Hyperscaler AI looks fast but hides long-term lock-in and rising costs. See how OpenStack and Kubernetes deliver GPU infrastructure you actually control.
Every industry is racing toward AI, and the fastest way to get started is usually hyperscaler GPU instances. You can spin up infrastructure, start training a model, and deploy it in hours.
The convenience is hard to resist. But the real costs often appear later.
Many teams optimize for time to first model and end up locking their infrastructure decisions in place. The storage system chosen for speed becomes home to terabytes of training data. The managed GPU service becomes the foundation of every pipeline. What began as convenience turns into dependency, with unpredictable costs and migrations that become harder every year.
The scale of the shift is enormous. Amazon, Microsoft, Alphabet, and Meta alone are expected to spend roughly $650 billion on data centers and AI infrastructure in 2026. That level of investment is concentrating compute power inside a small number of cloud platforms.
That is why more organizations are looking at open infrastructure. Platforms built on OpenStack and Kubernetes give teams control over GPU compute, storage, and networking through open APIs. OpenStack manages the infrastructure layer. Kubernetes keeps workloads portable. Together they allow AI to scale without handing long term control of the stack to a single provider.
§1 The Hidden Lock-In of Hyperscaler AI
Lock in rarely arrives as a single decision. It builds gradually through dozens of small choices that seem perfectly reasonable at the time.
It usually starts with the GPU instance. Every hyperscaler offers proprietary instance types with custom naming, unique configurations, and platform specific quotas. A p5.48xlarge on AWS has no direct equivalent on Azure or GCP. You are not just choosing hardware. You are choosing an architecture.
Then the pipeline begins to form. Training jobs connect to the provider’s managed storage. Experiment tracking integrates with their ML platform. Model registries, feature stores, and inference endpoints all connect to services that only exist inside that ecosystem. There is no portable equivalent and no open standard behind them.
The dependency grows at every layer.
Storage
Training data lands in proprietary object stores where egress fees make it expensive to move.
Networking
GPU communication relies on provider specific fabrics and placement groups that cannot be replicated elsewhere.
Orchestration
Even Kubernetes deployments often run on vendor managed control planes with proprietary extensions and CRDs.
Identity and access
IAM policies, secrets management, and service accounts are tightly coupled to the platform.
None of these choices feel like lock-in when you make them. Each one solves a practical problem. But six months into production the architecture tells a different story. Every component points inward toward a single provider.
The real cost appears when you try to leave. Migrating AI workloads off a hyperscaler is rarely simple. Pipelines need to be rebuilt. Storage architectures must be redesigned. Networking assumptions have to change. Models must be validated again in a new environment.
For many organizations the cost and risk of migration becomes so high that staying becomes the only realistic option, regardless of price increases, policy changes, or strategic misalignment.
That is not partnership. That is dependency. And the system is built to make that dependency hard to escape.
If you want to learn more about this topic, we highly encourage you to read this post.
§2 When AI Infrastructure Becomes a Sovereignty Issue
AI workloads do not just consume compute. They process some of the most sensitive data an organization has: patient records, financial models, government intelligence, and proprietary research.
Because of that, where the data lives and who controls the infrastructure beneath it are no longer just technical decisions. They are governance decisions.
Organizations increasingly need guarantees around data residency, jurisdictional control, and the ability to audit the infrastructure their systems run on. Hyperscalers operate globally under their own legal frameworks, which makes those guarantees difficult to provide in every situation.
That is why AI infrastructure is becoming part of the sovereignty conversation. Governments and regulated industries are no longer asking only how fast they can train models. They are asking who controls the platform those models run on and what happens when that control sits with someone else.
§3 The Cost Reality: GPUs Are Only Part of the Bill
GPU compute gets most of the attention, but it is only part of the cost of running AI.
Training large models is expensive even before the surrounding infrastructure is considered. According to Sam Altman, training GPT-4 is estimated to have cost over $100 million in compute alone, illustrating how quickly GPU spending can scale.
Storage grows quickly as well. Training datasets, model checkpoints, intermediate outputs, and inference logs accumulate over time, and they sit in storage tiers with pricing you do not control.
Networking costs appear more slowly but add up over time. Data moving between GPU nodes, across availability zones, or out of a provider’s network often carries additional fees.
Idle compute is another hidden expense. GPU instances provisioned for peak training runs frequently sit underused between jobs. Reserved capacity purchased to reduce hourly costs can easily become wasted spend when workloads shift.
The GPU hourly rate is only the starting point. The real cost of AI infrastructure includes storage, networking, data transfer, idle capacity, and managed services. Those are the pieces that quietly push hyperscaler AI bills far beyond what most teams originally expect.
§ 4. Open Infrastructure: A Different Model
There is another way to build AI infrastructure, and it is not experimental. It is already widely used.
Platforms built on technologies like OpenStack, Kubernetes, and Ceph provide the core building blocks needed to run AI workloads: compute, orchestration, and storage. The difference is that these systems are based on open standards rather than proprietary services. The APIs are open, the components can be replaced, and the infrastructure can be inspected and audited.
Because of this, the architecture is designed for portability instead of lock in. Workloads can move between environments because the underlying technologies follow common standards rather than vendor specific implementations.
For organizations running AI, this means less dependence on a single cloud provider and greater control over where workloads run, how data is stored, and how infrastructure costs evolve over time.
§ 5. OpenStack + Kubernetes: The Integrated Alternative
OpenStack and Kubernetes solve different parts of the infrastructure problem. OpenStack manages the underlying infrastructure, including GPU allocation, networking, storage, and identity across regions. Kubernetes manages the workloads, handling scheduling, scaling, and portability across environments.
Individually, they are powerful systems. Together they remove the need to depend on a hyperscaler.
OpenStack gives organizations direct control over the infrastructure layer. GPUs can be accessed through passthrough instead of proprietary instance types. Networking can be segmented without relying on vendor specific fabrics. Storage can be placed where it makes sense without worrying about egress penalties. Identity and access management remain under the operator’s control instead of being tied to a single platform.
Kubernetes ensures that workloads remain portable. AI training jobs, inference pipelines, and model serving endpoints are orchestrated through open, declarative APIs based on CNCF standards. If the infrastructure underneath needs to change, whether that means a new region, provider, or deployment model, the workloads can move without being rebuilt.
Atmosphere by VEXXHOST brings these layers together into a single production ready platform. It is built on upstream OpenStack and CNCF certified Kubernetes, without proprietary forks or vendor specific extensions. The goal is to provide AI ready infrastructure without forcing organizations into architectural compromises.
Also, whether you run on AWS, Google Cloud, Azure, OpenStack, bare metal, or VMware; VEXXHOST lets you deploy Kubernetes anywhere with the same platform and the same support, wherever your business needs it.
The platform supports GPU passthrough for full hardware performance, high performance networking with SR-IOV and DPDK, and flexible deployment across on premise, colocation, and hybrid environments. It provides the capabilities teams expect from a hyperscaler such as rapid provisioning, scalability, and managed operations, while preserving the openness and control that hyperscalers cannot structurally offer.
The cost model is different as well. There are no egress surprises, no idle instance traps, and no storage fees designed to discourage migration. There are no committed use contracts that reward staying instead of optimizing. Organizations pay for infrastructure rather than dependency. Because every component remains open and aligned with upstream projects, the option to move elsewhere always remains available.
Conclusion
The rush to deploy AI is real, but the infrastructure choices made today will shape cost, flexibility, and independence for years.
Hyperscalers offer speed, but they also create dependency. Proprietary services, tightly coupled storage, and pricing models that make leaving difficult can quietly lock organizations into a single platform.
Open infrastructure offers another path. OpenStack provides control over the infrastructure layer. Kubernetes keeps workloads portable. Atmosphere brings both together in a platform built for AI without locking organizations into one ecosystem.
The real cost of AI infrastructure is not just what appears on the invoice. It is the architecture behind it. Choose one that keeps control in your hands.
Explore Atmosphere and discover how open infrastructure powers AI without lock-in.
