Deliver fully isolated, CNCF-certified tenant clusters on your GPU infrastructure. vCluster virtualizes K8s control planes so every tenant gets their own API server without provisioning separate physical clusters.
Standard Kubernetes forces a painful choice between strong tenant isolation and operational efficiency.
Standard Kubernetes namespace isolation exposes cluster-wide agents and other tenants' nodes to workloads that should never see them.
Provisioning a dedicated cluster per tenant multiplies hardware, operational overhead, and time to revenue at every new customer.
Building a private GPU cloud Kubernetes platform in-house takes 6 to 10 engineers, 6 to 12 months, and over one million dollars.
vCluster virtualizes the Kubernetes control plane itself, running fully isolated, CNCF-certified tenant clusters as lightweight pods inside your host cluster. Every tenant gets a dedicated API server, etcd, and RBAC on your private GPU cloud Kubernetes infrastructure with near-zero marginal cost per tenant. Production-proven across 100K+ GPU nodes and 50+ GPU clouds.
A complete stack from bare metal provisioning to tenant cluster orchestration to kernel-native workload isolation, purpose-built for GPU cloud providers.
Each tenant on your private GPU cloud Kubernetes environment gets a fully dedicated control plane running as a pod. Own API server, etcd, scheduler, and RBAC. Spins up in seconds with near-zero overhead per tenant.
PXE boot, OS installation, machine registration, and network automation handled automatically. vMetal takes GPU racks from physical delivery to production-ready Kubernetes nodes with no manual intervention.
Assign dedicated physical GPU nodes to a tenant with their own CNI and CSI. No other tenant workloads share the hardware. Complete hardware-level isolation for your most demanding enterprise customers.
vNode (currently in private beta) places every workload in its own secure runtime using seccomp, cgroups, namespaces, and AppArmor. Container breakout protection at bare metal GPU performance with no hypervisor tax.
Turn a bare tenant cluster into a production AI platform in minutes with pre-validated environments for Run:AI, Ray, and Jupyter. Skip weeks of integration work and deliver managed AI services on day one.
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
vCluster virtualizes the Kubernetes control plane itself rather than managing separate physical clusters. Each tenant on your private GPU cloud Kubernetes infrastructure gets a real, CNCF-certified API server running as a lightweight pod inside a single host cluster. This means you avoid the cost and provisioning delay of physical clusters while still giving every tenant full cluster-admin, their own CRDs, and complete RBAC isolation. Over 40 million tenant clusters have been created using this approach in production.
vCluster offers a full isolation spectrum: shared nodes with resource quotas, private nodes with dedicated hardware and separate CNI and CSI, and dedicated nodes that eliminate noisy-neighbor GPU contention. At the workload level, vNode (currently in private beta) adds kernel-native isolation using seccomp, cgroups, and AppArmor to prevent container breakouts without introducing hypervisor overhead. GPU performance is preserved at the bare metal level throughout.
Yes. vCluster Standalone runs as a single binary directly on Linux bare metal with no dependency on k3s, kubeadm, or any existing Kubernetes base layer. vMetal extends this with zero-touch provisioning: PXE boot, OS installation, machine registration, and network automation from rack to production. Lintasarta launched Indonesia's leading GPU cloud in 90 days using this stack with 170 or more tenant clusters in production.
Boost Run launched a managed Kubernetes offering in under 45 days with zero new platform engineering hires using vCluster. The full stack from bare metal provisioning through tenant cluster orchestration to AI-ready environments is designed to compress what traditionally takes 6 to 12 months of internal engineering into weeks, avoiding the multi-million dollar build cost of a DIY private GPU cloud Kubernetes platform.
Every tenant cluster provisioned by vCluster is a CNCF-certified Kubernetes distribution with 100 percent API compatibility. Enterprise customers get the same Kubernetes API they use on AWS EKS or Google GKE, with full cluster-admin access to install their own CRDs and configure RBAC. vCluster is also named in the NVIDIA DGX SuperPOD reference architecture and in SemiAnalysis ClusterMax evaluation criteria.
vCluster powers infrastructure for more than 50 GPU clouds and Fortune 500 customers, including CoreWeave and Nscale on the GPU cloud side and JPMorganChase and Adobe on the enterprise side. The platform has proven scale across 100 thousand or more GPU nodes and 1 million or more CPU nodes in production, underpinning managed Kubernetes services and internal AI factory deployments worldwide.
See how vCluster powers tenant clusters for GPU cloud providers worldwide.
