ai-cloud
Build Your AI Cloud on Hard Tenant Isolation
Turn GPU infrastructure into an AI cloud. vMetal orchestrates machines, and vCluster provides independent tenant clusters with dedicated Private Nodes and optional workload isolation.
Trusted by the fastest-growing AI cloud providers
Problem
The AI Cloud Infrastructure Trap
AI cloud providers face three compounding problems that delay revenue and erode margins.
Raw Compute Is Not Enough
Selling bare metal GPUs alone is a race to the bottom. Customers expect managed environments and self-service, and they compare you with the hyperscalers.
DIY Platform Takes Too Long
Building a GPU cloud platform in-house requires significant engineering investment.
Isolation vs. Efficiency Tradeoff
Namespace isolation is too weak, and separate physical clusters are too expensive. Standard Kubernetes forces you to pick one.
Solution
One Stack from Bare Metal to Tenant Clusters
vMetal orchestrates bare metal and VM capacity through one Machine API. vCluster provisions independent Kubernetes tenant environments with dedicated Private Nodes, while teams add supported workload stacks. vCluster Labs serves 50+ GPU clouds & Fortune 500s.
Built for AI Cloud Providers at Scale
Every layer of the stack purpose-built for GPU cloud operators who need tenant isolation without sacrificing bare metal performance.
Tenant Isolation
Separate Control Planes Per Tenant
Each tenant has an independent Kubernetes API, data store and RBAC boundary. Lightweight control-plane hosting reduces dedicated server requirements for AI cloud services; Private Nodes keep production workers tenant-specific.
- Independent tenant API and data store
- Tenant-scoped scheduling on Private Nodes
- No dedicated control-plane servers per tenant
Bare Metal
Zero-Touch GPU Server Provisioning
vMetal exposes one Machine API above bare metal and VM provisioning drivers. Registered inventory, images and credentials enable repeatable machine lifecycle workflows for AI cloud services. Network automation uses supported integrations such as Netris.
- Stable Machine API
- Bare metal and VM provisioning
- Driver-specific lifecycle automation
Workload Security
Workload Isolation Without Hypervisor Tax
vNode uses Linux user namespaces and seccomp to restrict workload privileges and system calls. It adds runtime hardening for AI cloud services without a guest kernel or hypervisor, complementing dedicated workers rather than allocating GPUs.
- Linux user namespaces and seccomp
- No guest kernel or hypervisor
- Helps limit workload breakout
Dynamic Scaling
Automatic GPU Node Provisioning
Karpenter-based Auto Nodes requests worker capacity according to configured policies. With the vMetal provider, Machine requests provision available infrastructure for AI cloud services; inventory, driver readiness and release rules determine actual capacity.
- Karpenter-based node management
- Supported infrastructure provider integration
- Inventory-aware capacity policies
Tenant Experience
Template Based Portal for Your Customers
Give your AI cloud customers a self-service portal to provision and manage their own Kubernetes environments. Deliver the cloud experience AI teams expect from hyperscalers, on your infrastructure.
- Approved templates and access policies
- Dedicated production tenant workers
- Readiness depends on available capacity
Why vCluster
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.
100K+
GPUs Powered
50+
GPU Clouds & F500s
<45
Days to Launch
30K
GitHub Stars
Get Started in 3 Steps
1
Schedule a Demo
Talk to our team about your stack
2
Deploy vCluster
Deploy vCluster on your infra in minutes
3
Onboard Your Tenants
Go live with a hyperscaler-grade tenant experience in days
FAQs
What is an AI cloud and how does vCluster support it?
Operators expose approved Kubernetes templates through vCluster Platform interfaces or an integrated customer portal. Templates define supported configurations, while access and capacity policies govern requests. vMetal can provision machines and Private Nodes dedicate production workers. Customers receive their own Kubernetes environment once its control plane, networking, storage and worker capacity are ready; application stacks have additional prerequisites.
How does vCluster deliver tenant isolation without separate physical clusters?
vCluster virtualizes the Kubernetes control plane. Each tenant receives a cluster with its own API server, data store, scheduler, and RBAC running as a lightweight pod inside the control plane cluster, which gives tenants cluster-admin rights without a dedicated physical cluster each. Private Nodes, the recommended production model, add dedicated worker nodes with hardware-level separation. vNode can add workload-level isolation, and Shared Nodes remain available for your own internal dev, test, and CI/CD.
How long does it take to launch a managed Kubernetes offering on GPU hardware?
Boost Run went from decision to production launch in under 45 days on vCluster, and Lintasarta runs 170+ tenant clusters in production. The time saved comes from running a production-proven stack for bare metal provisioning, tenant cluster orchestration, and Day 2 operations instead of building one yourself.
Does vCluster require an existing Kubernetes cluster to run on bare metal?
vCluster Standalone runs as a Kubernetes binary directly on Linux without requiring another Kubernetes cluster, k3s or kubeadm. It can provide an initial Kubernetes foundation. vMetal is a separate infrastructure orchestration layer that requires vCluster Platform and a connected Kubernetes cluster for its Metal3/Ironic path. Hardware registration, images, credentials and driver setup remain prerequisites for machine provisioning.
How does vNode protect GPU cloud tenants from container breakouts?
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.
Is vCluster recognized in any industry standards for GPU cloud infrastructure?
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.
Launch Your AI Cloud Faster
See how AI cloud providers run isolated tenant clusters on bare metal GPUs with vCluster.
