Running your own AI model: open source LLMs for privacy and control

Key Takeaways: Most companies use AI through the cloud: you send your question to a vendor's servers and get an answer back. For general work that is fine, but for sensitive data it is not always desirable to send everything out the door. Open source language models that you run yourself (such as Llama and Mistral) then offer an alternative: you keep full control over where your data goes, which is attractive under the GDPR and for sectors with strict requirements. The downside is your own infrastructure, costs and management. This article explains when running your own LLM is smart and when cloud AI is simply enough.

The core difference: where does your data go?

With cloud AI (ChatGPT, Claude, Microsoft Copilot) the model runs on the vendor's servers. You send your text there, the model processes it, and you get an answer. Fast, scalable, no infrastructure of your own, but your data does leave your own environment.

With a self-hosted open source model you download the model and run it on your own servers or a shielded private environment. Your data stays within your own walls. You trade the convenience of the cloud for control and privacy.

For most tasks that difference is not decisive, with a solid business agreement, cloud AI can also be GDPR-compliant and secure. But for specific situations the balance tips.

When running your own LLM is smart

Three situations make a self-hosted model worthwhile.

Strict data privacy requirements. If you work with medical records, legal files, financial data or other particularly sensitive information, the requirement may be that this data never leaves your environment. Your own model makes that watertight.

Sector or client obligations. Some clients or regulators contractually require data to stay within certain boundaries. Your own model gives you the proof and the control.

High, predictable volume. If you process enormous amounts of text, ongoing cloud API costs can add up. At a certain point your own infrastructure becomes more economically attractive than paying per use.

Outside these situations, cloud AI is usually the pragmatic choice: less hassle, no management, and secure enough with a solid agreement.

What it costs and requires

Your own model is not "free because it is open source". The software is free, but you pay for the infrastructure (hardware or cloud capacity with enough computing power), the setup (getting the model up, connected and tuned) and the management (keeping it running and up to date). For lighter models that stays limited; for large models with heavy use it adds up.

The right comparison is therefore not "free versus paid", but ongoing cloud API costs versus your own infrastructure and management. At low usage the cloud almost always wins; at high volume or strict privacy requirements it tips towards self-management. And like any model in production, your own LLM also needs MLOps to stay reliable.

Does it perform well enough?

A fair question. The best open source models have improved strongly in recent years and are more than good enough for many business tasks, a knowledge base chatbot, document processing, classification. On the very heaviest, most complex tasks they often still lag slightly behind the largest commercial models, but for well-defined business use cases that difference is usually small in practice. The question is rarely "is it the very best model?" but "is it good enough for this task, with the privacy I need?".

How to make the choice

Start with your data, not with the technology. Ask: how sensitive is the data that passes through the AI, and are there requirements that determine where that data may live? If the answer is "not particularly sensitive", then cloud AI with a solid agreement is the pragmatic route. If the answer is "highly sensitive" or "contractually bound", then a privacy-friendly self-hosted model is worth considering. Then calculate the costs honestly, volume often tips the balance.

Stratalytic and privacy-friendly AI

We choose the setup that fits your data and requirements, cloud or self-managed:

• Data-first advice: we determine what makes sense based on your data sensitivity, no standard answer.
• Self-hosted models: setup and management of open source LLMs on your cloud or on-premises, with data that never leaves your environment.
• Maintenance built in: MLOps so your own model stays reliable; you are the owner.
• Subsidy: development and infrastructure often qualify under WBSO.

Schedule a 30-min intro call and we will determine whether cloud AI is enough or whether a self-hosted model fits you.

Frequently asked questions

What is an open source LLM? An AI language model that you can freely download and run yourself (such as Llama or Mistral), instead of using it through the cloud. You keep control over where your data goes.

Is running your own LLM better for privacy? For sensitive data it can be, your data never leaves your environment. But cloud AI with a solid business agreement can also be GDPR-compliant.

What does it cost? Infrastructure, setup and management. The trade-off is ongoing cloud API costs versus your own infrastructure, volume often tips the balance.

Does it perform as well as ChatGPT? More than good enough for many business tasks; on the heaviest tasks sometimes slightly less. For well-defined use cases the difference is usually small.