AI and on-premise LLMs: The dilemma when selling to German hospitals

AI is transforming our lives, but in digital health the road to innovation is never smooth.

In Germany, many hospitals and health insurers are saying no to cloud-based solutions. Why? Security concerns and a need for strict (even unrealistic) certifications.

This makes deploying large language models (LLMs) a challenge. Centralized AI in the cloud is fast and efficient, but for many healthcare institutions, it's a non-starter.

So the question is: can LLMs work on-premise?

Let’s explore what this really means, what the trade-offs are, and how startups can navigate this complex, yet exciting, opportunity.

1. Cloud vs. On-Premise: A matter of trust

Cloud-based AI has obvious benefits. It’s fast to deploy, easy to maintain, and offers scalability without the headache. For startups, especially, the cloud is the default. You build once and serve many.

But when selling AI for hospitals in Germany, trust is paramount. Hospitals and insurers are bound by strict data protection laws, regulatory requirements, and a deep-rooted culture of caution 🚑. That’s why many demand on-premise solutions.

This feels safer to institutions—data stays local, and they maintain control. But for LLMs on-premise deployment introduces major hurdles: setup, updates, scalability, and cost 💸. For startups, these hurdles can seem insurmountable. You’re no longer maintaining one product. You're managing dozens of unique deployments. That’s not scalable in the long run.

2. Can on-premise LLM deployments work?

The short answer is no. On-premise LLMs can work but they don’t scale. LLMs are resource-heavy. Running them locally means having updated hardware and a dedicated DevOps team most of the time. You also need to think about regular model updates, bug fixes, and security patches.

Also worth noting: every hospital has different IT systems, security policies, and budgets. That means every on-premise deployment becomes a custom job. You can quickly find yourself buried in complexity.

Many startups have successfully deployed on-premise LLMs, especially with smaller, fine-tuned models that serve very specific use cases (e.g. summarizing clinical notes or handling internal queries). But even then, the costs can stack up fast.

So while it’s technically possible, it’s very inefficient, especially at scale.

3. Data anonymisation isn’t a silver bullet

Another common question is: “Can’t we just anonymise the data and work in the cloud?”

Unfortunately, it’s not that simple. Proper data anonymisation is hard. GDPR requires you to prove that the data cannot be re-identified if it leaks. This requires adding significant noise to the data, making it harder to use it for AI. Getting anonymisation right requires expertise, time, and robust validation. Even then, clients might still feel uneasy, especially when you are talking about health data.

So while anonymisation should be part of your toolkit, it’s not a magic solution. Legal, technical, and human trust factors must all align.

4. Are there any other alternatives?

If cloud is too risky and on-premise is too heavy, what else is there? Enter hybrid solutions like:

• Edge computing: Here, the sensitive parts of the model are run locally (on devices or local servers), while sending non-sensitive tasks to the cloud.
• Distributed LLM training: This approach splits the workload across multiple devices or servers. Data stays local, but insights are shared.
• Multi-party computation (MPC):  Here, different parties to jointly compute results from their data—without ever sharing the raw data itself.

These are exciting technologies, but they're highly complex and relatively unproven. They require top-tier talent, robust infrastructure, and long development cycles. For most startups, it's not realistic—at least not in the early stages.

Plus, even with these models, you still need to convince clients that their data remains safe. That means transparency, certifications, and robust documentation.

5. Secure the deal, build trust, sell the cloud

Startups need to prioritise simplicity. If on-premise gets your foot in the door, it might be worth it. So, what’s your strategy here? Our advice

• Short-term: go on-premise to win the deal.
• Long-term: build up trust until you can sell a cloud solution.

Start with the solutions your customers are asking for. Show you understand their security concerns. Deliver a secure, stable product they can trust. In other words, deliver a good on-premise LLM solution.

But don’t stop there. Build trust with your clients and use that trust as a foundation to educate them. Explain the benefits of cloud-based AI: faster updates, improved models, better integration, and lower long-term costs.

Show them that “secure and privacy-preserving” doesn’t mean “no cloud.” You can demonstrate your commitment through:

• Certifications (e.g. ISO 27001, BSI, C5, etc.)
• Penetration testing and audits
• Clear data minimization practices

Ultimately, German hospitals don’t hate the cloud—they fear the risks. If you can prove your cloud deployment is as secure or more secure than local solutions, you’re back in the game.

In summary: start small, build trust, scale out

Bringing LLMs to hospitals in Germany is a challenge—but it’s far from impossible. The key is to respect your client’s concerns, deliver reliable short-term solutions, and work toward scalable, secure long-term models.

Startups shouldn’t aim to “beat” on-premise requirements—they should aim to meet them with excellence while building the case for cloud, gradually and transparently.

Yes, deploying LLMs on-premise is hard. Yes, it’s expensive and time-consuming. But if it gets you in the door, it’s worth considering—as long as it’s not your only strategy.

Need help figuring it out? We’re here to help!

Chino.io works as your partner to help you solve all privacy, security, and compliance issues. Our unique combination of regulatory expertise, legal know-how, and technical experience helps eliminate compliance risks while saving you money and time. Book a call with our experts to learn how we can help you deliver compliant-by-design innovation.