On-premise AI

On-premise AI means the models run on infrastructure you control: your own data centre, or a hosting provider with a named location. The difference from cloud is not the technology but the question of whose machines see your data while it is being processed.

When is running it yourself worth it?

Four situations argue for it.

Legal or contractual obligation: processing agreements with tight constraints, professional confidentiality, or customer contracts that specify where processing may happen. Procurement questionnaires now ask this regularly.

Internal approval: when data protection and the works council will only approve a project if the data stays in house, that is a requirement rather than a preference. IW Köln reports that 45% of companies prefer data centres located in Germany.

Very high usage: above a certain volume, per-request compute costs more than owning the hardware.

Stable tasks: when a model does one clearly bounded job indefinitely, it does not need to be the most capable one available. That is exactly where smaller self-hosted models do well.

What running it yourself costs

Not just hardware. A self-hosted model needs updates, monitoring, scaling for load peaks, and somebody accountable when it fails. That work recurs indefinitely, and it is why projects in this direction fail on staffing more often than on technology.

Then there is the capability gap. Self-hostable models have become good, but they trail the largest vendor models on demanding tasks. For drafting text or categorisation you will not notice. For complex reasoning you will.

The middle path most companies take

Rarely all or nothing. Usually split by data class.

A vendor model for tasks with no sensitive data, drafting text or public content. A self-hosted model for processes touching personal or contractual data. Which class goes where is a decision for AI governance.

One point gets overlooked here: in a RAG setup it is not only the model that matters but the vector database too. Content can be partly reconstructed from embeddings, so a hosted vector store is the same decision as outsourcing the documents.

Technically it helps to run the model behind your own interface. Switching between a vendor and self-hosting then becomes a configuration change rather than a rebuild of the application.

What to settle first

Four questions decide whether self-hosting is the right answer, and all four can be answered before any procurement.

Which data classes are genuinely affected? Often the constraint applies to only some processes, and that subset does not justify moving everything in house.

How many requests per month? Without that number there is no comparison to make between vendor pricing and your own hardware.

Who operates it day to day, and with what on-call expectation? This is the question that sinks projects in this direction most often.

And is a smaller model sufficient for the task? Testing that in an AI pilot with real data gives you a dependable answer instead of inferring one from model comparisons.