AI Cloud
GPU as a Service Built on Tenant Isolation
Launch a competitive GPU as a service offering in weeks.
Trusted by the fastest-growing AI cloud providers
Problem
Why GPU as a Service Is Hard to Launch
GPU providers stall competing on specs alone while customers demand cloud-grade managed Kubernetes experiences.
Raw Compute Is Not Enough
Customers don't just want raw compute. They expect self-service environments, managed Kubernetes, and cloud-native tooling from day one.
Namespace Isolation Is Too Weak
Namespace isolation leaves tenants exposed to platform internals and each other.
Building In-House Takes Years
Building a GPU cloud platform yourself takes 6 to 10 engineers, 6 to 12 months, and over a million dollars.
Solution
One Stack From Bare Metal to Tenant Clusters
vCluster Platform virtualizes the Kubernetes control plane itself, giving every tenant their own API server, etcd, RBAC, and CRDs as lightweight pods on shared bare metal.
Everything to Launch Your GPU Cloud
From zero-touch bare metal provisioning to isolated tenant clusters and pre-validated AI environments, vCluster covers the full stack.
Bare Metal
Zero-Touch GPU Server Provisioning
PXE boot, OS installation, machine registration, and network automation handled automatically.
- PXE boot to production automatically
- Full machine lifecycle management
- Network automation built in
Tenant Isolation
Isolated Tenant Clusters in Seconds
Every tenant gets a fully isolated Kubernetes control plane running as a lightweight pod.
- Spin up tenant clusters in seconds
- Own API server per tenant
- Hundreds of tenants on shared hardware
Workload Security
Kernel-Native Workload Isolation
vNode gives each workload its own secure runtime using seccomp, cgroups, namespaces, and AppArmor.
- No hypervisor tax on GPU performance
- Container breakout protection
- Defense-in-depth isolation stack
AI Environments
Pre-Validated AI Platforms Included
Pre-validated environments for Run:AI, Ray, and Jupyter. feature5Id: self-service-portal
- Run:AI, Ray, Jupyter ready instantly
- Cluster to AI platform in minutes
- Certified against tenant isolation
Customer Experience
EKS-Grade Self-Service for Customers
Give end customers an EKS-like self-service portal.
- Self-service cluster provisioning portal
- EKS-like customer experience
- Fully branded tenant environments
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+
GPU Nodes 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 GPU as a service and how does Kubernetes fit in?
GPU as a service means providing customers on-demand access to GPU compute with cloud-grade management on top. Kubernetes has become the standard orchestration layer for AI workloads, so GPU cloud providers are expected to offer managed Kubernetes alongside raw compute. vCluster Platform lets you deliver fully isolated, CNCF-certified tenant clusters on your bare metal GPU infrastructure so every customer gets the cloud experience they expect without you provisioning a separate physical cluster per tenant.
How fast can I launch a GPU as a service offering with vCluster?
Boost Run launched their managed Kubernetes service in less than 45 days using vCluster Platform. Lintasarta launched Indonesia's leading GPU cloud in 90 days with 170+ tenant clusters. The platform handles bare metal provisioning, tenant cluster orchestration, and workload isolation in one integrated stack, so your engineering team focuses on differentiation rather than rebuilding infrastructure primitives from scratch.
How does vCluster isolate tenants on shared bare metal GPU hardware?
Each tenant receives a fully isolated Kubernetes control plane running as a lightweight pod with its own API server, etcd, RBAC, and CRDs. On the workload side, vNode adds kernel-native isolation using seccomp, cgroups, namespaces, and AppArmor to prevent container breakouts without adding hypervisor overhead.
Does vCluster support dedicated GPU nodes per tenant?
Yes. vCluster offers isolation ranging from shared nodes to fully dedicated physical hardware per tenant.
Is vCluster compatible with existing AI platforms like Run:AI or Ray?
Yes. Certified Stacks are pre-validated AI environments that include Run:AI, Ray, and Jupyter.
What bare metal GPU infrastructure does vCluster support?
vMetal handles zero-touch provisioning for GPU servers including PXE boot, OS installation, machine registration, and network automation.
Launch Your GPU Cloud in Weeks
See how vCluster powers GPU as a service for 50+ GPU clouds and Fortune 500 customers.
