GPUs are the new cloud lock-in. Learn how OpenStack, Kubernetes, and Atmosphere give you AI infrastructure control without hyperscaler dependency.
The AI gold rush is real. Spending on AI infrastructure is accelerating rapidly, driven by the global expansion of GPU-optimized data centers and specialized compute environments. And that investment reflects only the capital required to build capacity, not the operational weight that follows once these systems are deployed at scale.
But beneath the acceleration lies a structural shift most organizations won’t recognize until it’s expensive to reverse: the GPU you rent today can quietly become the platform you depend on tomorrow.
The cloud era conditioned us to worry about API lock-in. The AI era introduces something deeper: infrastructure gravity. When GPU capacity is increasingly concentrated within a small number of hyperscale providers, every AI workload pulls networking, identity, storage, and tooling deeper into proprietary ecosystems.
This isn’t just about pricing. It’s about control.
This post unpacks the new lock-in equation and explores how open infrastructure built on OpenStack and Kubernetes offers a different path.
§1 The GPU Rush Is Concentrating Power
AI demand is no longer experimental. It is reshaping capital allocation across the global technology economy. Analysts project that hyperscalers will spend roughly $650 billion on infrastructure in 2026, the majority of it tied to AI-driven data center expansion and GPU capacity buildouts. For context, that level of spending rivals the GDP of entire countries.
These expansions are happening under real constraints. Power availability is limited. Equipment lead times remain long. Permitting slows deployment. Capacity is not elastic. It is being allocated strategically.
At the same time, GPU supply itself is highly concentrated. NVIDIA controls the overwhelming majority of advanced AI accelerator market share, and a small number of large cloud providers account for a significant portion of its data center revenue. That concentration reinforces an uncomfortable reality: the companies building the infrastructure are also the primary gatekeepers of access to it.
When supply is constrained and demand keeps accelerating, access becomes leverage. GPU allocation shapes competitive timelines. Innovation velocity depends not only on engineering talent, but on whether compute capacity is available when needed.
Publicly listed GPU SKUs do not guarantee availability. Quotas, regional caps, approval processes, and preferential allocation are increasingly part of the equation. Multi-cloud strategies are often used as capacity hedges rather than optimization choices.
If you are not a hyperscaler, or tightly aligned with one, you are competing for access to the same finite pool of AI hardware.
And that concentration shifts the balance of power.
§2 The New Lock-In Equation
In earlier cloud cycles, lock-in revolved around APIs, proprietary databases, and managed services. In the AI era, it runs deeper. It sits in the infrastructure itself.
Hyperscalers now offer fully integrated AI stacks where models, training pipelines, deployment, monitoring, and governance are tightly connected. The convenience is undeniable. So is the dependency.
GPU availability anchors the stack. Storage, networking, and identity integrate around it. Over time, your architecture conforms to the environment hosting it. Exiting no longer means replacing a service. It means reworking the foundation.
At the hardware layer, NVIDIA’s ecosystem reinforces this dynamic. CUDA, NVLink, and related tooling shape how models are built and optimized. The stronger the integration, the harder the displacement.
The equation has changed.
Scarce GPU supply plus proprietary tooling plus coupled infrastructure equals structural lock-in.
§3 When GPU Dependency Becomes Platform Dependency
Renting GPUs inside a hyperscaler rarely means renting compute alone. It means inheriting the surrounding stack: networking, storage, IAM, observability, billing assumptions.
As AI systems mature, pipelines embed themselves into that ecosystem. Training depends on platform storage. Inference connects to proprietary load balancers. Monitoring integrates with native tooling. What began as experimentation becomes architectural alignment.
Eventually, portability erodes.
It is not surprising that conversations about workload repatriation are increasing. AI workloads challenge traditional cloud economics, especially when GPU-intensive scaling meets premium infrastructure pricing.
When AI strategy is anchored to a single provider’s GPU layer, architecture and budget tend to follow.
§4 Reclaiming Control: OpenStack and Infrastructure Sovereignty
If the problem is infrastructure gravity, the answer is infrastructure control.
OpenStack allows organizations to run GPU workloads on open, programmable infrastructure without tying compute decisions to proprietary cloud platforms. It separates hardware ownership from vendor-controlled ecosystems.
For AI workloads, that matters.
GPU scheduling, NUMA-aware placement, PCI passthrough, vGPU support, and multi-tenant isolation are not experimental features. They are production capabilities. Enterprises can manage GPU resources the same way they manage CPU and network infrastructure, with policy control and operational visibility.
OpenStack has been operating large-scale production clouds for over a decade. Retailers, financial institutions, research labs, and telecom providers already rely on it to run millions of cores globally. AI workloads are simply the next evolution.
Running GPUs on OpenStack means the infrastructure layer remains yours.
Your hardware.
Your networking.
Your policies. Your data.
If you want to learn more about how OpenStack reduces long-term cloud risks, we recommend reading this blog post.
§5 Kubernetes as the AI Workload Control Plane
If OpenStack provides infrastructure control, Kubernetes provides workload mobility.
AI workloads today run like any other distributed system: training jobs, inference services, data pipelines, autoscaling endpoints. Kubernetes orchestrates these patterns cleanly. GPU scheduling, job management, scaling, and rollout strategies are native concerns.
This abstraction matters. Kubernetes decouples the application layer from the underlying hardware. It allows AI workloads to move across regions, providers, and environments.
But portability is conditional.
Kubernetes running inside a proprietary cloud still inherits that cloud’s constraints. If storage, networking, GPU drivers, and identity systems are provider-specific, the portability remains theoretical.
Kubernetes delivers real mobility only when the infrastructure beneath it is open.
OpenStack anchors the infrastructure.
Kubernetes orchestrates the workload.
Together, they form an open control plane for AI. If you want to learn more about running Kubernetes in 2026, we recommend this blog post.
§6 Atmosphere: OpenStack and Kubernetes, Operationalized
This is where the architecture becomes operational.
Atmosphere is VEXXHOST’s OpenStack distribution, built to run natively on Kubernetes. It integrates infrastructure control and workload orchestration into a single, open platform.
OpenStack manages the infrastructure layer: networking, storage, identity, virtual machines, and bare metal.
Kubernetes orchestrates workloads, including GPU-accelerated AI jobs.
The separation is intentional. Infrastructure remains under your control. Workloads remain portable. GPU resources stay compute assets, not leverage mechanisms.
Atmosphere is fully OpenStack-powered and Kubernetes conformant. It delivers virtual machines, Kubernetes clusters, and bare metal services on-premises or in hybrid environments, without tying operations to a hyperscaler’s stack.
For organizations without internal cloud engineering capacity, Atmosphere can be delivered as a fully managed solution. The operational complexity is handled upstream. The control remains downstream.
With support for GPU acceleration, SR-IOV, high-performance networking, and multi-tenant resource management, Atmosphere is suited for AI, data-intensive workloads, and regulated environments where sovereignty matters.
Atmosphere makes the open architecture practical.
OpenStack provides infrastructure control.
Kubernetes provides workload mobility.
Atmosphere binds them without binding you.
You can learn more about how to run AI workloads on Kubernetes and OpenStack in 2026 in this blog post.
Conclusion
The risk is not GPU pricing. It is architectural dependency.
Every AI workload built inside a proprietary stack strengthens that gravity. AI strategy is now infrastructure strategy. Own the foundation, or rent your future.
Ready to take control of your GPU infrastructure? Explore Atmosphere and discover how open infrastructure powers AI without lock-in.