namespace isolation to virtual clusters and, more recently, full-fledged inter...

Kubernetes was designed for efficient resource sharing, but complexity escalates when multiple teams or users step into the same cluster. As organizations scale, the challenge isn't just about running workloads; it's about how those workloads coexist securely and efficiently.

Despite being in high demand, the high cost and maintenance of GPU resources pose a problem for providers. A solution that reduces costs and improves efficiency is necessary. Enter multi-tenant Kubernetes. Multi-tenant Kubernetes allows different apps, workloads, and teams to liv...

Navigating the complexities of Kubernetes can often feel like steering through uncharted waters, especially when it comes to ensuring security and managing multi-tenancy. As organizations continue to adopt Kubernetes at an accelerating pace, the need for more robust, scalable, an...