Idle GPUs Are the Most Expensive Problem in AI Infrastructure
GPU hardware loses value fast. The real competitive advantage in AI infrastructure isn't better chips — it's how quickly you can start monetizing them.
NVIDIA H100 GPUs that sold for $40,000 at launch are already appearing on secondary markets for around $6,000. For organizations building AI infrastructure platforms, every month of delay means compounding losses from depreciation, engineering burn, and missed revenue. The question isn't which GPU to buy — it's how fast you can get your platform to production.
How to: Exploring K8S on vCluster, Deploying an Observability stack - part 1
Metrics, dashboards, alerting, and long-term storage, deployed on a multi-node Kubernetes cluster that runs entirely in Docker
Observability isn't optional, it's foundational. This guide walks through deploying Prometheus, Grafana, Thanos, and RustFS on a local multi-node Kubernetes cluster powered by vCluster. No cloud account required. No VMs. Just Docker and a few commands.
Introducing vMetal: Run Your GPU Data Center Like a Hyperscaler
Most GPU data centers take months to operationalize. vMetal gets you from rack to running cluster in minutes.
Buying GPUs is the easy part. Operating them like a cloud platform is what separates Neoclouds that launch from ones that stall. vMetal automates the full bare metal lifecycle — discovery, provisioning, cluster attachment — so you can start delivering GPU capacity immediately.
Day 7: The vCluster Platform UI: Managing vind Clusters Visually
A web dashboard for all your local vind clusters with projects, team management, role-based access, and automation through access keys
The CLI is great for daily dev work. But when you need to see all your clusters at a glance, organize them into projects, manage team access, or hand off a demo environment, the vCluster Platform UI takes vind from a better KinD to a real cluster management platform. Day 7 of the 7 Days of vind series.
Day 6: Advanced Features: Sleep/Wake, Registry Proxy, and Custom Networking
Pause clusters and resume where you left off, pull locally built images in 45ms, install Cilium, and map custom ports to your local cluster
With KinD, you delete your cluster at the end of the day and recreate it tomorrow. With vind, you pause it and resume exactly where you left off. Deployments, services, PVCs, all still there. Day 6 covers sleep/wake, registry proxy, custom CNI, and more.
Day 5: CI/CD with vind: The setup-vind GitHub Action
Drop setup-kind from your GitHub Actions and get built-in registry proxy, automatic log export, and multi-cluster CI workflows with setup-vind
If you're using setup-kind in GitHub Actions, you're still loading images manually and missing automatic log exports. setup-vind is a drop-in replacement with built-in registry proxy, automatic artifact export, and multi-cluster support. Day 5 of the 7 Days of vind series.
Day 4: External Nodes: Joining a GCP Instance to Your Local vind Cluster
Run your control plane locally in Docker, join a real cloud VM as a worker node over VPN, and schedule pods across both
Local Kubernetes tools stop at your laptop. vind doesn't. Join a GCP Compute Engine instance as a real worker node to your local cluster over an encrypted VPN tunnel. Test GPU workloads, mixed architectures, and hybrid setups. Day 4 of the 7 Days of vind series.
Day 3: Multi-Node vind Clusters: Real Scheduling, Real Node Drains
Create a multi-node local cluster in Docker and test pod distribution, node drains, affinity, and anti-affinity, just like production
Single-node clusters can't test scheduling. With vind, spin up a 4-node cluster in Docker, deploy across workers, drain nodes, and test affinity rules, real Kubernetes behavior on your laptop. Day 3 of the 7 Days of vind series.
Day 2: Getting Started with vind: Your First Deployment with LoadBalancer
Install vind, create a local Kubernetes cluster, and deploy nginx with a working LoadBalancer — in under 3 minutes
KinD needs MetalLB for LoadBalancer services. vind has it built in. In Day 2 of the 7 Days of vind series, we walk through creating a cluster, deploying nginx, and hitting a real LoadBalancer IP, all running in Docker on your laptop.
Day 1: Introduction to vind: Why I Replaced KinD with vCluster in Docker [vind]
KinD works until it doesn't. vind picks up where it leaves off.
KinD works, until you need LoadBalancer services, multi-node setups, or the ability to pause and resume clusters. vind gives you a production-like local Kubernetes experience in Docker with features KinD simply doesn't have. Day 1 of the 7 Days of vind series.
When 37% of Cloud Environments Are Vulnerable, "Just Use VMs" Isn't Good Enough
How vNode delivers VM-level isolation for containerized AI workloads — without the VM overhead
A three-line Dockerfile broke container security. CVE-2025-23266 exposed 37% of cloud environments running AI workloads, giving attackers full root access to Kubernetes nodes. VMs are too heavy, gVisor can't catch it. vNode offers a third option: container-native isolation that's as strong as VMs but as lightweight as containers.
Reimagining Local Kubernetes: Replacing Kind with vind — A Deep Dive
An open-source alternative to KinD with native LoadBalancer support, free UI, pull-through caching, and the ability to attach external nodes to your local cluster
Kubernetes developers have long relied on tools like KinD (Kubernetes in Docker) to spin up disposable clusters locally for development, testing, and CI/CD workflows. While KinD is a solid tool, it has limitations like not being able to use service type LoadBalancer, accessing homelab clusters from the web, or adding GPU nodes to your local cluster. Introducing vind (vCluster in Docker) - an open source alternative that enables Kubernetes clusters as first-class Docker containers, offering improved performance, modern features, and an enhanced developer experience.
Pragmatic Hybrid AI: Bursting Across Private GPUs and Public Cloud Without Leaking Data or Dollars
Hybrid AI That Works: Network Isolation, Data Gravity, and Workload Placement in the Real World
For the past two years, the AI infrastructure debate has been framed as binary: go all-in on on-prem GPU estates or stay all-in on the cloud. Neither approach is sustainable at enterprise scale. The winning pattern is intelligent placement—keep sensitive or data-heavy jobs local, burst elastic workloads into the cloud. Success depends on strict isolation, careful placement, and scheduling that is cost-aware from the start.
Why the nodes/proxy Kubernetes RCE Does Not Apply to vCluster
How vCluster provides more security than vanilla Kubernetes when using nodes/proxy permissions for monitoring stacks
A security researcher recently disclosed that Kubernetes nodes/proxy permissions can be exploited for remote code execution. Kubernetes labeled it "working as intended" and issued no CVE. Since vCluster was mentioned in the disclosure, we investigated how this vulnerability affects our users. The conclusion: vCluster is not compromised and actually provides more security than vanilla Kubernetes when using features that require the nodes/proxy permission.
Launching vCluster Free - Get vCluster Enterprise Features at No Cost
A free tier that makes advanced Kubernetes multi-tenancy accessible—without trials or sales gates.
We’re launching vCluster Free to make advanced Kubernetes multi-tenancy available to more builders.
Isolating Workloads in a Multi-Tenant GPU Cluster
Practical strategies for securing shared GPU environments with Kubernetes-native isolation, hardware partitioning, and operational best practices
Sharing GPU access across teams maximizes hardware ROI, but multitenant environments introduce critical performance and security challenges. This guide explores proven workload isolation strategies, from Kubernetes RBAC and network policies to NVIDIA MIG and time-slicing, that enable you to build secure, scalable GPU clusters. Learn how to prevent resource contention, enforce tenant boundaries, and implement operational safeguards that protect both workloads and data in production AI infrastructure.
Separate Clusters Aren’t as Secure as You Think — Lessons from a Cloud Platform Engineering Director
Lessons in Intentional Tenancy and Security at Scale from a Cloud Platform Director
If a workload needs isolation, give it its own cluster. It sounds safe, but at scale, this logic breaks down. Learn why consistency, not separation, is the real security challenge in modern Kubernetes environments.
Solving GPU-Sharing Challenges with Virtual Clusters
Why MPS and MIG fall short—and how virtual clusters deliver isolation without hardware lock-in
GPUs are expensive, but most organizations only achieve 30-50% utilization. The problem? GPUs weren't designed for sharing. Software solutions like MPS lack isolation. Hardware solutions like MIG lock you into specific vendors. vCluster takes a different approach—solving GPU multitenancy at the Kubernetes orchestration layer.
vCluster Ambassador program
Introducing the first vCluster Ambassadors shaping the future of Kubernetes multi-tenancy and platform engineering
Meet the first vCluster Ambassadors - community leaders and practitioners advancing Kubernetes multi-tenancy, platform engineering, and real-world developer platforms.
Architecting a Private Cloud for AI Workloads
How to design, build, and operate a cost-effective private cloud infrastructure for enterprise AI at scale
Public clouds are convenient for AI experimentation, but production workloads often hit walls. For enterprises running continuous training and inference, a private cloud can deliver better ROI, data sovereignty, and performance. This comprehensive guide walks through architecting a private cloud for AI workloads from the ground up.
GPU Multitenancy in Kubernetes: Strategies, Challenges, and Best Practices
How to safely share expensive GPU infrastructure across teams without sacrificing performance or security
GPUs don't support native sharing between isolated processes. Learn four approaches for running multitenant GPU workloads at scale without performance hits.
AI Infrastructure Isn’t Limited By GPUs. It’s Limited By Multi-Tenancy.
What the AI Infrastructure 2025 Survey Reveals, And How Platform Teams Can Respond
The latest AI Infrastructure 2025 survey shows that most organizations are struggling not due to GPU scarcity, but because of poor GPU utilization caused by limited multi-tenancy capabilities. Learn how virtual clusters and virtual nodes help platform teams solve high costs, sharing issues, and low operational maturity in Kubernetes environments.
KubeCon + CloudNativeCon North America 2025 Recap
Announcing the Infrastructure Tenancy Platform for NVIDIA DGX—plus what we learned from 100+ conversations at KubeCon about GPU efficiency, isolation, and the future of AI on Kubernetes.
KubeCon Atlanta 2025 was packed with energy, launches, and conversations that shaped the future of AI infrastructure. At Booth #421, we officially launched the Infrastructure Tenancy Platform for NVIDIA DGX—a Kubernetes-native platform designed to maximize GPU efficiency across private AI supercomputers, hyperscalers, and neoclouds. Here's what happened, what we announced, and why it matters for teams scaling AI workloads.
Scaling Without Limits: The What, Why, and How of Cloud Bursting
A practical guide to implementing cloud bursting using vCluster VPN, Private Nodes, and Auto Nodes for secure, elastic, multi-cloud scalability.
Cloud bursting lets you expand compute capacity on demand without overprovisioning or re-architecting your systems. In this guide, we break down how vCluster VPN connects Private and Auto Nodes securely across environments—so you can scale beyond limits while keeping costs and complexity in check.
vCluster and Netris Partner to Bring Cloud-Grade Kubernetes to AI Factories & GPU Clouds With Strong Network Isolation Requirements
vCluster Labs and Netris team up to bring cloud-grade Kubernetes automation and network-level multi-tenancy to AI factories and GPU-powered infrastructure.
vCluster Labs has partnered with Netris to revolutionize how AI operators run Kubernetes on GPU infrastructure. By combining vCluster’s Kubernetes-level isolation with Netris’s network automation, the integration delivers a full-stack multi-tenancy solution, simplifying GPU cloud operations, maximizing utilization, and enabling cloud-grade performance anywhere AI runs.
Recapping The Future of Kubernetes Tenancy Launch Series
How vCluster’s Private Nodes, Auto Nodes, and Standalone releases redefine multi-tenancy for modern Kubernetes platforms.
From hardware-isolated clusters to dynamic autoscaling and fully standalone control planes, vCluster’s latest launch series completes the future of Kubernetes multi-tenancy. Discover how Private Nodes, Auto Nodes, and Standalone unlock new levels of performance, security, and flexibility for platform teams worldwide.
Bootstrapping Kubernetes from Scratch with vCluster Standalone: An End-to-End Walkthrough
Bootstrapping Kubernetes from scratch, no host cluster, no external dependencies.
Kubernetes multi-tenancy just got simpler. With vCluster Standalone, you can bootstrap a full Kubernetes control plane directly on bare metal or VMs, no host cluster required. This walkthrough shows how to install, join worker nodes, and run virtual clusters on a single lightweight foundation, reducing vendor dependencies and setup complexity for platform and infrastructure teams.
GPU on Kubernetes: Safe Upgrades, Flexible Multitenancy
How vCluster and NVIDIA’s KAI Scheduler reshape GPU workload management in Kubernetes - enabling isolation, safety, and maximum utilization.
GPU workloads have become the backbone of modern AI infrastructure, but managing and upgrading GPU schedulers in Kubernetes remains risky and complex.
This post explores how vCluster and NVIDIA’s KAI Scheduler together enable fractional GPU allocation, isolated scheduler testing, and multi-team autonomy, helping organizations innovate faster while keeping production safe.
A New Foundation for Multi-Tenancy: Introducing vCluster Standalone
Eliminating the “Cluster 1 problem” with vCluster Standalone v0.29 – the unified foundation for Kubernetes multi-tenancy on bare metal, VMs, and cloud.
vCluster Standalone changes the Kubernetes tenancy spectrum by removing the need for external host clusters. With direct bare metal and VM bootstrapping, teams gain full control, stronger isolation, and vendor-supported simplicity. Explore how vCluster Standalone (v0.29) solves the “Cluster 1 problem” while supporting Shared, Private, and Auto Nodes for any workload.
Introducing vCluster Auto Nodes — Practical deep dive
Auto Nodes extend Private Nodes with provider-agnostic, automated node provisioning and scaling across clouds, on-prem, and bare metal.
Kubernetes makes pods elastic, but node scaling often breaks outside managed clouds. With vCluster Platform 4.4 + v0.28, Auto Nodes fix that gap, combining isolation, elasticity, and portability. Learn how Auto Nodes extend Private Nodes with automated provisioning and dynamic scaling across any environment.
