AI clouds demand strong tenant isolation without VM overhead. vCluster combines virtualized control planes with vNode's kernel-native workload isolation to protect every tenant at bare metal speed.
Standard Kubernetes forces a painful tradeoff between security, performance, and cost.
Tenants can see platform internals they should not — cluster-wide agents, other tenants' nodes and pods.
Provisioning full physical clusters per tenant destroys density, slows onboarding, and kills margins.
AI workloads running untrusted code on shared GPUs create real exposure to cross-tenant data leaks.
vCluster delivers a complete isolation spectrum — from virtualized control planes to kernel-native workload security via vNode. Every tenant gets a fully isolated, CNCF-certified Kubernetes cluster without hypervisor overhead, production-proven across 100K+ GPU nodes and 50+ GPU clouds.
Every layer of the stack is designed to give tenants hard boundaries without sacrificing GPU performance or operational efficiency.
vNode (currently in private beta) gives each workload its own secure runtime using seccomp, cgroups, Linux namespaces, and AppArmor — preventing container breakouts without any hypervisor tax on GPU performance.
Each tenant gets their own API server, etcd, scheduler, and RBAC running as lightweight pods — providing real Kubernetes isolation without provisioning separate physical clusters.
Assign tenants their own physical nodes with dedicated CNI and CSI. No workloads from other tenants share the hardware, delivering complete node-level tenant isolation.
vNode's (currently in private beta) defense-in-depth architecture strongly limits workload escape from container boundaries. Compatible with gVisor and Kata Containers for additional layered security where requirements demand it.
Per-tenant network boundaries enforced via VLANs, VXLANs, VRFs, ACLs, and DPU policies through Netris integration — ensuring tenant traffic never crosses boundaries at the network layer.
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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Tenant isolation in Kubernetes means ensuring that each customer's workloads, data, and control plane resources are fully separated from other tenants on shared infrastructure. For AI clouds running GPU workloads, weak isolation creates real risks — container escapes, data leaks, and noisy-neighbor GPU contention. Strong tenant isolation requires separation at the control plane, workload runtime, and network layers simultaneously, which namespace-level partitioning alone cannot provide.
Kubernetes namespaces share a single API server, etcd, and control plane across all tenants — so a misconfiguration or privilege escalation in one namespace can affect others. vCluster gives every tenant a fully isolated API server, etcd, scheduler, and RBAC as a lightweight pod inside the host cluster. This means each tenant has a real Kubernetes cluster boundary, not just a logical partition, eliminating the shared blast radius that namespace isolation creates.
Not with vCluster. Traditional VM-based isolation imposes hypervisor overhead that degrades GPU performance — a critical problem for AI workloads. vNode (currently in private beta) uses kernel-native isolation (seccomp, cgroups, Linux namespaces, AppArmor) to deliver strong workload-level tenant isolation without any VM tax. Tenants get protected runtimes running at bare metal GPU speeds, making it possible to enforce security without compromising the performance your customers are paying for.
vCluster offers a flexible isolation spectrum. Shared Nodes provide namespace and resource quota boundaries for cost-sensitive workloads. Private Nodes give tenants fully dedicated physical hardware with their own CNI and CSI. Dedicated VMs add OS-level kernel separation for regulated environments. vNode (private beta) layers kernel-native workload isolation on top of any configuration. This spectrum means you can match isolation strength to each tenant's security requirements without changing your platform architecture.
Yes. vCluster's vNode component is specifically designed to prevent workload escape from container boundaries. Using seccomp profiles, AppArmor policies, Linux cgroups, and kernel namespace isolation per workload, vNode prevents the syscall-level attacks that enable container breakouts. It is also compatible with gVisor (user-space kernel) and Kata Containers (lightweight VMs) for environments that require additional defense-in-depth layers beyond kernel-native isolation.
Yes. vCluster powers 100K+ GPU nodes in production across 50+ GPU clouds and Fortune 500 customers including CoreWeave and Nscale. It is named in the NVIDIA DGX SuperPOD reference architecture and referenced in the SemiAnalysis ClusterMax evaluation criteria for GPU cloud providers. Lintasarta launched Indonesia's leading GPU cloud in 90 days using vCluster, and Boost Run launched managed Kubernetes in under 45 days with zero new platform engineering hires.
Learn how AI cloud providers enforce strong tenant isolation at bare metal speed.
