Engineering Tech Lead
About the job
- Production experience working directly with containerd, rather than only through Docker or Kubernetes.
- Experience with the Kubernetes kubelet, the Container Runtime Interface (CRI), or a node-resident agent, including knowledge of cgroups v2, OCI hooks, and the kubelet's Pod Lifecycle Event Generator (PLEG).
- Ability to write production Go for systems-level code involving system calls, namespaces, file descriptors, and process lifecycle.
- Experience shipping with Linux isolation technologies including user namespaces, seccomp-BPF, capabilities, and Landlock, and the ability to reason about their failure modes.
- Ability to set technical direction by writing design documents, prototyping difficult parts, and bringing the team along without becoming a bottleneck.
- Drive the architecture for how vNode wraps containerd, integrates with the kubelet, and exposes safe isolation primitives; set priorities for what ships, is deferred, or is redesigned.
- Lead work across vNode, containerd, Kata Containers, gVisor, runc, and the Linux kernel, improving the behavior between a Pod specification and a process running under a constrained user namespace and seccomp profile.
- Own vNode's integration with the node lifecycle, including CRI, kubelet device plugins, cgroups v2, eviction, and interactions between Kubernetes' node model and a multi-tenant runtime.
- Run technical design reviews, establish testing patterns for isolation guarantees, and mentor engineers.
- Run vNode internally against vCluster Platform tenant clusters before customer release and use production feedback to address AI Cloud operator needs.
- Contribute upstream to containerd, runc, and Kubernetes SIG-Node; write technical posts about namespace-based isolation; and represent vCluster Labs at relevant technical events.
- Direct experience with Kata Containers, gVisor, or another sandboxed or isolated runtime.
- Meaningful upstream contributions to containerd, runc, Kata, gVisor, Kubernetes SIG-Node, or related projects.
- Experience building or operating infrastructure where hostile workloads share hosts, including AI Cloud operators, multi-tenant SaaS, or regulated industries.
- Talks, posts, or RFCs that advance discussion of container isolation.
As an Engineering Tech Lead at vCluster Labs, you aren't just shipping container runtime features; you are defining how Kubernetes operators get VM-grade tenant isolation without the VM tax. vNode replaces virtual kubelets and microVMs with a runtime built on Linux user namespaces and seccomp, and the person in this seat owns where that runtime goes next. You will partner directly with the vNode founding engineers, run the technical bar for the team, and ship the work that decides whether AI Clouds and regulated enterprises can adopt vNode as their default isolation layer.
As an Engineering Tech Lead, your role will include:
Owning the vNode technical execution: Drive the architecture for how vNode wraps containerd, integrates with the kubelet, and exposes safe isolation primitives. You will set the bar for what ships, what gets deferred, and what gets redesigned.
Going deep on container runtimes and isolation: Lead the work where vNode meets containerd, Kata Containers, gVisor, runc, and the kernel. You will be the person who can explain (and improve) exactly what happens between a Pod spec and a process running under a constrained user namespace with a tight seccomp profile.
Shipping the kubelet integration surface: Own how vNode plugs into the node lifecycle: CRI, kubelet device plugins, cgroups v2, eviction, and the rough edges between Kubernetes' node model and a runtime that does not assume one tenant per node.
Raising the engineering bar: Run technical design reviews, set the pattern for testing isolation guarantees, and mentor the engineers shipping alongside you. You are not a people manager, but you are the engineer the team copies.
Being Customer Zero for vNode: Run vNode against vCluster Platform tenant clusters internally before customers see it. You will close the loop between what AI Cloud operators need and what vNode actually does in production.
Representing vNode externally: Contribute upstream where it matters (containerd, runc, Kubernetes SIG-Node), write the technical posts that explain why namespace-based isolation is the right answer, and represent vCluster Labs at KubeCon-class venues when the timing is right.
This role could be a fit for you if you bring:
Deep container runtime experience: You have shipped production work against containerd directly, not just consumed it through Docker or Kubernetes. Direct experience with Kata Containers, gVisor, or another sandboxed/isolated runtime is a strong plus.
Kubernetes node-level depth: You have worked inside the kubelet, the CRI layer, or a node-resident agent. You know what cgroups v2, OCI hooks, and the kubelet's PLEG do and where they break.
Go systems programming chops: You write production Go for systems-level code (syscalls, namespaces, file descriptors, process lifecycle), not just service handlers.
Linux isolation fluency: User namespaces, seccomp-bpf, capabilities, and Landlock are not abstract concepts; you have shipped against them and can reason about their failure modes.
Tech Lead instincts: You set technical direction by writing the design doc, prototyping the hard part, and then bringing the team along. You raise the bar without becoming the bottleneck.
Bonus points for:
Upstream contribution history: Meaningful commits to containerd, runc, Kata, gVisor, Kubernetes SIG-Node, or related projects.
Tenant Isolation domain expertise: You have built or operated infrastructure where the threat model includes hostile workloads on shared hosts (AI Cloud operators, multi-tenant SaaS, regulated industries).
Public technical voice: Talks, posts, or RFCs that move the conversation on container isolation.
About vCluster Labs
We're the #1 platform for AI infrastructure, trusted by the world's fastest-growing AI cloud builders. We're a venture-backed startup that's raised over $28M from top-tier investors including Khosla Ventures (first investor in OpenAI, GitLab, Stripe, and DoorDash), and we're in a hyper-growth phase looking for motivated people to join our team. Our headquarters are in San Francisco (Salesforce Tower), but our team is distributed around the globe with a remote-first culture.
We give AI Cloud providers and AI factories a hyperscaler-like experience on their own GPU infrastructure. Our platform runs the full stack an operator needs, from bare metal provisioning and node lifecycle management up through managed Kubernetes, Slurm, Ray, and inference clusters, so they can turn raw GPUs into cluster products they can sell in days instead of spending 12+ months building it themselves. Today we power over 100,000 GPUs and 1 million CPUs across 50+ AI clouds and Fortune 500 companies, backed by a team of 40+ infrastructure engineers who build alongside our customers rather than just shipping them software.
We're the company behind vCluster, the open source technology for tenant isolation on Kubernetes, with 11,000+ GitHub stars and 40M+ tenant clusters created since 2021. Open source is part of our DNA. At KubeCon North America 2025, we launched our Infrastructure Tenancy Platform for AI, a Kubernetes-native framework built for running AI, ML, and GPU-intensive workloads anywhere, with an NVIDIA-validated reference architecture for DGX systems.
Benefits
We offer the following benefits:
Competitive Salary: We offer a competitive compensation package, including equity.
Premium Insurance: Health, dental, vision, and life Insurance, including plans for you and eligible dependents (benefits vary depending on country).
Flexible Working Schedule: You have a doctor’s appointment or need to head to the supermarket to get groceries at 2pm? We won’t have an issue with that. To us, results matter more than clocking in and out at the same time every day.
Workplace Flexibility: We’re very flexible about where you work. We know things can change in life and we’re happy to adjust the work environment for you along the way.
Culture & Values
At vCluster Labs, we value and stand for:
Make it Happen: We have a relentless bias for action and the grit to push through obstacles. We do whatever it takes to figure it out, put in the work, and ruthlessly prioritize the actions that drive measurable impact for the business.
Own the Outcome: We understand that our responsibility doesn't end when a task is checked off; it ends when the value is delivered. We connect our daily individual actions to the broader success of the company and our customers.
Create Wow: We measure success by the experience we generate, both inside and outside the company. For our customers, this means impressive speed and intuitive experiences. For our team, this means going the extra mile to support one another and to continuously drive each other to new heights.
Open Source, Open Mind: We are actively contributing to and maintaining open-source projects. Internally, we foster meritocracy — the strongest ideas win, no matter who or where they come from.
Build Tomorrow’s Standards, Intentionally: We don't just ship software; we define the state-of-the-art of tomorrow. We are fearless in tearing down old approaches to build something better, but we are disciplined in how we do it because we know our users rely on our technology to run mission-critical infrastructure platforms.
About the company
vCluster Labs builds open-source tools that make Kubernetes easier to run at scale. Their projects help platform engineers manage multi-tenant Kubernetes clusters, optimize resource usage, and cut cloud costs so environments stay stable while growing. The products act as building blocks that teams can combine to enable multi-tenancy, efficient resource planning, and scalable cluster management. With over 100 enterprises using their solutions, vCluster’s goal is to help teams move faster, spend less on infrastructure, and keep platform stacks reliable.
Company Size
51-200
Company Stage
N/A
Total Funding
N/A
Headquarters
Stockholm, Maine
Founded
N/A
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Simplify's Take
What believers are saying
- March 2026 QumulusAI partnership puts vCluster on NVIDIA Blackwell GPUs for enterprise AI teams.
- Polarise partnership expands vCluster into sovereign European AI clouds, especially Germany's regulated mid-market.
- Aggressive hiring and fresh product releases suggest commercialization momentum, not runway distress.
What critics are saying
- NVIDIA and hyperscalers can bundle tenancy features, commoditizing vCluster’s core isolation advantage by 2027.
- AI infrastructure customers face long sales cycles; partnerships with QumulusAI and Polarise still signal channel dependence.
- If vMetal underdelivers reliability, operators will revert to manual scripts, weakening the unified platform narrative.
What makes vCluster Labs unique
- vCluster virtualizes Kubernetes tenancy, letting AI teams isolate workloads on shared GPU hardware.
- March 2026 vMetal adds bare-metal machine management, spanning provisioning, upgrades, and repurposing.
- Integrations with NVIDIA Run:ai, Netris, and KubeVirt create a full-stack AI infrastructure layer.
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Benefits
- Health Insurance
- Dental Insurance
- Vision Insurance
- Life Insurance
- Flexible Work Hours
- Remote Work Options
- Company Equity
Growth & Insights and Company News
6 month growth
1%1 year growth
2%2 year growth
2%Saturn Cloud and vCluster Labs announced a partnership on October 14, 2026, to help AI cloud providers sell managed, per-token inference on their own GPUs. Saturn Cloud's platform is now available as a vCluster Stack, a reusable deployment template that installs it inside an isolated tenant cluster. Each customer gets a separate environment and per-token endpoints. Operators create a tenant cluster on GPU servers they own, deploy the Saturn Cloud Stack, and the customer can start calling endpoints. Later customers follow the same automated process. "AI clouds are moving from renting GPUs to selling services, because the margins are in the services, not the servers," said Lukas Gentele, chief executive officer of vCluster Labs. The first version of the Stack is available now, and operators can adapt it to their own environments.
vCluster Labs ships Stacks to package any AI environment as a managed service. Oct 8, 2026 SAN FRANCISCO - October 8, 2026 - vCluster Labs, the platform operators use to build and run their own AI cloud like a hyperscaler, today announced the general availability of Stacks in vCluster Platform v4.12, which automate the long list of ordered setup steps required before a managed service can go live, so an operator can deploy the same environment to every tenant. The limit on how many managed services an operator can offer is not GPU supply. It is the setup work between an empty tenant cluster and an environment that is ready to use, stitched together by hand and repeated for every tenant. "Every operator runs into the same wall," said Lukas Gentele, Co-Founder and CEO of vCluster Labs. "Once the cluster exists, the real project starts: registry credentials, ingress, certificates, GPU components, a control plane, and registration, all wired together by hand, in the right order, again for every tenant." A Stack is that environment described once: which applications, in what order, what has to be healthy before the next thing starts, and which parameters the person deploying it is allowed to set. Deploy it and a tenant gets a running environment. Deploy it a hundred times and the hundredth costs what the first one did. A new managed service becomes something an operator ships rather than something they staff. Stacks make new services faster to launch and easier to operate. A new managed service can go live in days instead of months, because the work is authoring one template rather than staffing an install for every tenant. Each additional tenant is a deployment rather than a project, which is how a GPU fleet turns into higher-margin managed products instead of raw capacity sold by the hour. Operating them is the other half. Health, logs, upgrades, and clean tenant removal are handled by the platform, and because every tenant runs an identical environment, the team supporting the fiftieth tenant is troubleshooting the same configuration it learned on the first. Operators add new tenants without adding people to keep each environment running. Get started with the #1 platform for AI infra. Trusted by today's fastest-growing AI cloud builders. Available today. Stacks ship with ready-made examples covering the environments operators are standing up now: * NVIDIA Run:ai, in four certified configurations covering both a dedicated control plane for a single cluster and a shared control plane serving many tenant clusters. * NVIDIA Dynamo, standing up a distributed inference environment inside a tenant cluster so an operator can offer serving for large models that span many GPUs and nodes. * Saturn Cloud, installing the AI token factory platform inside a tenant cluster so an operator can offer per-token inference, fine-tuning, and usage billing on GPUs they already own. "Each of these is a service an operator can put in front of customers without building the integration first," Gentele added. "That is the difference between selling capacity and selling products, and you add one without adding a team to run it. The repository is open, so the next one does not have to come from us." "Adding a service to an AI cloud shouldn't mean starting a new integration project each time," said Hugo Shi, co-founder and CTO of Saturn Cloud. "vCluster built the Saturn Cloud Stack, so an operator can deploy it like any other service. Each customer gets private inference in an environment of their own, ready to use right away, and a small team can keep adding services without adding headcount." Anyone can author a Stack. Stacks are not limited to integrations vCluster Labs builds. Any team whose software runs inside customer clusters can describe a correct installation once and have it land identically everywhere it is deployed. A public repository is open for exactly that, so a team that has already solved this can hand the next team a starting point. Stacks are generally available today in vCluster Platform 4.12 with vCluster 0.37. Supporting resources. About vCluster Labs vCluster Labs is the platform operators use to build and run their own AI cloud, like a hyperscaler. vCluster turns raw GPUs into cluster products they can sell, from bare metal provisioning up through managed Kubernetes, Slurm and inference clusters, with tenant isolation at the infrastructure layer. The result is more margin per GPU, less dependence on a few big customers, and new managed services live in days instead of months. vCluster is trusted by fast-growing AI cloud providers including Nebius, Groq, Firmus and Corvex across 100K+ GPUs, and by enterprises including Adobe, Samsung and Deloitte. Table of Contents
The AI infrastructure bottleneck: why 'good enough' Kubernetes isn't cutting it anymore. While security eyes are on the RSAC conference in San Francisco this week, the compute world is focused on KubeCon EU in Amsterdam. But the theme of artificial intelligence is the pervasive across both, as in enterprise information technology we've reached a point where "AI curiosity" has officially been replaced by "AI urgency." Every chief information officer I talk to is under immense pressure to move from those neat little research-and-development experiments to actual production-grade deployment. But as they scale, they're hitting a wall that isn't about the models or the data - it's about the plumbing. Specifically, it's the graphics processing unit infrastructure bottleneck. For years, we've treated Kubernetes as the panacea to infrastructure woes. Need to scale? Throw it in a container. Need to orchestrate? K8s is your friend. But when you're dealing with Nvidia Corp. Blackwell B300s and massive training clusters, the standard way of doing things is sharing overprovisioned environments or waiting weeks for dedicated hardware. These are recipes for project failure, only adding to the narrative that the majority of AI project fail. Today at KubeCon, neocloud provider QumulusAI and vCluster, creators of virtual Kubernetes cluster technology, announced a partnership to address much of the friction between infrastructure agility and the rigid demands of high-performance GPUs. The real cost of infrastructure friction. Today's reality is that enterprise development teams are currently stuck in a "pick your poison" scenario. * The wait-and-see approach: A dedicated GPU environment is requested, but the IT team needs time to provision and tells the requester to check back in three weeks. In the past, this has been an annoyance but in the AI race, three weeks is an eternity and could be the difference in being an industry leader or a laggard. * The Wild West approach: Business units share a massively overprovisioned environment. It's faster to get into, but it's a security nightmare, and resource contention makes training runs highly unpredictable and ever harder to forecast when attempting to capacity plan. This inefficiency is more than just an inconvenience; it's a massive drain on return on investment, since time is money. When companies deal with hyperscalers or neocloud providers, they expect the kind of speed that Nvidia Blackwell B300s and RTXPRO 6000s promise. Having those chips sit idle while a developer fumbles a namespace configuration is the compute version of malpractice. QumulusAI and vCluster: partitioning power. The partnership between QumulusAI and vCluster brings customers a way to "slice and dice" high-end GPU power without the overhead of traditional virtualization. This gives customers more options but more importantly, the exact amount of GPU power they need to run their accelerated computing workloads, the primary one being AI. QumulusAI came to market with a value proposition of building a turnkey, vertically integrated AI cloud. Think of QumulusAI as a company that didn't just build a fast car, but designed the engine, the fuel and the highway it runs on. This "hyperspeed compute" setup provides massive power, but QumulusAI also provides the dashboard to keep all the horsepower under control. In fact, the company will let customers only use a piece of the engine if that's all that's required for the journey. By integrating vCluster's virtual Kubernetes technology, QumulusAI is essentially giving enterprises faster and more granular control of isolated environments. Instead of spinning up an entire physical cluster for every project, which is slow and expensive, teams can now spin up isolated virtual clusters on shared GPU hardware. This gives developers the "feel" of a dedicated environment - complete with their own application programming interface server and full control - while the platform team gets to maximize the utilization of those incredibly expensive GPUs. The vCluster AI Lab: innovation at the edge. Perhaps the most interesting part of this news is the launch of the vCluster AI Lab. The lab should provide QumulusAI customers assurance they can continue to use the platform for the long term. As the physical chips that are used for AI, such as GPUs, rapidly improve, the software managing them must stay ahead of the curve. This lab ensures that no matter how advanced the hardware becomes, the systems can handle the workload. It allows vCluster engineers to prototype how Kubernetes should handle emerging AI workloads in real time. Accelerating the move to AI factories. As I've noted in my previous posts, in 2026 the goal for companies should be to move AI factories from being projects to production infrastructure. To get there, organizations need three things: * Access: Getting the latest silicon (such as the B300) without a two-year lead time. * Isolation: Ensuring that Team A's training run doesn't crash Team B's inference model. * Speed: Moving from idea to environment in minutes, not months. This partnership addresses all three points and allows a midsized enterprise to act like a large company and enterprises to act like hyperscalers. They get the security of an isolated environment and the performance of bare-metal GPUs, all managed through a unified Kubernetes stack. Final thoughts. The AI race is going to be won by the companies that solve the operational headaches of GPU management. The technology is there, but can organizations deploy it in a way where it meets their needs now, doesn't break the bank and can scale with them? The partnership between QumulusAI and vCluster lowers the barrier to entry for secure, high-performance environments and makes it possible for AI teams to move as fast as their ideas. And in today's market, speed isn't just an advantage - it's the only thing that matters. Zeus Kerravala is a principal analyst at ZK Research, a division of Kerravala Consulting. He wrote this article for SiliconANGLE. Image: QumulusAI. A message from John Furrier, co-founder of SiliconANGLE: Support our mission to keep content open and free by engaging with theCUBE community. 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vCluster Labs has introduced vMetal, a bare metal machine management layer designed to help neocloud providers and AI factories provision and operate GPU infrastructure at scale. The platform automates the lifecycle of bare metal GPU servers, from provisioning and assignment to upgrades and repurposing. Combined with vCluster's tenant orchestration and AI-focused Certified Stacks, the vCluster Platform delivers what the company calls the industry's first unified AI infrastructure stack. The platform spans physical machines, Kubernetes environments and AI-ready application stacks, deployable across public cloud, private data centres and GPU neocloud providers. The initial Certified Stacks feature deep integration with NVIDIA Run:ai, which has validated vCluster as Run:ai conformant. The new release is available now.
vCluster Labs introduces Infrastructure Tenancy Platform for AI to maximize NVIDIA GPU efficiency on Kubernetes environments. New platform provides a Kubernetes-native foundation for running AI workloads on NVIDIA AI infrastructure, combining advanced isolation, dynamic scaling, and hybrid networking. ATLANTA (KubeCon + CloudNativeCon North America 2025, Booth #421) - November 10, 2025 - vCluster Labs, the company pioneering Kubernetes virtualization, today announced its Infrastructure Tenancy Platform for AI to help organizations build and operate high-performance AI infrastructure on GPU-focused compute clusters, including support for NVIDIA DGX systems. The company's new Reference Architecture for NVIDIA DGX systems is now available, offering architectural guidance for building secure, scalable Kubernetes environments optimized for NVIDIA AI infrastructure. Alongside this, vCluster introduced several new technologies, including vCluster Private Nodes, vCluster VPN, the Karpenter-based vCluster Auto Nodes feature, and direct integrations with NVIDIA Base Command Manager, KubeVirt, and the network isolation controller Netris, all of which form the foundation of the vCluster Infrastructure Tenancy Platform for AI, a unified framework for deploying and managing AI workloads on AI supercomputers in the private cloud as well as on top of hyperscalers and emerging neoclouds. "Our mission is to make AI infrastructure as dynamic and efficient as the workloads it supports," said Lukas Gentele, CEO of vCluster. "With our Infrastructure Tenancy Platform for AI, organizations running NVIDIA AI infrastructure can operate secure, elastic Kubernetes environments anywhere, with the performance, control, and efficiency that AI-scale workloads demand. It feels like getting the most cutting edge public cloud managed Kubernetes but on your bare metal AI supercomputer." Building blocks for the AI infrastructure era. As enterprises race to operationalize AI at scale, platform teams need a Kubernetes foundation that can manage GPU resources efficiently while ensuring workload isolation, mobility, and security. The Infrastructure Tenancy Platform for AI addresses these challenges through the following key innovations: * vCluster Private Nodes & Auto Nodes - Enable virtual clusters to dynamically autoscale GPU and CPU capacity across clouds, data centers, and bare metal environments using Karpenter-based automation. These features help maximize GPU utilization while maintaining full isolation and flexibility. * vCluster VPN - A Tailscale-powered overlay network that establishes secure communication between control planes and worker nodes across hybrid infrastructure. vCluster VPN simplifies burst-to-cloud scenarios, where GPU clusters seamlessly extend from on-premises NVIDIA DGX systems to public cloud environments. * NVIDIA Base Command Manager Integration - Integrates vCluster with NVIDIA Base Command Manager to bring Auto Nodes to NVIDIA DGX clusters, enabling elasticity, GPU lifecycle management, and efficient scaling across on-prem NVIDIA infrastructure. * KubeVirt Integration - Enables the creation of virtual machines on demand as nodes within a virtual cluster using KubeVirt, allowing large bare-metal servers to be partitioned into smaller, isolated compute units. This extends Auto Nodes to on-prem and bare-metal environments, giving platform teams elastic, tenant-aware GPU infrastructure under Kubernetes. * Netris Integration - Provides automated network isolation and lifecycle management for virtual clusters, giving each tenant its own dedicated network path and enabling multi-tenant GPU environments to run securely on shared infrastructure. * vNode Runtime - A secure, Kubernetes-native container sandbox that helps prevent container break-outs, enabling multi-tenant GPU workloads without reverting to VMs. Together, these technologies create the foundation of the vCluster Infrastructure Tenancy Platform for AI - a composable, Kubernetes-native framework purpose-built for running AI, ML, and GPU-intensive workloads anywhere. Industry analysts are increasingly highlighting the urgency of optimizing GPU utilization and simplifying AI infrastructure management. "As AI infrastructure becomes the new competitive frontier, organizations are under immense pressure to operationalize GPUs efficiently while maintaining security and governance across hybrid environments," stated Paul Nashawaty, Practice Lead and Principal Analyst at theCUBE Research. "We find that 71% of enterprises cite GPU utilization inefficiency as a major barrier to scaling AI workloads, and nearly two-thirds are exploring Kubernetes-native approaches to unify AI operations across cloud and on-prem. vCluster Labs' Infrastructure Tenancy Platform for AI directly addresses this gap by enabling dynamic, multi-tenant GPU orchestration with the same elasticity and control enterprises expect from the public cloud, now extended to private NVIDIA-powered AI systems." The new vCluster Reference Architecture for NVIDIA DGX systems outlines best practices for deploying virtual clusters on gpu-centric systems, enabling enterprises to deliver a cloud-like Kubernetes experience on-premises. With vCluster, teams can create lightweight virtual clusters that autoscale GPU resources, integrate securely with both on-prem and cloud networks, and maintain consistent performance across environments. "We've been using vCluster for a while and we love the technology," said Nick Jones, VP of Engineering at Nscale. "We're using vCluster to optimise GPU utilisation and accelerate Kubernetes cluster provisioning - delivering higher performance and efficiency that directly benefit our customers." Enabling cloud agility for NVIDIA GPU infrastructure. From AI factories to private GPU clouds, vCluster brings the scalability and efficiency of public cloud Kubernetes to NVIDIA environments. * Faster cluster provisioning - virtual clusters spin up in seconds with fully declarative provisioning via Terraform and GitOps * Higher GPU utilization - fewer idle GPUs across teams and tenants while ensuring fair use for everyone across the organization * Simplified day 2 operations - automated control plane and node upgrades, automatic backups with vCluster Snapshots and standardized guidance for integration into common cloud-native observability stacks Experience vCluster at KubeCon North America. Be among the first to experience the vCluster Infrastructure Tenancy Platform for AI at KubeCon + CloudNativeCon North America 2025 in Atlanta. Visit Booth #421 for live demos, technical sessions, and book signings. vCluster is also a Diamond Sponsor of Cloud Native + Kubernetes AI Day, where company leaders will present live sessions on GPU-accelerated Kubernetes operations, followed by a fireside chat featuring speakers from NVIDIA, JPMorgan Chase, and vCluster on "The Future of AI and Kubernetes."