When a company decides to integrate a large language model in its processes, the question of local or cloud LLM deployment is rapidly coming to the fore. Cloud providers promise simplicity, power and flexibility. Supporters of the On-premise LLM invoke security, sovereignty and independence. Both are right, and both omit crucial elements.
Here's what nobody really tells you before you sign.
Real costs: neither is “cheaper”.”
That's the cloud's big selling point: there's no initial investment, you pay as you use. Attractive on paper. It's often misleading in production.
The trap of pay-as-you-go cloud pricing: when the bill explodes
Le cost LLM cloud is based on a per-request model, generally charged per token. For a few tests or a prototype, this is negligible. For a business application which processes hundreds of documents a day, carries out continuous analyses or feeds several AI agents in parallel, the monthly bill can quickly reach several thousand euros. Knowledge control the costs of your LLMs in production is not an option: it's a necessity as soon as you scale up.
Added to this are costs that are often invisible in initial comparisons: data output costs, additional costs linked to long contexts, differentiated pricing depending on the model, and unilateral price increases that you have no recourse to once your architecture is dependent.
The hidden cost of on-premise: GPU, maintenance, in-house skills
Le local LLM deployment isn't free either, far from it. A GPU server capable of running a high-performance model represents a significant hardware investment, between €15,000 and €80,000 depending on the configuration. To this must be added electricity consumption, infrastructure maintenance, model updates and, above all, the in-house skills required to manage all this. If your technical team has no experience of AI infrastructure, the real cost of on-premise is rapidly spiralling out of control.
How to calculate an honest TCO over 3 years
The right question is not “which one is cheaper to start with?” but “which one costs less over 3 years, at my actual usage level?”. Visit TCO artificial intelligence must take into account: the volume of monthly requests, forecast growth, the cost of internal or external skills, the risk of vendor lock-in, and the value of the data processed. For a Belgian SME With moderate and variable usage, the cloud often retains the advantage. For a company with a high and predictable volume, on-premise generally pays for itself between 18 and 24 months.
Security, data sovereignty and RGPD: the real challenge
This is the subject that cloud providers deal with at the bottom of the page, with reassuring but not very restrictive wording. And yet it is often the decisive factor.
What happens to your data when you use a cloud LLM
When you send a confidential document, a customer conversation or financial data to a LLM cloud API, In the case of the Internet, this data is transferred to servers located outside your infrastructure, often outside Europe. The data confidentiality with ChatGPT in the workplace is a subject that many companies discover too late, having already industrialised their uses. Even with solid contractual clauses, you lose physical control of the data as soon as it leaves your perimeter.
RGPD and artificial intelligence: what the law really requires
Le RGPD and artificial intelligence is a combination that many companies still manage intuitively. But the legal reality is clear: as soon as you process personal data via a LLM cloud, You must ensure that the supplier acts as a subcontractor within the meaning of the RGPD, that the data is not used to re-train models, and that you can exercise the rights of data subjects. These obligations are part of a wider context of dependence on the US cloud which is of growing concern to European companies concerned about their environmental performance. digital sovereignty.
On-premise and open source: Ollama, Mistral, LLaMA - what's possible today
The good news is that On-premise LLM is no longer reserved for large companies with data teams. Tools such as Ollama are now able to run models such as Mistral or LLaMA on a standard server, without specialist expertise. The performance of these LLM open source enterprise have made considerable progress: for many business use cases such as information extraction, classification or structured text generation, they can compete fairly with proprietary models, at a fraction of the cost over the long term. The on-premise and european cloud solutions now offer a real strategic choice, not just a technical compromise.
How to choose according to your actual situation
There is no universal answer. There are, however, objective criteria for making the right decision for your context.
You process sensitive or regulated data: on-premise
If your business involves medical, legal, financial or customer data, the local LLM deployment is an obvious choice. The regulatory and reputational risk of a data incident handled via a third-party cloud far exceeds the cost of a AI infrastructure on-premise solution. It is also the only approach that is compatible with certain customer specifications or sector certifications.
If you're just starting out or need flexibility: cloud
If you're in a phase of exploration, of proof of concept, or if your needs are still difficult to quantify, the AI cloud remains the most rational choice. Flexibility, the variety of models available and the absence of initial investment mean that you can iterate quickly. Platforms such as Azure OpenAI also offer stronger contractual guarantees than consumer APIs, in particular that the data will not be used for training purposes.
The hybrid approach: the best of both worlds
For many companies, the right answer is neither: it's both. A hybrid model consists of processing sensitive data locally via a Open source on-premise LLM, while using the cloud for non-critical tasks requiring more power. This architecture makes it possible to optimise costs, security and performance at the same time, by building a customised business application adapted to your real constraints rather than to a supplier's offer.
Iterates, your partner for deploying your LLMs in companies
At Iterates, we support Belgian companies in the choice and deployment of their AI infrastructure, with no bias towards one approach or another. Our only criterion: what corresponds to your real situation.
Audit and consultancy: choosing the right architecture from the outset
Before any technical decision is taken, we analyse your context: the nature of the data being processed, the volume of requests expected, regulatory constraints, available in-house skills and business objectives. This audit helps to avoid costly architectural errors that need to be corrected once the system is in production.
Customised on-premise LLM deployment for Belgian SMEs
We design and deploy local LLM infrastructures adapted to the size and resources of Belgian SMEs including selection of the most appropriate open source model, optimised hardware configuration, integration into your existing systems and full documentation for your team.
From proof of concept to production: our method
Our approach is iterative: we start with a proof of concept to validate feasibility and measure actual performance, before moving on to gradual, secure deployment. Each stage is documented, tested and validated with your teams, so that you can be sure of AI adoption that lasts.
Ready to choose the right AI architecture for your business?
On-premise or cloud LLM The question is not ideological. It's strategic, financial and legal. And the right answer depends solely on your context, not your supplier's sales pitch.
→ Discuss your LLM project with Iterates
