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.
Building a GPU cloud platform in-house takes significant engineering investment.
vCluster Platform virtualizes the Kubernetes control plane itself, giving every tenant their own API server, etcd, RBAC, and CRDs as lightweight pods. For production workloads and untrusted tenants, vCluster Platform's Private Nodes join dedicated worker nodes directly and privately into each tenant cluster with per-tenant CNI and storage, delivering hardware-level isolation. Shared infrastructure is available for dev, test, and trusted-team workloads. Boost Run launched a GPU as a service offering in less than 45 days. Lintasarta launched a GPU cloud in Indonesia in 90 days.
From zero-touch bare metal provisioning to isolated tenant clusters and partner AI environments, vCluster covers the full stack.
PXE boot, OS installation, machine registration, and Netris-powered 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. For production workloads and untrusted tenants, vCluster Platform's Private Nodes join dedicated worker nodes directly and privately into each tenant cluster with per-tenant CNI and storage, delivering hardware-level isolation. Shared infrastructure is available for dev, test, and trusted-team workloads.
vNode 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.
Partner integrations with 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 a GPU cloud in Indonesia in 90 days with hundreds of tenant clusters running on vCluster Platform. 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 adds process-level isolation using seccomp, cgroups, namespaces, and AppArmor to prevent container breakouts and limit blast radius without adding hypervisor overhead. For production deployments, Private Nodes, dedicated worker nodes with per-tenant CNI and storage, deliver hardware-level isolation. This means you get strong tenant isolation across the full stack while preserving near-bare-metal performance.
Yes. vCluster Platform's production default is Private Nodes: dedicated worker nodes giving 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. For production, Private Nodes join dedicated worker nodes directly and privately into each tenant cluster with per-tenant CNI and storage, delivering hardware-level isolation, without the cost of separate physical clusters. Shared nodes are also available for dev, test, CI/CD, and trusted-team workloads.
Yes. Certified Stacks are AI environments that include partner integrations with 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 CNCF-certified control plane 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 supports 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.
