Build your AI factory Kubernetes platform with strong tenant isolation. vCluster creates fully isolated, CNCF-certified tenant clusters as lightweight pods on shared GPU infrastructure in seconds.
Standard Kubernetes forces painful tradeoffs that slow your AI factory deployment.
Namespace isolation is too weak. Separate physical clusters per tenant are too expensive and slow to provision at AI factory scale.
Building a GPU cloud platform in-house requires significant engineering investment your team likely does not have.
Tenants can see platform internals they should not, including cluster-wide agents and other tenants' nodes, creating unacceptable security exposure.
vCluster virtualizes the Kubernetes control plane itself, giving every AI factory tenant their own API server, etcd, RBAC, and CRDs as lightweight pods on shared GPU hardware. For production deployments, Private Nodes deliver dedicated worker nodes with per-tenant CNI and storage, providing hardware-level isolation. Proven at 100K+ GPU nodes across 50+ GPU clouds and Fortune 500 customers.
Every layer your AI factory Kubernetes platform needs, from bare metal provisioning to certified AI environments and kernel-native workload isolation (vNode).
Each AI factory tenant gets a fully isolated Kubernetes control plane running as a pod. Own API server, etcd, scheduler, and RBAC, spun up in seconds. For production deployments with untrusted tenants, Private Nodes deliver dedicated worker nodes with per-tenant CNI and storage, giving hardware-level isolation rather than control-plane separation alone.
Turn a bare Kubernetes cluster into a production AI factory environment in minutes. Partner integrations with Run:AI, Ray, and Jupyter mean your tenants get a complete AI platform, not just raw compute.
vMetal handles PXE boot, OS installation, machine registration, and full GPU server lifecycle management. Go from rack to production-ready AI factory Kubernetes with zero-touch provisioning and no manual intervention.
vNode delivers container breakout protection without hypervisor overhead. Each AI workload runs in its own secure runtime using seccomp, cgroups, namespaces, and AppArmor, no hypervisor tax on GPU performance.
Manage your entire AI factory Kubernetes fleet from a central control plane. Built-in observability, updates, backups, disaster recovery, and compliance tooling keep hundreds of tenant clusters operationally sound at scale.
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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Deploy vCluster on your infra in minutes
Go live with a hyperscaler-grade tenant experience in days
An AI factory Kubernetes platform is a managed infrastructure layer that gives multiple teams or customers isolated, production-ready AI environments on shared GPU hardware. Enterprises building internal AI infrastructure and GPU cloud providers selling managed compute to AI teams both need this capability. The challenge is delivering strong tenant isolation at scale without provisioning a separate physical cluster per tenant: which is where vCluster's control plane virtualization approach is purpose-built to help.
vCluster virtualizes the Kubernetes control plane itself. Each tenant receives a fully isolated cluster with its own API server, etcd, RBAC, and CRDs running as lightweight pods inside a shared control plane cluster. For production deployments, Private Nodes deliver dedicated worker nodes per tenant with per-tenant CNI and storage, providing hardware-level isolation. This means tenants cannot see each other's workloads, nodes, or platform internals. For workloads requiring deeper isolation, vNode adds kernel-native security at the runtime level without hypervisor overhead.
Deployment timelines depend on your environment, but production customers have launched quickly. Boost Run launched a managed Kubernetes offering in less than 45 days with a lean platform engineering team. Lintasarta launched a GPU cloud in Indonesia in 90 days with vCluster's full stack. vCluster's full stack from bare metal provisioning through tenant cluster orchestration to certified AI environments is designed to compress what typically takes quarters into weeks.
vCluster's certified stacks include partner integrations with Run:AI, Ray, and Jupyter. These environments are tested and certified to work with vCluster's tenant isolation model, so AI teams get a complete production platform rather than just a Kubernetes cluster. Slurm-on-Kubernetes is also supported via Slinky integration for teams running hybrid or migrating HPC workloads.
Yes. vCluster Standalone runs as a single binary directly on bare metal Linux with no dependency on k3s, kubeadm, or any external Kubernetes distribution. It is a CNCF-certified control plane binary, not a Kubernetes distribution itself. vMetal extends this with zero-touch provisioning of GPU servers including PXE boot, OS installation, and network automation via Netris. This gives AI factory operators a complete path from raw GPU racks to isolated tenant clusters without intermediate dependencies.
Yes. Every tenant cluster created by vCluster is CNCF-certified with 100% API compatibility. Tenants interact with a fully conformant Kubernetes API, not a proprietary or partial implementation. vCluster is also named in the NVIDIA DGX SuperPOD reference architecture
See how vCluster powers AI factory Kubernetes for GPU clouds and enterprises.
