On-premise LLM vs cloud AI API: how to choose

There are two ways to put a language model to work on company data. Send the data to a model someone else runs, or run a model yourself. Both work. They differ in where your data goes, how the bill behaves and how much you depend on someone else's decisions.

This guide sets the two side by side so the choice can be made on facts rather than on fashion.

01What each one is

A cloud AI service: you send text over the internet to a model run by a provider, and pay for what you send and receive.
An on-premise model: an open model runs on hardware you control, in your building or in a private environment you rent. The text never leaves it.

02Where the data goes

With a cloud service, the text of every request travels to the provider. Contracts can limit what the provider does with it, and for many uses that is enough. It remains a promise about someone else's systems.

With an on-premise model there is nothing to promise. The request is handled inside your network and can be shown to stay there.

03How the cost behaves

Cloud: no purchase up front. Cost rises with every request, which is ideal for low or uneven use and uncomfortable at high, steady volume.
On-premise: hardware and set-up first, then a cost that barely moves with volume. Reading a million records costs about what reading a thousand did.
The crossover depends on volume. A task that runs over every invoice, every material or every document, every day, usually favours your own hardware.

04Quality and speed

The largest hosted models are the strongest at open-ended writing and reasoning. For the work most companies need done on their own data — reading a description, classifying a document, extracting fields, matching records, answering from a known set of documents — smaller open models, well set up, are accurate and more consistent from one day to the next.

A local model answering over a local index also avoids the round trip to another continent, which matters when the task runs thousands of times.

05What each needs from you

Cloud: an account, a budget alarm and a clear rule about which data may be sent.
On-premise: hardware sized to the work, someone to monitor it and a plan for updating models. Much of this can be handed over once set up.

06A sensible default

Decide per task, not once for the whole company. Sensitive data, high volume and work tied to internal systems go on your own servers. Public content and occasional creative work can use a cloud model. Many companies end up with both, with the on-premise side as the default and the cloud as a deliberate exception.

In short

Cloud AI sends your text to a provider; an on-premise model keeps it inside your network.
Cloud cost rises with use; on-premise cost is mostly fixed, which favours high, steady volume.
For reading, classifying, extracting and matching company data, well-chosen local models are accurate and consistent.
Choose per task: sensitive and high-volume work on your own servers, with cloud use as an explicit exception.

Questions

Do we need expensive graphics cards to run models ourselves?

Less than the headlines suggest. Much enterprise work is rules and retrieval with a model used sparingly, and many tasks run on modest hardware. The hardware is sized to the task, not to the hype.

Are open models safe to use commercially?

Many are released under licences that allow commercial use. Check the licence of the specific model, as you would for any software.

Can we start in the cloud and move later?

Yes, if the system is built so the model is a replaceable part. Ask for that at the start; it is hard to add afterwards.

Which does Quantum Beetle use?

Local models on the client's own infrastructure by default. An outside AI service is an explicit choice the client makes for a specific task, never a requirement.

Sounds like your problem?

Tell us about it. We'll say honestly whether the swarm can help, and what it would take.

Talk to us hello@quantumbeetle.ai

On-premise language models or a cloud AI service: how to choose for company data