Launch a competitive GPU as a service offering in weeks. vCluster Platform deploys hundreds of fully isolated, CNCF-certified tenant clusters on shared bare metal with near-zero marginal cost per tenant.
GPU providers stall competing on specs alone while customers demand cloud-grade managed Kubernetes experiences.
Customers don't just want raw compute. They expect self-service environments, managed Kubernetes, and cloud-native tooling from day one.
Namespace isolation leaves tenants exposed to platform internals and each other. Separate physical clusters are too expensive to scale.
Based on industry experience, building a GPU cloud platform typically requires 6 to 10 engineers, 6 to 12 months, and over a million dollars. Most teams we've spoken with are still building two years in.
vCluster Platform virtualizes the Kubernetes control plane itself, giving every tenant their own API server, etcd, RBAC, and CRDs as lightweight pods on shared bare metal. Boost Run launched a GPU as a service offering in less than 45 days. Lintasarta launched Indonesia's leading GPU cloud in 90 days with 170+ tenant clusters.
From zero-touch bare metal provisioning to isolated tenant clusters and pre-validated AI environments, vCluster covers the full stack.
PXE boot, OS installation, machine registration, and network automation handled automatically. Go from GPU rack to production-ready infrastructure without manual intervention at every step.
Every tenant gets a fully isolated Kubernetes control plane running as a lightweight pod. Own API server, etcd, scheduler, and RBAC on shared bare metal hardware with no physical cluster per tenant.
vNode (currently in private beta) gives each workload its own secure runtime using seccomp, cgroups, namespaces, and AppArmor. Container breakout protection at bare metal GPU performance with no hypervisor overhead.
Pre-validated environments for Run:AI, Ray, and Jupyter turn a bare Kubernetes cluster into a production AI platform in minutes. Skip weeks of integration work and deliver managed AI tooling from launch.
Give end customers an EKS-like self-service portal to provision their own isolated environments on demand. Your GPU as a service offering matches the cloud experience AI teams already expect.
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
GPU as a service means providing customers on-demand access to GPU compute with cloud-grade management on top. Kubernetes has become the standard orchestration layer for AI workloads, so GPU cloud providers are expected to offer managed Kubernetes alongside raw compute. vCluster Platform lets you deliver fully isolated, CNCF-certified tenant clusters on your bare metal GPU infrastructure so every customer gets the cloud experience they expect without you provisioning a separate physical cluster per tenant.
Boost Run launched their managed Kubernetes service in less than 45 days using vCluster Platform. Lintasarta launched Indonesia's leading GPU cloud in 90 days with 170+ tenant clusters. The platform handles bare metal provisioning, tenant cluster orchestration, and workload isolation in one integrated stack, so your engineering team focuses on differentiation rather than rebuilding infrastructure primitives from scratch.
Each tenant receives a fully isolated Kubernetes control plane running as a lightweight pod with its own API server, etcd, RBAC, and CRDs. On the workload side, vNode (currently in private beta) adds kernel-native isolation using seccomp, cgroups, namespaces, and AppArmor to prevent container breakouts without adding hypervisor overhead. This means you get strong tenant isolation across the full stack while preserving bare metal GPU performance.
Yes. vCluster Platform offers a flexible isolation spectrum ranging from shared nodes to private nodes with fully dedicated physical hardware per tenant. Private nodes give each tenant their own CNI and CSI with no workload overlap from other tenants, making it suitable for GPU customers who require complete hardware isolation for performance or compliance reasons.
Yes. Certified Stacks are pre-validated AI environments that include Run:AI, Ray, and Jupyter, turning a bare Kubernetes cluster into a production AI platform in minutes. These environments are tested and certified to work with vCluster tenant isolation, so AI platforms run in isolated tenant environments without requiring custom integration work from your team.
vMetal handles zero-touch provisioning for GPU servers including PXE boot, OS installation, machine registration, and network automation. vCluster Standalone runs as a single binary directly on Linux with no dependency on k3s, kubeadm, or any external Kubernetes distribution as a base layer. vCluster is named in the NVIDIA DGX SuperPOD reference architecture and powers 100K+ GPU nodes across 50+ GPU clouds and Fortune 500 customers.
See how vCluster powers GPU as a service for 50+ GPU clouds and Fortune 500 customers.
