Grundlagen & Ratgeber
Open-Source LLMs in the Enterprise: Opportunities and Limitations
Open language models promise data sovereignty without vendor lock-in. But "open" is not the same as "open" — and self-hosting comes at a price. An honest look at licenses, advantages, and limitations.
For privacy-conscious companies, open language models sound like the ideal solution: the model runs in-house, the data never leaves, and no one can shut down or increase the price of the service. That is true — with important restrictions that should be known before committing.
"Open Weights" is not "Open Source"
The most common misconception first: "open weights" only means that the trained model parameters are downloadable and can be operated locally. This says nothing about the license — and training data is almost never published. True open-source licenses (such as Apache 2.0) on the other hand guarantee use for any purpose, without restrictions on user groups or fields of application.
Meta demonstrates how large the difference is: The Open Source Initiative explicitly states that the Llama license is not open source, and speaks of "open washing". Specifically, the Llama license requires a special license for more than 700 million monthly active users and a "Built with Llama" notice.
The most important open model families
Mistral (Small series) — e.g., Mistral Small 3 (24B) under Apache 2.0: true open-source freedom, ideal for self-hosting.
Meta Llama — very widespread, but under its own "Community License" with requirements — not OSI-compliant.
DeepSeek — DeepSeek-V3 (671B, MoE): code MIT-licensed, weights under own Model License, commercial use permitted.
Qwen & Gemma — mixed: partly Apache 2.0 (smaller Qwen models), partly own licenses with usage restrictions.
Mistral (Large) — under Research License: commercial self-use requires a commercial license.
The Real Advantages
For companies, one thing above all counts: Data Sovereignty. A self-hosted model processes prompts and company data in its own — or an EU data center, so that no third-country transfer occurs and the entire Schrems II issue is eliminated. In addition: no vendor lock-in (with Apache models, no one can shut them down or increase prices), predictable infrastructure costs instead of per-token billing, and the ability to adapt models via fine-tuning.
The honest limits
Self-hosting is not a walk in the park: large models require significant GPU resources, operation (scaling, monitoring, security) is the company's responsibility, and there is no provider SLA. Furthermore, the best open models still lag behind the proprietary top tier — the gap is shrinking, but it is real.
For many companies, the pragmatic path is therefore not "operate yourself or not at all," but a platform that provides open models in a German data center — with the sovereignty advantages of open source, but without the operational burden. The European Data Protection Board also emphasizes in its Opinion 28/2024, that control over processing is decisive.
Kasimir combines open models with German hosting: the data sovereignty of open source, without you having to operate your own GPU infrastructure.
Conclusion
Open-source LLMs are a powerful lever for data sovereignty — provided that one understands the licenses and knows the operational effort. Those who want the benefits without the burden choose a platform that provides sovereignly hosted open models. How to evaluate such a platform is shown in our Guide to the AI Platform for Medium-Sized Businesses, the data protection criteria in the article Data Sovereignty in the Company.
Sources
GDPR-compliant AI from a real German data center
Kasimir runs on its own infrastructure in Germany — no detour via US providers, no CLOUD Act reach.
