The short answer: put AI into production without vendor lock-in by using open-weight models run on-premise or in EU sovereign cloud, separating orchestration from the model and keeping data and logs inside your perimeter. That way you control data and models and switch provider without rewriting everything. The rest is detail, but it matters.
01What does vendor lock-in in AI really mean?
Lock-in is not the price per token. It is the inability to leave. When your prompts, integrations, training data and pipeline live only inside one provider's platform, migrating becomes a months-long project. And as long as leaving costs more than staying, you are not a customer: you are a polite hostage.
The European cybersecurity agency classified lock-in as a high risk back in its first cloud risk assessment, pointing to the lack of standards and of tools to move data and systems between providers (ENISA, Cloud Computing Risk Assessment). In AI the problem is sharper: the model is opaque, the API is proprietary, and the data you feed it today trains tomorrow's provider.
02Why is a "datacenter in Europe" not enough?
This is the most common trap. A US provider opens a region in Frankfurt and sells "data in the EU". But the US CLOUD Act follows the provider's control, not the location of the servers: a company subject to US law can be compelled to hand over data wherever it physically sits, and FISA 702 widens the reach further.
GDPR governs transfers, but it does not cancel this exposure. In their post Schrems II recommendations, European data protection authorities point to encryption with customer-held keys, retained inside the European area, as the technical measure that actually works. In other words: data residency is not data sovereignty. We go deeper on our European digital sovereignty page.
03How dependent are we, in numbers
This is not a niche paranoia: it is the structure of the market. Three citable figures, from institutional sources, capture European dependence.
Source: EuroStack initiative, Bertelsmann Stiftung, February 2025, in line with the European Parliament study on software and cyber dependencies (STUD 2025/778576). Building AI in production on this base, with no way out, means inheriting the dependence at the application layer.
04How do you build portable AI, in practice?
The P3 rule is just one: orchestration matters more than the model. The model is a replaceable component; the architecture around it is what keeps you free. Here are the four pillars we use.
Open-weight models, not closed boxes
Start from models with open weights, runnable on your own infrastructure. You do not need to train one from scratch: for almost every SME that is a waste. Take a ready model, run it where you want, adapt it with your data. If something better ships tomorrow, you replace it without touching the rest.
An abstraction layer between you and the model
Never call a provider directly from business code. Put a standard API layer in between: your software talks to that, and that talks to the model. Swapping model becomes a config line, not a refactoring.
Data and logs inside the perimeter
Prompts, responses, logs and adaptation data stay in your infrastructure. That is the difference between using AI and giving away your know-how to someone who might resell it to you one day.
Portability written into the contract
Portability is verified before you sign. The European Data Act (Regulation EU 2023/2854) dedicates Chapter VI to switching cloud provider, precisely to prevent lock-in: it applies from 12 September 2025 and requires switching charges, including data egress fees, to be withdrawn by 12 January 2027 (EUR-Lex, Data Act). Use it as contractual leverage: get export timelines and formats in writing.
05Where sovereign AI pays off, and where it doesn't
Anti-hype, always. On-premise AI is not the answer to everything. It pays off when data is sensitive, regulated or strategic: medical records, industrial designs, internal communications, security. There, control beats convenience.
For low-risk, low-confidentiality cases, a managed service can be fine, as long as the way out stays open. The point is not to refuse the cloud on principle. It is not to build anything critical on a door you cannot reopen. It is the same principle behind Fenrir, our SOC/MDR with sovereign AI: detection runs on controlled infrastructure, logs do not leave, the model is replaceable.
06Where to start, concretely
The typical mistake is to start from the model. You start instead from the map: which data the AI would touch, how sensitive it is, where it would end up. That is why the first step we propose is almost always an exposure audit: a snapshot of data, flows and dependencies, before choosing any technology.
From there, adoption becomes a sequence of informed decisions, not a leap in the dark. Pick the use cases, pick the models, define the way out. The hard part is not making AI work. It is making it work without handing over the keys to the house. If you want a discussion on your case, write to us or look at our sovereign AI and cyber products.