04/09/2025
The 6 crucial differences between public and private AI
In a short time, Artificial Intelligence (AI) has grown from an experimental technology to an indispensable component within many organizations. It's used to accelerate and streamline administrative processes, analyze large amounts of data, and improve services for citizens, patients, and customers. As AI becomes increasingly entrenched in our core processes, one question we're seeing more and more often is: how do you deploy AI without losing control of your data, compliance, and IT environment?
Organizations often start with the more well-known public AI services, such as ChatGPT, Microsoft Azure OpenAI, or Google Gemini. These solutions are powerful and easy to use, but they often lack compliance with privacy, regulations, and cost control. Private AI offers an alternative. Private AI gives you the same AI functionality, but with complete control, integrated into your existing IT environment, and compliant with the latest laws and regulations.
In this article, we delve into the six key differences between public and private AI that will determine your strategic choices.
1. Data ownership and privacy
Public AI: Data is often processed in data centers outside the Netherlands or even outside the European Union. Even if vendors promise not to use data for model training, the risk remains that sensitive information will be stored or processed in jurisdictions with different laws and regulations, such as the US Cloud Act.
Private AI: All data remains physically and legally under your control. With solutions such as Fuse AI Data is processed exclusively in Dutch data centers, completely separate from other customers. This means that patient and personal data, or confidential company information, can never escape your own secure environment.
Why this is important: In sectors such as healthcare and government, the loss of data sovereignty can lead to reputational damage, legal problems and a loss of trust.
2. Legislation and regulations
Public AI: Public AI services often fundamentally comply with the GDPR requirements, but they may still conflict with sector-specific requirements. Moreover, data processed outside the EU is subject to foreign legislation, which hinders compliance.
Private AI: Private AI is immediately set up to comply with Dutch and European privacy regulations and additional guidelines such as NEN7510 (healthcare) or BIO (government). Combined with a sovereign cloud, it's easier to demonstrate compliance with the NIS2 guideline, which imposes stricter requirements on security and risk management.
Why this is important: The EU AI Act and NIS2 make compliance mandatory, not optional. If you're still working in public environments without taking control, you'll soon be left behind.
3. Model selection and customization
Public AI: You're essentially always limited to the models the vendor uses and offers. Customization is nearly impossible, and switching to a different model or infrastructure is hampered by vendor lock-in.
Private AI: You can often choose which Large Language Model (LLM) you use, from open-source models like Llama 2 and Mistral to your own trained models. Furthermore, everything can be tailored to your specific use cases, and it's usually possible to use your own sources and data to supplement the LLM. This allows the solution to better align with specific processes, data, and quality standards.
Why this is important: For example, a municipality can train a model on local policy documents to answer citizen questions more efficiently and quickly, while a healthcare institution can tailor AI to medical terminology for correct reporting.
4. Cost structure and predictability
Public AI: Often based on variable costs per API call or token. This can hinder cost predictability, especially as usage of the solution grows.
Private AI: You work with fixed, pre-agreed costs, without usage peaks causing your bill to skyrocket. Scaling can be done in a controlled manner, with insight into budget and capacity.
Why this is important: In public models, a successful internal rollout can unexpectedly turn out to be more expensive than planned, which in turn hinders innovation.
5. Integration and chain control
Public AI: You often rely on external APIs and the vendor's roadmap when integrating the solution with existing systems. Furthermore, you have little control over updates, changes, or deprecations.
Private AI: The solution integrates with your own infrastructure, connecting to existing databases, DMS environments, and processes. You can control how AI is deployed and optimize processes without external dependencies.
Why this is important: AI will no longer be a standalone experiment, but a fixed and integrated part of the core processes of your organization.
6. Transparency and source references
Public AI: The 'black box' nature of many public AI models makes it difficult to determine which sources were used – and therefore difficult to determine whether the generated output is reliable.
Private AI: Provides answers with clear source citation. This way, you know exactly what data a conclusion is based on. This is essential for substantiation and accountability.
Why this is important: In sectors that place high demands on accountability, this is the difference between a 'nice idea' and a practical solution.
The strategic choice
The question is no longer whether AI is relevant for organizations, but how it can be deployed securely, compliantly, and future-proof. Public AI is ideal for experimenting with accessible applications, but organizations that want to integrate AI into their core processes cannot ignore private AI.
With a solution like Fuse AI, you benefit from all the advantages of generative AI, without compromising data control, compliance, and cost management.
Curious about what Fuse AI looks like in practice?
In our Fuse AI inspiration guide, you can read how the solution works and find concrete use cases for each sector. Discover how you can innovate safely and compliantly with the power of AI.
