On-premise LLM vs cloud AI API: how European companies should choose

With a cloud AI API, your software sends a request over the internet to a provider, the provider runs a model in its own data centre, and the answer comes back. You never see the model. You pay for what you use, usually per request or per amount of text, and the provider decides which model versions exist and for how long.

With an on-premise LLM, you download an open-weight model and run it on machines you control: a server in your office, or a dedicated machine rented in a data centre you choose. The second variant is often called private cloud. The model, the prompts and the answers stay on that hardware.

There is a third shape worth knowing about. A small model can run on the user’s own device, inside the browser. 24U works this way: the model is fetched once, runs on the visitor’s hardware, and no draft travels to a server. It suits narrow tasks where each user handles their own text.

This section is an engineering view, not legal advice. Your data protection officer or lawyer has the final word, and the answer depends on what data you process and why.

When you call a cloud API with personal data, the provider usually acts as your processor. Article 28 of the GDPR requires a written contract with a processor that limits what it may do with the data. Most providers offer a standard data processing agreement; read it rather than assume it. If the provider or any of its sub-processors handle the data outside the European Economic Area, the transfer rules in Chapter V apply, which means an adequacy decision or safeguards such as standard contractual clauses.

Before sending real documents to any API, get written answers to four questions: in which region is the text processed, how long are prompts and outputs kept, is any of it used to train models, and who are the sub-processors.

On-premise removes the AI provider from the data flow, which makes it far easier to describe and audit. If the machine is rented in a data centre, the host is usually still a processor, so you still need an Article 28 contract with it, and Chapter V rules if it sits outside the EEA. It does not remove your own duties. You are still the controller, Article 32 still requires appropriate security, and logs of prompts are personal data too if people’s names are in them. Lawyers, doctors and accountants should also check their professional secrecy rules, which can be stricter than the GDPR about letting client files leave the firm at all.

The two options are priced in opposite ways, so comparing a single month tells you little. Compare the shape.

A cloud API costs almost nothing to start and grows in line with use. Ten times the questions means roughly ten times the bill. That is ideal for a pilot, a seasonal spike or a feature nobody is sure will be used. The provider also sets the price, and can change it.

On-premise is the reverse. You pay up front for hardware, or monthly for a rented machine, plus power and the time of whoever keeps it running. After that, one more question costs almost nothing. Idle hardware still costs money, so the economics favour steady, heavy use: a team querying an archive all day, or an automation processing every incoming document.

• Cloud costs people forget: rework when a model version is retired, rate limits at busy times, and the tokens spent on long documents sent with every question.
• On-premise costs people forget: monitoring, security updates, someone available when the server stops, and replacing hardware as models grow.

Latency. A cloud request includes the network round trip and a queue shared with other customers, so response times vary through the day. A local model has no network hop, and a small model on a good graphics card can feel instant. A large model on modest hardware is slow, though, so speed on-premise is something you size for, not something you get by default.

Quality. The largest cloud models are still generally stronger at open-ended reasoning and long, complex writing. Open-weight models have closed much of the gap on narrow, well-defined work: summarising, classifying, extracting fields, and answering questions over your own documents. For that last task, retrieval often matters more than model size. Size is not the whole story. A smaller model that is handed the right three paragraphs beats a larger one that guesses. Our retrieval demo shows the idea: it grades its own documents before it answers. Whatever you read, test both options on twenty real examples from your own work before deciding.

Upkeep. In the cloud, the provider patches and scales everything, but it also retires model versions on its own schedule, and a prompt that worked in spring can behave differently in autumn. On-premise, nothing changes unless you change it. That stability is valuable for audited processes, and it is paid for in maintenance work.

The same criteria, side by side. Neither column wins every row, which is why the decision depends on your data and your volume.

Choose the cloud API when the data is not sensitive, when you are still finding out whether the feature is useful, when usage is low or unpredictable, or when the task needs the strongest reasoning available.

Choose on-premise when client files, health records, contracts or case notes must not leave your infrastructure, when the same task runs thousands of times a week, when an audit requires the model to behave identically over time, or when you need the system to work without an internet connection.

The hybrid is common and sensible. Route by source, not by guessing at content: anything that comes from the client-file system or case management goes to the local model, while general drafting and research go to the cloud. Spotting personal data inside free text is unreliable, so the source is the safer rule, and it keeps the expensive question small. If you want the local half built, that is what our private AI and on-premise LLM deployment service does.

Answer these in order. The first few usually settle it.

• Will the model see personal data, client files or anything under professional secrecy?
• If yes, can you get a data processing agreement, a processing region and a no-training commitment in writing?
• Would any processing happen outside the EEA, and is there a valid transfer mechanism for it?
• How many requests a day do you expect in six months, and is that use steady or seasonal?
• Does the task need open-ended reasoning, or is it classify, extract, summarise and answer from documents?
• Have you tested both options on twenty real examples and compared the answers side by side?
• Must the system behave the same way next year for audit or compliance reasons?
• Who will patch, monitor and restart a local server, and is that cost in the plan?