In short: Whether AI should run in the cloud or on your own hardware is something I decide per task, not in principle. Five questions help: which data is processed? May it leave the house? What quality and speed are needed? How often is the model used? And who runs the solution? The best answer is often hybrid: sensitive data stays local, non-critical tasks run in the cloud.
Why local models interest me
A local language model runs on your own hardware. The input doesn't have to be sent to a third-party provider for processing. That is a real advantage for source code, internal documents and recurring tasks with many requests.
But local doesn't automatically mean secure. Web interfaces, network shares, logs, backups and additional tools have to be secured just the same. A model API that is reachable on the network without protection is a risk, even if the model sits in your own office.
The cloud remains useful
Cloud models are more convenient for many tasks: no model download, no hardware, usually very capable models and plenty of tools around them. In return, you need to keep terms of use, data processing and running costs in mind.
I use cloud AI for brainstorming, editorial drafts, public information and as a sparring partner while developing, for example. What I never paste into a chat is described in AI in Everyday Development.
Five questions for the decision
- Which data is actually processed? Public product information is different from customer data or internal code.
- May this data leave your own infrastructure? Contracts, data protection law and sometimes simply your own standards decide.
- What quality and response time does the process need? A chat needs fast answers, an overnight run over thousands of texts doesn't.
- How often is the model used, and what does it cost? With few requests, the cloud is cheaper. With very many requests, your own hardware can pay off because there is no cost per request.
- Who runs, monitors and updates the solution? Local AI is a system that needs care: updates, power, backups, network.
Hybrid is often the best answer
In practice it is rarely either-or. An example from the shop world:
- local: analysing internal documents, processing customer data, bulk processing of product descriptions
- cloud: translating already approved texts, researching public topics, drafting blog articles
The key is to decide for each use case separately. An internal knowledge system has different requirements than translating approved product texts.
What really matters with local AI
The number of parameters says little about everyday usefulness. More important are:
- Memory: large models need a lot of fast memory, which runs out quickly on normal hardware.
- Context: how much text can the model process at once?
- Speed: a model with excellent answers helps little if every answer takes minutes.
- Quantisation: models can be compressed to need less memory. How much quality and speed suffer depends on the specific variant.
- Tooling: interface, API, integration into the IDE or your own scripts.
How I evaluate models
When I test a model, I use real tasks instead of leaderboards:
- Does it understand German and English technical requirements?
- Can it handle longer code sections consistently?
- Does it say when it is unsure, or does it invent APIs?
- Does the response time fit my workflow?
- Can it be integrated sensibly into my tools?
How I now run local AI myself is the subject of a separate article.
Conclusion
The question is not "local versus cloud" but "which tool for which task". Your own AI hardware opens up new possibilities, but it replaces neither a security concept nor expert review of the results.
Frequently asked questions
Is local AI automatically privacy-compliant?
No. The data stays in-house, but access, logs, backups and network shares still have to be handled properly.
Is local AI cheaper than the cloud?
With very many requests, often yes, because there is no cost per request. In return you pay for hardware, power and your time running it. For occasional use, the cloud is usually cheaper.
Are local models as good as cloud models?
For many well-defined tasks they are enough, and some deliver very good results. For particularly demanding tasks, the large cloud models are often still ahead. That is why it pays to test with your real tasks.