The idea that a GPU can be priced in simple hourly units creates a false sense of transparency. In practice, what seems like a straightforward “x-dollar per GPU” model hides a long list of additional costs and dependencies that determine the true economics of AI infrastructure.
But the issue goes deeper. (Neo) Clouds focus narrowly on the price of a single GPU, ignoring the true system-level economics. The real costs sit on top of that GPU: high-speed networking at 100 to 200 Gbps that moves massive datasets, data egress and transfer fees, premium storage and tiering, software licensing, orchestration and scheduling overhead, and multi-tenancy inefficiencies that make performance unpredictable. Then there are operational layers that most people never see, such as 24/7 monitoring and support, governance and FinOps efforts, downtime or throttling during peak demand, and the integration work required to connect AI pipelines, networking, and storage into one coherent, optimised environment.
Once all of this is included, the “cheap GPU-hour” narrative quickly collapses. The GPU itself is only a small part of the total cost, and the rest lies in keeping the system efficient, secure, and operational at scale.
Most customers also face fragmented cost management, limited visibility, and multi-tenancy complexity. You often do not know who shares your hardware, how workloads are prioritised, or why throughput varies from day to day. Add to that hidden data-flow bottlenecks, unoptimised configurations, and opaque software stacks, and what seems cheap per hour quickly becomes the most expensive way to train or deploy AI once these things are factored in.
An owned AI Factory provides better performance in tokens per dollar and the same agility once associated only with the cloud, but with far greater cost predictability, control, transparency, sustainability, and security.
At MDCS.AI we bring that principle to life. Together with a network of specialised hardware and software partners, we design, implement, and manage end-to-end AI stacks that organisations truly own. Our model supports both CapEx and OpEx options with fixed monthly pricing, continuous hardware refresh, guaranteed performance based on validated reference architectures, full-stack optimisation from network to storage to software, transparent operations, advanced and auditable security, sovereignty, and a minimal CO₂ footprint.
The GPU is just the engine, but we deliver the chassis, wheels, steering, pit crew, and tuning, so you get predictable performance, consistent throughput, and full control. Because in the end, a “GPU per hour” says nothing about value. AI infrastructure is not a cost centre, it is a strategic asset that drives performance, independence, and long-term value creation.
The GPU is only the engine. If you do not control the rest of the car, if you do not have the team or the circuit, you will not win the race.
