A local language model makes sense when two things are true at once. The first is that the company handles data it can’t send to an outside provider, or simply doesn’t want to. The second is that it has steady, repeatable uses for the model, such as searching documentation or handling support tickets.
What if you care more about the strongest models and a quick start without building your own infrastructure? Then a cloud API usually wins. Below I compare both options and list the signs that point to an on-premise deployment. I also cover the questions to ask a vendor and the first step to take.
Table of contents
What is a local language model and how is it different from a cloud API?
An on-premise LLM is a model that runs on the company’s own server or in its private infrastructure, so queries never go to an outside provider. A cloud API works the other way round. You pay per use, the model runs on the provider’s side, and the content of every query leaves the company. In both cases we are talking about inference, which means an already trained model generating answers. A large ecosystem of tools has grown up around open models. As the Transformers library documentation explains, a shared model definition lets you run the same model in different inference engines and training frameworks. In practice, switching tools later on hurts much less.
Cloud API or on-premise LLM: a comparison in four areas
The differences come down to four things: who controls the data, how the costs add up, what quality you get, and who maintains it all.
- Data: in the cloud, it lands in the provider’s infrastructure and falls under the provider’s rules. With a local model, it stays on servers where you decide who has access.
- Cost: an API means a subscription plus token fees that grow with usage. Your own server means a one-off hardware purchase, followed by ongoing costs for power and upkeep.
- Quality: the cloud gives you the latest commercial models. Local setups usually run smaller open models, fine-tuned for a specific task.
- Maintenance: with an API, it’s the provider’s headache. With an in-house deployment, it’s your own team’s or a technology partner’s.
I would also add the risk of changing prices and terms to the calculation, in other words dependence on a single provider. As you scale, it can get expensive. But you don’t have to pick just one. Some companies combine both options and route each task according to how sensitive the data is.
When a local language model pays off and when it doesn’t
It usually pays off when the data is sensitive and there are many queries coming in regularly. What are the strong signs in favour? Personal data, trade secrets, internal documentation, client requirements or industry regulations, a steady query volume, and working without internet access. Against it: occasional use, nobody to look after a server, and a need for the strongest models for complex reasoning. Let’s be honest, in that last case the cloud usually wins. Typical business uses include summaries, ticket classification, an internal assistant, and document search with RAG (the model answers based on passages retrieved from the company knowledge base).
GDPR-compliant AI: what in-house data processing gives you
A model on your own server makes it easier to control where personal data is processed and by whom. The data doesn’t go to yet another party, and that’s that. But GDPR still applies. You still need a legal basis for processing, data minimisation and access control, and the server’s location doesn’t exempt you from any of that. In practice, what matters is query logs, role-based permissions, retention rules (how long you keep conversations and documents) and keeping the test environment separate from production.
What a good deployment of a model on a company server looks like
It starts with a single process, not with buying hardware, however tempting that is. The stages usually go like this:
- Choose one process and the data the model will work on.
- Test several models on a sample of the company’s real documents.
- Pick hardware for the model that performed best.
- Integrate with company systems, such as a document repository or a helpdesk.
- Set up access control and monitor answer quality.
- Hand over documentation and a maintenance plan.
After launch, the system doesn’t run itself. The model needs updating, security needs patching, and backups need making. That is why IT infrastructure maintenance is planned from day one, not after the first outage. Web Systems, a software house from Łódź operating since 2006, also builds its own AI products: Ragable, Botino and Inteleo.
What questions should you ask a vendor before deploying an LLM?
The most important ones are about testing on your data, maintenance and what the company ends up owning. Before you sign a contract, ask:
- What data do you test the model on, and how do you measure answer quality?
- What hardware do you recommend, and why that particular setup?
- Who maintains the system after deployment?
- How do access control and logging work?
- Can the solution be moved to a different model?
- What does the company own once the project ends?
Red flags? Promises of results without a test on the company’s data, no maintenance plan, and an architecture locked into a single model. If you spot any of these, it’s better to stop.
Where to start: a local language model in your company
Start by picking one process and a set of data that can’t go to the cloud today. Then run a short pilot before you buy hardware. Test the model on real documents and review the answers with the people who will use them every day. A test like this will show whether a smaller model is enough or whether you need a more powerful server or a hybrid setup.
Considering a local language model and want to check if it’s the right direction for you? Book a free consultation. We’ll talk about deploying a model on your server with your data and processes in mind.
Frequently asked questions
Is a local language model as good as cloud models?
It depends on the task. For narrow uses on your own documents, such as search or summaries, it is often more than enough. For complex reasoning, the latest cloud models can be stronger. The fairest approach is to compare both options on the same data sample.
Does an on-premise LLM automatically meet GDPR requirements?
No. It makes data easier to control because the data doesn’t go to an outside provider, but your obligations as a data controller remain. You still need a legal basis for processing, access and retention rules, and documentation.
Can you combine a local model with a cloud API?
Yes, that’s a hybrid model. Sensitive data is handled by the model on your own server, while tasks without personal data or company secrets can go through the API. There is one condition: clear rules on which queries go down which path.
