An open (or open-weight) model is an AI model whose weights — the trained "brain" — are publicly available and can be run on infrastructure of your choosing. The alternative is closed models, accessible only through a vendor's API: powerful, but "black boxes" you have to send your data to in order to get an answer.
With a closed model, the data goes to the model; with an open model, the model comes to the data.
With an open model running internally, sensitive data never leaves your perimeter. No sending it to foreign APIs, no transit through cloud infrastructure outside Switzerland, no risk of your content feeding the training of a third party's model.
A model you own is portable and stable. You don't depend on one vendor's decisions: price increases, changed terms, deprecated features or sudden service interruptions don't expose you. The system you build today remains yours tomorrow.
Open models are inspectable. You can verify their behavior, adapt them to your domain, subject them to security review and document them for an audit. In a regulated context, being able to explain how a system works matters just as much as the fact that it works.
Above a certain volume of usage, owning your own infrastructure becomes more predictable and more economical than a per-call cost that grows without limit. The costs are yours, controllable and plannable.