ai-cloud
Managed Kubernetes for GPU Cloud Builders
Build managed Kubernetes for your GPU cloud. vMetal orchestrates server provisioning, and vCluster supplies independent tenant control planes with dedicated GPU workers through Private Nodes.
Trusted by the fastest-growing AI cloud providers
Problem
Why GPU Cloud Launches Stall
Building managed Kubernetes on GPU hardware is harder than it looks.
DIY Requires Time and Investment
Building a GPU cloud platform in-house requires significant engineering investment. Most teams underestimate the scope until they are deep into the build.
Weak Isolation or Wasted Hardware
Namespace isolation is too weak. Separate physical clusters are too expensive. Standard Kubernetes forces you to choose between tenant isolation and operational efficiency.
Customers Expect a Cloud Experience
AI teams expect self-service Kubernetes and familiar tooling. Manual onboarding and unclear tenant boundaries make the infrastructure harder to use.
Solution
Managed Kubernetes from Machines to Tenants
vMetal orchestrates GPU server provisioning and vCluster supplies CNCF-certified tenant control planes. Private Nodes dedicate customer workers, while templates and fleet operations support your managed Kubernetes service. vCluster Labs serves 50+ GPU clouds & Fortune 500s.
Built for GPU Cloud at Every Layer
From bare metal provisioning to tenant isolation to AI-ready environments, vCluster covers the full stack GPU cloud providers need.
Tenant Isolation
Lightweight Tenant Kubernetes Control Planes
Each tenant has an independent Kubernetes API, data store and RBAC boundary. Lightweight control-plane hosting reduces dedicated server requirements for managed Kubernetes delivery; 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 managed Kubernetes delivery. Network automation uses supported integrations such as Netris.
- Stable Machine API
- Bare metal and VM provisioning
- Driver-specific lifecycle automation
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 managed Kubernetes delivery; inventory, driver readiness and release rules determine actual capacity.
- Karpenter-based node management
- Supported infrastructure provider integration
- Inventory-aware capacity policies
AI Workload Integration
Supported AI Workload Templates
Use supported workload templates for managed Kubernetes delivery, including the NVIDIA Run:AI partner integration. Configure GPU, network and storage prerequisites, and validate the selected template for Private Nodes before offering it to production customers.
- Native NVIDIA Run:AI catalog
- Validate Private Node configuration
- Release-specific integration prerequisites
Tenant Experience
Template Based Portal for Your Tenants
Give your customers an EKS-like self-service experience. Tenants provision and manage their own isolated Kubernetes environments through a portal without opening a support ticket or waiting on your team.
- 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 makes vCluster different from building managed Kubernetes ourselves?
Building managed Kubernetes for a GPU cloud in-house takes significant engineering investment, and much of it goes into control plane plumbing your customers don't see. vCluster Platform gives you the stack from bare metal provisioning to isolated tenant clusters to pre-validated AI environments, so you aren't starting from scratch.
How does tenant isolation work on GPU cloud hardware?
vCluster virtualizes the Kubernetes control plane. Each tenant receives a CNCF-certified Kubernetes cluster with its own API server, data store, scheduler and RBAC, running as lightweight processes in your control plane cluster, and tenants cannot see platform internals or other tenants' workloads. Private Nodes, the recommended production model, add dedicated worker nodes with per-tenant CNI and storage. vNode adds workload isolation with user namespaces, seccomp and cgroups, without hypervisor overhead.
Do tenants get real Kubernetes or a restricted subset?
Real Kubernetes. Every tenant gets a conformant, CNCF-certified cluster with cluster-admin access inside its own environment. Tenants can install custom CRDs, configure their own RBAC and use standard Kubernetes tooling. Each one has its own control plane rather than a namespace partition or a proprietary API subset.
How quickly can a GPU cloud provider go live with managed Kubernetes?
Boost Run went from decision to production launch of its managed Kubernetes offering in under 45 days, and Lintasarta runs 170+ tenant clusters in production on vCluster. The platform covers bare metal provisioning, tenant cluster orchestration and AI environment setup, so providers aren't assembling separate tools from scratch.
Is vCluster compatible with NVIDIA GPU hardware and AI platforms?
NVIDIA Run:AI is available through the native Certified Stacks integration, with release-specific prerequisites and templates. Its bundled templates do not automatically supply dedicated Private Nodes. Configure and validate the production worker model, GPU access, networking and storage before offering it to customers. Ray and Jupyter can use compatible Kubernetes deployment tooling and require their own supported configuration.
Can vCluster support a GPU cloud deployed across multiple regions or data centers?
vCluster Platform can coordinate tenant environments across supported clouds and data centers. Each Kubernetes tenant has its own control plane; a central fleet interface does not mean one tenant API or one failure domain. Plan worker placement, routing, storage and data residency for each location. An optional encrypted VPN can connect control planes and workers where appropriate for the deployment.
Launch Managed Kubernetes on Your GPU Cloud
See how GPU cloud providers go from bare metal to managed Kubernetes faster.
