Stop choosing between tenant isolation and performance. vCluster creates fully isolated Kubernetes clusters on bare metal, giving every AI cloud tenant their own API server, etcd, and RBAC without provisioning separate physical clusters.
AI cloud providers face three compounding problems that delay revenue and erode margins.
Selling bare metal GPUs alone is a race to the bottom. Customers want the cloud experience, not just raw specs.
Building a GPU cloud platform in-house requires significant engineering investment.
Namespace isolation is too weak. Separate physical clusters are too expensive. Standard Kubernetes forces you to choose.
vCluster delivers the complete infrastructure path for AI cloud providers: zero-touch bare metal provisioning with vMetal, fully isolated CNCF-certified tenant clusters via vCluster, and workload-level isolation through vNode. Proven across 100K+ GPU nodes and 50+ GPU clouds including CoreWeave and Nscale.
Every layer of the stack purpose-built for GPU cloud operators who need tenant isolation without sacrificing bare metal performance.
Each tenant gets a fully isolated Kubernetes control plane running as a lightweight pod. Own API server, etcd, scheduler, and RBAC with no physical cluster provisioning required.
PXE boot, OS installation, machine registration, and Netris-powered network automation handled automatically. Take GPU racks from delivery to production-ready Kubernetes without manual intervention.
vNode wraps every workload in its own secure runtime using seccomp, cgroups, namespaces, and AppArmor. Container breakout protection without hypervisor tax on GPU performance.
When tenants schedule workloads, bare metal GPU nodes are provisioned automatically via Terraform. Scale physical infrastructure on demand without manual datacenter operations.
Give your AI cloud customers a self-service portal to provision and manage their own Kubernetes environments. Deliver the cloud experience AI teams expect from hyperscalers, on your infrastructure.
This isn’t a side project. Behind every vCluster deployment is 5+ years of deep K8s engineering, security hardening, and battle-tested infrastructure work at massive scale.
Talk to our team about your stack
Deploy vCluster on your infra in minutes
Go live with a hyperscaler-grade tenant experience in days
An AI cloud (sometimes called a neocloud) is a provider offering GPU compute as a service, typically built on bare metal infrastructure rather than legacy virtualization. vCluster supports AI cloud providers by delivering the full infrastructure stack from bare metal provisioning to isolated tenant Kubernetes clusters to workload-level isolation. Providers like CoreWeave and Nscale use this stack to offer managed Kubernetes environments on top of their GPU hardware without the cost or complexity of provisioning separate physical clusters per customer.
vCluster virtualizes the Kubernetes control plane itself. Each tenant receives a fully isolated cluster with its own API server, etcd, scheduler, and RBAC running as a lightweight pod inside the control plane cluster. This gives tenants genuine cluster-admin isolation without the cost of dedicated physical clusters. The isolation spectrum leads with Private Nodes as the production default, dedicated worker nodes with hardware-level separation, followed by shared nodes scoped to dev, test, CI/CD, and trusted-team workloads, and vNode for the strongest workload-level isolation, so operators can match isolation strength to tenant requirements.
Boost Run launched their managed Kubernetes service in less than 45 days using vCluster . Lintasarta launched a GPU cloud in Indonesia in 90 days, deploying hundreds of tenant clusters on their infrastructure. The speed advantage comes from vCluster replacing years of custom platform engineering with a production-proven stack that covers bare metal provisioning, tenant cluster orchestration, and Day 2 operations out of the box.
No. vCluster Standalone runs as a single binary directly on Linux bare metal with no external Kubernetes dependency. There is no need for k3s, kubeadm, or k0s as a base layer. This means GPU cloud providers can go from raw hardware to a running CNCF-certified control plane to isolated tenant clusters in one integrated path, without stitching together multiple open-source tools.
vNode provides kernel-native workload isolation using seccomp, cgroups, Linux namespaces, and AppArmor at the individual workload level. It significantly limits container escape from reaching the host kernel or neighboring tenant workloads. Unlike VM-based isolation, vNode adds no hypervisor overhead, preserving near-bare-metal performance. It is compatible with gVisor and Kata Containers for operators who need additional defense-in-depth layers, and it complements vCluster's control plane isolation.
Yes. vCluster is named in the NVIDIA DGX SuperPOD reference architecture. The platform powers 100K+ GPU nodes in production across 50+ GPU clouds and Fortune 500 customers. The CNCF-certified control plane binary ensures each tenant cluster meets full API conformance standards without proprietary lock-in.
See how GPU cloud providers deploy isolated tenant clusters on bare metal in days.
