Local AI means the language model runs on a server you control, and your data never leaves the building. For many companies that is the decisive point. According to Bitkom, 66 percent of companies already using AI name data protection requirements as an obstacle, and 45 percent worry that company data could fall into the wrong hands. Open models are now good enough to handle many everyday tasks reliably. For very demanding tasks, large cloud models are still ahead.
How does local AI work?
Open models, whose weights are freely available, run on your own hardware. Well-known families include Llama, Mistral, Gemma, Qwen and OpenAI's open models. With a tool such as Ollama they can be installed and run on a server. On top sits an interface your team uses to chat as usual, for example Open WebUI. Automations, for instance in n8n, can use the local model too.
What are local models good at?
- Summarising texts and writing drafts
- Searching your own documents and answering questions about them
- Classifying inquiries and emails
- Extracting details from receipts and forms
- Translating and rewording texts
These tasks often involve confidential data, such as contracts, customer data or internal figures. That is exactly where local AI pays off.
Where is the cloud ahead?
For complex reasoning over many steps, very long documents and the newest capabilities, the large cloud models are usually stronger. The gap is narrowing, but it exists. Many companies therefore do well with a mix: sensitive data stays local, non-critical tasks run with a cloud provider under a data processing agreement and with processing in the EU.
What hardware do you need?
Small models already run on a normal server, though slowly. For several people to work smoothly, you need a graphics card with enough memory. How large the server has to be depends on which model you use and how many people work with it at the same time. You can test this in advance before spending money.
Does it pay off?
With the cloud you pay per use; with local AI you mainly pay for the server, however much you use it. According to Bitkom, infrastructure such as cloud and computing power is a major cost item for 51 percent of AI users. Local AI therefore pays off mainly with regular, heavy use or when data may not leave the building for legal reasons. How to calculate this in advance is shown in What does AI cost a company?.
Four questions for your decision
- Do you work with data that must not go to an external provider?
- Does your team use AI every day or only occasionally?
- Are good results on everyday tasks enough, or do you need the strongest models?
- Is there someone to look after the server, or will a partner handle it?
We run models via Ollama and our own AI chat ourselves, on servers in Germany. You can find an overview of our tools on our stack page.
Sources
- Bitkom: For the first time, most companies use AI (14 September 2026, German)
- Ollama
- Open WebUI
