The comparison in brief
| Criterion | In the cloud | On your own server |
|---|---|---|
| Where the data is | With the service provider, under its rules | In the company |
| Upfront cost | Low, you pay for use | Higher: hardware and setup |
| Cost at heavy use | Grows with the number of requests | A fixed cost of hardware and upkeep |
| Model quality | Access to the largest models | Smaller models, enough for many tasks |
| Works without internet | No | Yes |
| Maintenance | The provider's job | The company's job: updates, backups, access |
First, the question about data
Before you choose a model, answer three questions: what data will go to the AI, who owns it, and whether client contracts or the law allow it to be handed to a cloud provider. If the answer is “I don't know”, that's the first thing to settle, even before the technology.
Free consumer versions of AI chats have different data processing rules from business versions and APIs. Don't paste client data, contracts or personal data there without checking the terms.
When your own server makes sense
- the data may not leave the company, or the rules require it,
- the tasks are repetitive and well defined, for example searching your own documents,
- the tool has to work inside the company network, also without internet,
- use is heavy and steady.
How much hardware you need is described in the guide to hardware for local models, together with a memory calculator.
When the cloud is better
- you need the best quality for complex tasks,
- use is light or irregular,
- you don't want to maintain hardware,
- you want to check quickly whether the idea works at all.
A hybrid setup
Often a sensible setup: sensitive data and simple tasks are handled by a model in the company, and for complex tasks, on data that may leave the company, a cloud model is used. The key is that the rule for which data goes where is written into the system, not left to the user's goodwill.
Security in every model
Your own server isn't secure by definition. Updates, backups, access control and monitoring are always needed, regardless of where the model runs.
Terms from this guide
- Language model (LLM)
An AI program trained on a large amount of text that can write, summarise, answer questions and organise information. It can be wrong and make things up, so its output has to be checked.
Questions and answers
Does a model on your own server learn from company data?
It doesn't have to. Most often the model uses company documents at the time of answering (this approach is called RAG), without being trained on them. That's easier to control.
What hardware is needed for AI in a company?
It depends on the size of the model and the number of users. The most important parameter is the graphics card's memory. Details and a calculator are in the guide to hardware for local models.
Can you start in the cloud and move to a server?
Yes, if the solution is designed that way from the start. It's worth deciding at the planning stage.