Cloud vs colocation vs on-prem for AI compute

Once you've decided to run serious AI, you face a second decision: where does the hardware live? There are three real options - cloud, colocation, and on-prem - and they trade off cost, control, and effort very differently.

Cloud: fastest to start, priciest to stay

Renting GPUs in the cloud is the quickest way to begin and the easiest to scale up and down. The trade-off is cost at steady volume, data leaving your control, and dependence on a provider's pricing and rules.

On-prem: maximum control, maximum overhead

Running servers in your own building gives you total control - and total responsibility for power, cooling, connectivity, physical security, and uptime. For most teams that is a distraction from the actual work.

Colocation: own the hardware, rent the facility

Colocation is the middle path: you buy the servers (they are your asset) and place them in a professional datacenter that provides power, cooling, connectivity, and security. You get on-prem-level control without running a facility.

Colocation gives you the control of owning the box without the burden of running the building.

How they compare

• Cost at steady load: on-prem and colo win; cloud is most expensive.
• Time to start: cloud wins; colo and on-prem need procurement.
• Control and sovereignty: on-prem and colo win, especially in the EU.
• Operational effort: cloud and colo are low; on-prem is high.

The VANAFTER approach

We build the server, you own it, and we host it in an EU datacenter - colocation with the procurement and operations handled. You get sovereign, owned hardware without buying a facility or a forklift.

For bursty experiments, cloud is fine. For steady, sensitive, high-volume AI, owning the hardware and colocating it in the EU is usually cheaper, more sovereign, and barely more effort.