Build your AI factory Kubernetes platform with robust 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 yourself takes six to twelve months and significant engineering headcount 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. 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, currently in private beta).
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 on shared GPU infrastructure with near-zero marginal cost.
Turn a bare Kubernetes cluster into a production AI factory environment in minutes. Pre-validated 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 (currently in private beta) 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.
Talk to our team about your stack
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 host cluster. This means tenants cannot see each other's workloads, nodes, or platform internals. For workloads requiring deeper isolation, vNode (currently in private beta) 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 zero new platform engineering hires. Lintasarta launched Indonesia's leading GPU cloud in 90 days with over 170 tenant clusters. 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 pre-validated 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. vMetal extends this with zero-touch provisioning of GPU servers including PXE boot, OS installation, and network automation. 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 a CNCF-certified Kubernetes distribution 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 and is referenced in the SemiAnalysis ClusterMax evaluation criteria for GPU cloud providers.
See how vCluster powers AI factory Kubernetes for GPU clouds and enterprises.
