Tech Blog by vCluster

New machine management layer joins vCluster to deliver the industry’s first unified AI infrastructure stack, from bare metal to AI-ready environments.

vCluster Labs today introduced vMetal, a new bare metal machine management layer designed to help Neocloud providers and AI factories provision and operate GPU infrastructure at scale — automating everything from initial provisioning to upgrades and repurposing with cloud-like automation.

Partnership pairs Polarise's sovereign European GPU cloud with vCluster Labs' Kubernetes multi-tenancy platform to rapidly deliver fully isolated AI compute to enterprise customers.

Polarise and vCluster Labs announce a strategic partnership pairing sovereign European GPU cloud with Kubernetes virtual cluster technology to give mid-market enterprises rapid access to isolated, sovereign AI compute — no vendor lock-in, no data sovereignty trade-offs.

Most GPU data centers take months to operationalize. vMetal gets you from rack to running cluster in minutes.

Buying GPUs is the easy part. Operating them like a cloud platform is what separates Neoclouds that launch from ones that stall. vMetal automates the full bare metal lifecycle — discovery, provisioning, cluster attachment — so you can start delivering GPU capacity immediately.

A web dashboard for all your local vind clusters with projects, team management, role-based access, and automation through access keys

The CLI is great for daily dev work. But when you need to see all your clusters at a glance, organize them into projects, manage team access, or hand off a demo environment, the vCluster Platform UI takes vind from a better KinD to a real cluster management platform. Day 7 of the 7 Days of vind series.

Pause clusters and resume where you left off, pull locally built images in 45ms, install Cilium, and map custom ports to your local cluster

With KinD, you delete your cluster at the end of the day and recreate it tomorrow. With vind, you pause it and resume exactly where you left off. Deployments, services, PVCs, all still there. Day 6 covers sleep/wake, registry proxy, custom CNI, and more.

Drop setup-kind from your GitHub Actions and get built-in registry proxy, automatic log export, and multi-cluster CI workflows with setup-vind

If you're using setup-kind in GitHub Actions, you're still loading images manually and missing automatic log exports. setup-vind is a drop-in replacement with built-in registry proxy, automatic artifact export, and multi-cluster support. Day 5 of the 7 Days of vind series.

Run your control plane locally in Docker, join a real cloud VM as a worker node over VPN, and schedule pods across both

Local Kubernetes tools stop at your laptop. vind doesn't. Join a GCP Compute Engine instance as a real worker node to your local cluster over an encrypted VPN tunnel. Test GPU workloads, mixed architectures, and hybrid setups. Day 4 of the 7 Days of vind series.

Create a multi-node local cluster in Docker and test pod distribution, node drains, affinity, and anti-affinity, just like production

Single-node clusters can't test scheduling. With vind, spin up a 4-node cluster in Docker, deploy across workers, drain nodes, and test affinity rules, real Kubernetes behavior on your laptop. Day 3 of the 7 Days of vind series.

Install vind, create a local Kubernetes cluster, and deploy nginx with a working LoadBalancer — in under 3 minutes

KinD needs MetalLB for LoadBalancer services. vind has it built in. In Day 2 of the 7 Days of vind series, we walk through creating a cluster, deploying nginx, and hitting a real LoadBalancer IP, all running in Docker on your laptop.

KinD works until it doesn't. vind picks up where it leaves off.

KinD works, until you need LoadBalancer services, multi-node setups, or the ability to pause and resume clusters. vind gives you a production-like local Kubernetes experience in Docker with features KinD simply doesn't have. Day 1 of the 7 Days of vind series.

Exemplar status isn't about who has the most H100s. It's about who can operate AI infrastructure with measurable, repeatable, production-grade excellence.

GPU capacity alone won't get you Exemplar status. NVIDIA is now rewarding operational maturity, reproducible benchmarking, efficient multi-tenancy, and scalable Kubernetes operations. Here's what NVIDIA Cloud Partners need to architect to qualify.

Empower your engineering teams with a self-service Kubernetes platform to streamline development workflows and enhance productivity.

Discover how to build an internal Kubernetes platform that enables developers to create and manage their own namespaces, reducing operational overhead and accelerating development cycles. Learn about the benefits of self-service environments, improved resource utilization, and enhanced developer autonomy.

How vNode delivers VM-level isolation for containerized AI workloads — without the VM overhead

A three-line Dockerfile broke container security. CVE-2025-23266 exposed 37% of cloud environments running AI workloads, giving attackers full root access to Kubernetes nodes. VMs are too heavy, gVisor can't catch it. vNode offers a third option: container-native isolation that's as strong as VMs but as lightweight as containers.

An open-source alternative to KinD with native LoadBalancer support, free UI, pull-through caching, and the ability to attach external nodes to your local cluster

Kubernetes developers have long relied on tools like KinD (Kubernetes in Docker) to spin up disposable clusters locally for development, testing, and CI/CD workflows. While KinD is a solid tool, it has limitations like not being able to use service type LoadBalancer, accessing homelab clusters from the web, or adding GPU nodes to your local cluster. Introducing vind (vCluster in Docker) - an open source alternative that enables Kubernetes clusters as first-class Docker containers, offering improved performance, modern features, and an enhanced developer experience.

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.

How vCluster provides more security than vanilla Kubernetes when using nodes/proxy permissions for monitoring stacks

A security researcher recently disclosed that Kubernetes nodes/proxy permissions can be exploited for remote code execution. Kubernetes labeled it "working as intended" and issued no CVE. Since vCluster was mentioned in the disclosure, we investigated how this vulnerability affects our users. The conclusion: vCluster is not compromised and actually provides more security than vanilla Kubernetes when using features that require the nodes/proxy permission.

A free tier that makes advanced Kubernetes multi-tenancy accessible—without trials or sales gates.

We’re launching vCluster Free to make advanced Kubernetes multi-tenancy available to more builders.

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.

A 5-stage maturity model for evolving your GPU infrastructure from basic clusters to production-ready AI factories with secure multi-tenancy

Most enterprises start with basic GPU clusters and hit scaling walls fast. This guide walks through the 5-stage journey from manual provisioning to a fully automated AI factory, covering multi-tenancy, cost optimization, and how to achieve cloud-like operations on-prem without vendor lock-in.

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.

Streamline MLOps and Multi-tenancy by Running Isolated GPU Workloads with Virtual Clusters

Scale your AI infrastructure without the overhead. In this hands-on tutorial, we demonstrate how to use vCluster technology to virtualize GPU resources, ensuring secure multi-tenancy and efficient resource sharing for production-grade MLOps.

Why MPS and MIG fall short—and how virtual clusters deliver isolation without hardware lock-in

GPUs are expensive, but most organizations only achieve 30-50% utilization. The problem? GPUs weren't designed for sharing. Software solutions like MPS lack isolation. Hardware solutions like MIG lock you into specific vendors. vCluster takes a different approach—solving GPU multitenancy at the Kubernetes orchestration layer.

Introducing the first vCluster Ambassadors shaping the future of Kubernetes multi-tenancy and platform engineering

Meet the first vCluster Ambassadors - community leaders and practitioners advancing Kubernetes multi-tenancy, platform engineering, and real-world developer platforms.

Explore time-slicing, MIG, and custom workarounds for sharing GPUs across Kubernetes workloads—plus their trade-offs for isolation and management.

GPUs are expensive and rarely used to full capacity. Learn the standard methods for sharing GPUs in Kubernetes—time-slicing and MIG—along with DIY alternatives, and discover why combining these techniques with vCluster delivers the isolation multi-tenant AI/ML workloads demand.

How to design, build, and operate a cost-effective private cloud infrastructure for enterprise AI at scale

Public clouds are convenient for AI experimentation, but production workloads often hit walls. For enterprises running continuous training and inference, a private cloud can deliver better ROI, data sovereignty, and performance. This comprehensive guide walks through architecting a private cloud for AI workloads from the ground up.

How to deploy Karpenter on Google Cloud Platform (and why you might want vCluster Auto Nodes instead)

Karpenter has become the gold standard for Kubernetes autoscaling on AWS, but what about GCP? This guide shows you how to set it up and introduces a better way.

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.

What the AI Infrastructure 2025 Survey Reveals, And How Platform Teams Can Respond

The latest AI Infrastructure 2025 survey shows that most organizations are struggling not due to GPU scarcity, but because of poor GPU utilization caused by limited multi-tenancy capabilities. Learn how virtual clusters and virtual nodes help platform teams solve high costs, sharing issues, and low operational maturity in Kubernetes environments.

Announcing the Infrastructure Tenancy Platform for NVIDIA DGX—plus what we learned from 100+ conversations at KubeCon about GPU efficiency, isolation, and the future of AI on Kubernetes.

KubeCon Atlanta 2025 was packed with energy, launches, and conversations that shaped the future of AI infrastructure. At Booth #421, we officially launched the Infrastructure Tenancy Platform for NVIDIA DGX—a Kubernetes-native platform designed to maximize GPU efficiency across private AI supercomputers, hyperscalers, and neoclouds. Here's what happened, what we announced, and why it matters for teams scaling AI workloads.

New platform combines virtual clusters, dynamic GPU autoscaling, and hybrid networking to maximize NVIDIA infrastructure efficiency—from DGX systems to bare metal and cloud.

vCluster Labs announced its Infrastructure Tenancy Platform for AI at KubeCon North America 2025, delivering a Kubernetes-native foundation for running AI workloads on NVIDIA GPU infrastructure. The platform introduces vCluster Private Nodes, Auto Nodes with Karpenter-based autoscaling, vCluster VPN for hybrid networking, and direct integrations with NVIDIA Base Command Manager, KubeVirt, and Netris—helping organizations maximize GPU utilization while maintaining full workload isolation and security across cloud, on-prem, and bare metal environments.

Why virtual clusters aren’t “fake” Kubernetes clusters — they’re the foundation of real multi-tenant platforms built for cloud, on-prem, and bare metal.

Many engineers think “virtual” means “not real.” In Kubernetes, the opposite is true. Virtual clusters run full control planes inside namespaces, giving teams real isolation, faster CI/CD, and multi-tenant efficiency across cloud and on-prem infrastructure with vCluster from Loft Labs.

A practical guide to implementing cloud bursting using vCluster VPN, Private Nodes, and Auto Nodes for secure, elastic, multi-cloud scalability.

Cloud bursting lets you expand compute capacity on demand without overprovisioning or re-architecting your systems. In this guide, we break down how vCluster VPN connects Private and Auto Nodes securely across environments—so you can scale beyond limits while keeping costs and complexity in check.

vCluster Labs and Netris team up to bring cloud-grade Kubernetes automation and network-level multi-tenancy to AI factories and GPU-powered infrastructure.

vCluster Labs has partnered with Netris to revolutionize how AI operators run Kubernetes on GPU infrastructure. By combining vCluster’s Kubernetes-level isolation with Netris’s network automation, the integration delivers a full-stack multi-tenancy solution, simplifying GPU cloud operations, maximizing utilization, and enabling cloud-grade performance anywhere AI runs.

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.

How vCluster and NVIDIA’s KAI Scheduler reshape GPU workload management in Kubernetes - enabling isolation, safety, and maximum utilization.

GPU workloads have become the backbone of modern AI infrastructure, but managing and upgrading GPU schedulers in Kubernetes remains risky and complex. This post explores how vCluster and NVIDIA’s KAI Scheduler together enable fractional GPU allocation, isolated scheduler testing, and multi-team autonomy, helping organizations innovate faster while keeping production safe.

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.

Auto Nodes extend Private Nodes with provider-agnostic, automated node provisioning and scaling across clouds, on-prem, and bare metal.

Kubernetes makes pods elastic, but node scaling often breaks outside managed clouds. With vCluster Platform 4.4 + v0.28, Auto Nodes fix that gap, combining isolation, elasticity, and portability. Learn how Auto Nodes extend Private Nodes with automated provisioning and dynamic scaling across any environment.

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.

A new name, the same mission, building the best Kubernetes tenancy tools for teams everywhere.

Loft Labs is now vCluster Labs, a name that reflects our focus on building the best Kubernetes multi-tenancy and infrastructure engineering tools. The same team, projects, and mission remain, but with a clearer brand aligned to our product, vCluster.

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.

A curated list of impactful, technical, and thought-provoking sessions to catch at KubeCon + CloudNativeCon India 2025 in Hyderabad.

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.

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.

Container breakouts on GPU nodes are real, and just three lines of code can be enough. Discover how vNode neutralizes vulnerabilities like NVIDIAScape without relying on VMs.

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.

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.

How virtual clusters and smarter tenancy models can reduce carbon impact while scaling AI workloads.

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.

Keep your CI pipelines clean and efficient by automating Kubernetes resource cleanup with vCluster and Loft.

Leftover Kubernetes resources from CI jobs can drive up cloud costs and clutter your clusters. This guide shows how to automate cleanup tasks using vCluster, helping you maintain cleaner, faster CI/CD pipelines.

Keep your CI pipelines clean and efficient by automating Kubernetes resource cleanup with vCluster and Loft.

Leftover Kubernetes resources from CI jobs can drive up cloud costs and clutter your clusters. This guide shows how to automate cleanup tasks using vCluster, helping you maintain cleaner, faster CI/CD pipelines.

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.

Step-by-step guide to setting up a GPU-enabled Kubernetes cluster on GKE for scalable AI and ML workloads.

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.

Cut Kubernetes costs by up to 91% using spot instances and vCluster, without compromising workload stability.

Spot instances offer massive savings but come with unpredictability. In this step-by-step guide, learn how to combine them with vCluster to build resilient, cost-effective Kubernetes environments for CI/CD, AI/ML, and more.