Define tenant boundaries from machines to workloads. vMetal provisions infrastructure, and vCluster combines separate tenant control planes with dedicated Private Nodes and optional vNode runtime isolation.
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.
vMetal manages infrastructure below separate tenant control planes. Private Nodes dedicate workers, Netris can segment supported networks, and vNode adds runtime hardening. Each layer addresses a different tenant boundary. Lintasarta runs 170+ tenant clusters in production.
Every layer of the stack is designed to give tenants hard boundaries without sacrificing GPU performance or operational efficiency.
vNode uses Linux user namespaces and seccomp to restrict workload privileges and system calls. It adds runtime hardening for tenant isolation without a guest kernel or hypervisor, complementing dedicated workers rather than allocating GPUs.
Each tenant has an independent Kubernetes API, data store and RBAC boundary. Lightweight control-plane hosting reduces dedicated server requirements for tenant isolation; Private Nodes keep production workers tenant-specific.
Private Nodes dedicate worker capacity to one tenant cluster, with its own CNI and storage configuration. For tenant isolation, select physical servers or VMs according to the required infrastructure boundary.
Combine separate tenant APIs, dedicated Private Nodes and vNode runtime hardening for tenant isolation. These boundaries help limit tenant incidents; infrastructure patching, least privilege and application security remain part of the deployment.
Tenant-owned CNI configuration and supported Netris integrations provide separate network controls for tenant isolation. Ethernet segmentation can use VLANs, VRFs and ACLs; InfiniBand isolation uses fabric partitions rather than Ethernet constructs.
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 keeping each customer's workloads, data and control plane resources separated from other tenants on the same infrastructure. For AI clouds running GPU workloads, weak isolation creates real risks: container escapes, data leaks and noisy-neighbor GPU contention. Strong tenant isolation needs separation at the control plane, workload runtime and network layers at the same time, which namespace-level partitioning alone can't provide.
Namespaces divide resources within a Kubernetes cluster, but share its API and cluster-wide administrative scope. RBAC, quotas and network policies remain useful controls. vCluster adds a separate tenant API and data store, with Private Nodes dedicating production workers. This reduces dependencies between tenant environments while platform administrators still manage the underlying infrastructure and its failure domains.
Dedicated Private Nodes remove cross-tenant workload placement from those workers. Bare metal avoids a hypervisor layer, but this is not a throughput or latency guarantee. GPU drivers, workload configuration, network fabrics and storage can still influence performance. vNode adds runtime hardening using the Linux kernel; benchmark the selected stack against the workload and service levels you intend to offer.
vCluster offers an isolation spectrum. Private Nodes are the recommended production model, giving tenants dedicated worker nodes with their own CNI and CSI. Shared Nodes, with namespace and quota boundaries, suit internal dev, test and CI/CD by trusted teams. Dedicated VMs add kernel separation for regulated environments, and vNode layers workload isolation on top of any of these. You can match isolation strength to each tenant without changing your platform architecture.
vNode uses Linux user namespaces and seccomp to restrict workload privileges and system calls, helping prevent container breakout. It uses the underlying Linux kernel rather than a separate guest kernel. Add it as runtime hardening alongside dedicated Private Nodes, patching and least-privilege policies. Validate kernel, container runtime, GPU access and workload compatibility; it is not a replacement for GPU allocation or application authorization.
vCluster Labs software powers 100K+ GPUs and 1M+ CPUs, serving 50+ GPU clouds & Fortune 500s combined. In production, Lintasarta operates 170+ tenant clusters, while Boost Run completed its managed Kubernetes launch in under 45 days from the decision. These named deployments provide scale and launch examples for teams evaluating the platform for their own infrastructure.
Learn how AI cloud providers enforce strong tenant isolation at bare metal speed.
