On-premise vs. cloud LLM: when local AI is the right choice
On-prem or cloud is rarely a model question. It is a question of data sovereignty, cost, and control.
This translation was produced automatically using AI. The German version is the editorially reviewed original.
The decision between a locally run language model and a cloud service is rarely a question of the best model. It comes down to three points: where your data is allowed to sit, how predictable your load is, and how much control over operations you want to keep. Whoever starts with these three questions arrives at a robust answer. Whoever starts with the model name optimises for the wrong thing.
This piece sets both paths side by side soberly and describes which path holds up for which situation.
01. The question is not the model, it is the data
A cloud LLM means that your requests, along with the context sent with them, reach a third-party server. For much content, that is unproblematic. For contracts, personal data, design documents, or patient data, it is often the point where the legal department stops the project.
On-premise means the model runs on your own hardware and no dataset leaves the building. You trade the convenience of a managed provider for full control over data and availability. We described the lesson from a provider switch that no user decided on in The Fable-5 sovereignty lesson.
02. On-premise and cloud in direct comparison
The following comparison summarises the points where the two approaches genuinely differ. Neither is fundamentally superior; they suit different situations.
| Dimension | On-premise | Cloud |
|---|---|---|
| Data sovereignty | Data stays in-house | Data leaves the building |
| Setup effort | Hardware and setup required | Usable within hours |
| Cost at constant load | Usually cheaper per request | Usually more expensive per request |
| Cost at fluctuating load | Hardware sits idle at times | Pay only for actual use |
| Control over availability | Fully in your hands | Depends on the provider |
| GDPR and auditability | Directly demonstrable | Via data processing agreement |
03. The cost calculation, honestly worked out
Cloud looks cheaper at first, because no hardware is needed and only actual usage counts. That holds as long as load is low or fluctuating. Once many requests run constantly throughout the day, the calculation often flips, because every request is billed individually.
On-premise reverses that relationship: buying the hardware is a one-time investment, after which the cost per request drops significantly. The fair comparison therefore does not calculate the list price per request, but the total cost over two to three years at your expected load. What hardware is realistic for this and what it costs is covered in Local LLM in the enterprise: hardware, cost, reality.
04. Data sovereignty, GDPR, and the EU AI Act
For regulated industries, the decision is often already made before costs even come into view. If data may not leave the EU, or an auditor needs to trace exactly where processing takes place, running locally is the more direct proof. A comparable outcome can be achieved with a cloud service through a data processing agreement and EU hosting, but the effort required to demonstrate it is usually higher.
The EU AI Act tightens this requirement further for certain use cases. We cover what specifically businesses need to do and by when in EU AI Act 2026: what businesses need to implement now.
05. A pragmatic middle path
In practice, the answer is rarely one-sided. Many companies run sensitive workloads locally and use the cloud for non-critical tasks where speed and peak performance matter. Open models make this middle path easier, because the same model can run locally and in an EU cloud without switching providers.
Recent releases show just how far open models now carry, for instance Kimi K2.7 Code and NVIDIA's Nemotron 3. The sensible starting point is therefore not a fundamental commitment to one camp, but a case-by-case assessment of each workload by sensitivity and load.
