Build your GPU as a Service Kubernetes offering with strong tenant isolation and bare metal speed. vCluster virtualizes the K8s control plane itself, giving every tenant a real API server without provisioning separate physical clusters.
Standard Kubernetes forces painful choices between isolation, performance, and speed to market.
Selling raw GPU compute alone commoditizes your offering. Customers want the cloud experience, not just specs.
Tenants can see platform internals they should not. Namespace isolation shares blast radius across your entire fleet.
Based on typical platform engineering experience, building a GPU cloud platform yourself takes 6 to 10 engineers, 6 to 12 months, and over a million dollars.
vCluster virtualizes the Kubernetes control plane itself, running CNCF-certified tenant clusters as lightweight pods on your GPU infrastructure. Every tenant gets their own API server, etcd, and RBAC with bare metal performance preserved. Proven across 100K+ GPU nodes and 50+ GPU clouds.
From bare metal provisioning to isolated tenant clusters and pre-validated AI environments, vCluster covers the full stack.
Each tenant gets a fully isolated, CNCF-certified Kubernetes cluster running as a lightweight pod on your host infrastructure. Spin up hundreds of tenant environments in seconds with near-zero marginal cost.
Automate the full lifecycle from GPU rack to production Kubernetes with PXE boot, OS installation, machine registration, and network configuration. No manual steps, no intermediate dependencies.
Assign fully dedicated physical nodes to each tenant with their own CNI and CSI. No workloads from other tenants share the hardware, eliminating noisy-neighbor GPU contention at the infrastructure level.
Turn a bare Kubernetes cluster into a production AI platform in minutes with pre-validated integrations for Run:AI, Ray, and Jupyter. Certified to work with vCluster tenant isolation without custom configuration.
Give your customers an EKS-like self-service experience to provision and manage their own Kubernetes environments. Deliver the cloud experience your AI customers expect without building a portal from scratch.
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, meaning each tenant gets a real API server, etcd, and RBAC running as a lightweight pod on shared GPU infrastructure. Competing approaches either provision full physical clusters per tenant — which is expensive and slow — or use namespace isolation, which provides weak security boundaries. vCluster delivers strong tenant isolation at bare metal GPU speed without the cost of separate physical clusters per customer.
Yes. vCluster includes vMetal, a bare metal provisioning layer that handles PXE boot, OS installation, machine registration, and network configuration. vCluster Standalone also runs as a single binary directly on bare metal with no dependency on k3s, kubeadm, or any external Kubernetes distribution. This gives GPU cloud providers a complete path from raw hardware to fully isolated tenant Kubernetes clusters without intermediate layers.
vCluster offers a flexible isolation spectrum. Tenants can share nodes with namespace-level boundaries, get dedicated physical nodes with their own CNI and CSI, or run control planes in dedicated VMs for OS-level separation. For workload-level isolation, vNode (currently in private beta) adds kernel-native security using seccomp, cgroups, namespaces, and AppArmor without hypervisor overhead, preserving bare metal GPU performance while preventing container breakouts.
Boost Run launched a managed Kubernetes offering in under 45 days using vCluster. Lintasarta launched Indonesia's leading GPU cloud in 90 days with 170+ isolated tenant clusters using the same platform. The full stack from bare metal provisioning through tenant cluster orchestration to AI environments is pre-integrated, eliminating months of platform engineering work typically required to build this from scratch.
Yes. vCluster is named in the NVIDIA DGX SuperPOD reference architecture, confirming compatibility with high-density NVIDIA GPU deployments. The platform is also cited in the SemiAnalysis ClusterMax evaluation criteria. Tenant clusters on vCluster are CNCF-certified Kubernetes distributions, meaning all standard GPU operator tooling, CUDA workloads, and Kubernetes-native AI frameworks run without modification.
vCluster includes Certified Stacks — pre-validated integrations with Run:AI, Ray, and Jupyter that turn a bare Kubernetes cluster into a production AI platform in minutes. Slurm-on-Kubernetes is also supported via a Slinky integration for teams migrating from or running hybrid HPC and Kubernetes environments. All certified stacks are tested against vCluster tenant isolation so they run in isolated environments without additional configuration.
See how vCluster powers tenant isolation and bare metal Kubernetes for GPU clouds.
