With cloud AI you use models via an API from an external provider. Think of OpenAI, Anthropic, Google, or Azure OpenAI. You send data to their servers, the model processes that data, and sends a response back. You need no hardware of your own, and you benefit from the best and most up-to-date models.

Providers typically offer data processing agreements (DPAs) and give guarantees about how they handle your data. Many also offer the option to not use your data for model training.

On-premise means the AI model runs on infrastructure you manage yourself: servers in your own data centre or on a private cloud you control. You need open-source models (such as Llama, Mistral, or Qwen) or self-hosted solutions.

Data never leaves your own network. That is the core of the privacy promise of on-premise AI.

For many organisations cloud AI is fine, provided a few conditions are met:

• You have signed a data processing agreement with the provider
• The data you process is not particularly sensitive (no medical records, no national ID numbers)
• Your provider offers guarantees about data centre location (EU hosting)
• You can minimise data: do not send more than necessary to the API

Sectors like marketing, retail, and e-commerce typically use cloud AI without major concerns, as long as they do not put personal data in prompts.

There are situations where on-premise is preferred or even required:

• Healthcare: Medical data falls under strict privacy legislation. Cloud use is possible but requires additional safeguards and consent.
• Legal services: Confidential client information may in many cases not go to external servers.
• Government institutions: Some government organisations may not bring data outside their own network.
• Financial sector: Depending on the type of data and jurisdiction, there are restrictions.
• Trade secrets: If your prompts contain core business data you do not want to share, consider on-premise.

On-premise AI is not free. You need hardware: GPU servers for larger models are expensive to purchase and maintain. Beyond hardware you need DevOps capacity to manage and update the infrastructure.

Open-source models are now quite capable in terms of quality, but they do not always match the best cloud models. A Llama or Mistral model is excellent for many tasks, but for the most complex reasoning or writing tasks, closed-source models often still perform better.

The most pragmatic route for many organisations is a hybrid approach:

• Non-sensitive data is processed via cloud APIs for maximum quality and simplicity
• Sensitive data is processed on-premise or in a private cloud
• Summarise or anonymise data before sending it to the cloud

This gives you the benefits of cloud AI where possible, with the security of on-premise where necessary.

European privacy legislation (GDPR) sets requirements for processing personal data, including when that happens via AI prompts. Ensure you have a legal basis for processing, a data processing agreement with your AI provider, and that you inform users when processing their data.

The choice between cloud and on-premise is just one aspect of GDPR compliance in AI.

The choice between cloud AI and on-premise depends on your sector, the sensitivity of your data, and your budget. At Mach8 we help clients make the right trade-offs and implement AI in a way that fits their privacy requirements and risk profile.

Want advice on the right AI architecture for your organisation? Contact Mach8 or view our AI agents service.