Build your AI cloud on bare metal with fully isolated, CNCF-certified tenant clusters. vCluster virtualizes the Kubernetes control plane so you can run hundreds of AI workloads without provisioning separate physical clusters.
Scaling Kubernetes for AI workloads forces painful choices most platforms cannot resolve.
Namespace isolation is too weak for GPU tenants. Separate physical clusters are too expensive and slow to provision at scale.
Based on vCluster customer interviews, building a GPU cloud platform typically requires 6 to 10 engineers, 6 to 12 months, with total investment often exceeding a million dollars.
AI teams have used AWS and GCP. They expect self-service environments and managed Kubernetes, and they will leave if you cannot deliver it.
vCluster virtualizes the Kubernetes control plane, giving every tenant their own API server, etcd, and RBAC as a lightweight process on shared GPU infrastructure. From bare metal provisioning with vMetal to kernel-native workload isolation with vNode (private beta), the complete stack powers 100K+ GPU nodes across 50+ GPU clouds and Fortune 500 customers.
Every layer of the stack is purpose-built for running AI workloads on shared GPU infrastructure with strong tenant isolation and no performance loss.
Each tenant running AI workloads gets a fully isolated Kubernetes cluster with its own API server, etcd, and RBAC as a lightweight pod. Spin up in seconds with near-zero marginal cost per tenant on shared GPU hardware.
Pre-validated AI environments with Run:AI, Ray, and Jupyter turn a bare Kubernetes cluster into a production AI platform in minutes. Slurm-on-Kubernetes is supported via Slinky for teams running hybrid workloads.
vMetal handles PXE boot, OS installation, network automation, and full GPU server lifecycle management. Go from rack to production-ready Kubernetes for AI workloads without manual steps or external dependencies.
vNode (currently in private beta) provides container breakout protection using seccomp, cgroups, namespaces, and AppArmor without any hypervisor overhead. AI workloads retain bare metal GPU performance while running in strongly isolated runtimes.
Auto Nodes automatically provisions bare metal GPU nodes via Terraform when tenants schedule AI workloads. Eliminate idle capacity and scale GPU infrastructure dynamically as demand changes across your tenant base.
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.
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Go live with a hyperscaler-grade tenant experience in days
vCluster virtualizes the Kubernetes control plane itself. Each tenant gets a fully isolated cluster with its own API server, etcd, scheduler, RBAC, and CRDs running as a lightweight process inside a shared host cluster. This means tenants on the same GPU nodes cannot see each other's workloads, platform internals, or resources. For deeper workload isolation, vNode (currently in private beta) adds kernel-native security using seccomp, cgroups, namespaces, and AppArmor without any hypervisor overhead, preserving bare metal GPU performance.
Standard Kubernetes forces you to choose between weak namespace isolation and expensive separate physical clusters for each tenant. vCluster eliminates that tradeoff by virtualizing the control plane. You get hundreds of fully isolated, CNCF-certified tenant clusters on shared GPU infrastructure at near-zero marginal cost per tenant. The full product stack also covers bare metal provisioning via vMetal and pre-validated AI environments via Certified Stacks, so you are not assembling disparate tools.
Yes. vCluster Standalone runs as a single binary directly on bare metal Linux with no dependency on k3s, kubeadm, or any external Kubernetes distribution. vMetal handles zero-touch bare metal provisioning covering PXE boot, OS installation, network automation, and full GPU server lifecycle management. This means you can go from a rack of GPU servers to production-ready Kubernetes for AI workloads without standing up an intermediate infrastructure layer.
vCluster Certified Stacks include pre-validated integrations with Run:AI, Ray, and Jupyter. Slurm-on-Kubernetes is supported via the Slinky integration for teams running hybrid Slurm and Kubernetes AI workloads. Each AI platform runs inside an isolated tenant cluster, so multiple teams or customers can run different AI environments on the same physical GPU infrastructure without interference.
Boost Run launched a managed Kubernetes service in less than 45 days using vCluster. Lintasarta launched Indonesia's leading GPU cloud in 90 days with 170 or more tenant clusters. Based on experience with GPU cloud operators, a DIY approach typically requires 6 to 12 months and 6 to 10 engineers. vCluster delivers the complete path from bare metal provisioning to tenant cluster orchestration to workload isolation in one integrated stack.
Yes. Every tenant cluster created by vCluster is a CNCF-certified Kubernetes distribution with 100 percent API compatibility. Tenants can install their own CRDs, configure RBAC, and use any standard Kubernetes tooling without restrictions. vCluster also integrates with GitOps and IaC workflows including Terraform, Argo CD, and CI/CD pipelines, so your existing automation works without modification.
See how vCluster powers AI workloads across 50 or more GPU clouds and Fortune 500 customers.
