Trusted by the fastest-growing AI cloud providers
Why Traditional Kubernetes Falls Short
Scaling GPU tenants with standard Kubernetes forces painful tradeoffs between isolation, cost, and speed.
Namespace Isolation Is Too Weak
Tenants can see platform internals they should not: cluster-wide agents, other tenants' nodes and pods, shared blast radius.
Full Clusters Are Too Expensive
Provisioning separate physical clusters per tenant drives up costs, slows onboarding, and wastes GPU capacity at scale.
DIY Takes Too Long
Building a GPU cloud platform in-house requires significant engineering investment.
One Virtual Control Plane Per Tenant, at Scale
vCluster virtualizes the Kubernetes control plane itself, running CNCF-certified tenant clusters as lightweight pods inside a control plane cluster. Every tenant gets a real API server, etcd, scheduler, and full cluster-admin without a single additional physical machine. Production-proven across 100K+ GPUs, 50+ GPU clouds, and Fortune 500 customers.