Private AI vs Cloud AI (OpenAI and similar)
What works for regulated companies when confidential data is involved and you still want to use AI productively.
Cloud AI like OpenAI or Anthropic is ready in minutes, but your data leaves the building. Private AI runs on your own infrastructure, in exchange you need architecture and operations. For regulated industries the question is rarely convenience, it is privacy and control. Here is the plain comparison.
The most common challenges
With cloud AI your data leaves the company
Prompts and documents are processed at a third party, often outside the EU. For confidential or regulated data this is frequently a deal breaker.
GDPR and data processing agreements
Cloud AI requires clean data processing agreements and a solid legal basis. With sensitive data this quickly becomes a blocker.
Private AI needs architecture, not just a model
A local LLM alone is not enough. Without retrieval, access control and operations, the value does not materialise.
The CCsolutions approach
Cloud AI is worth it for non-critical use cases, fast prototypes and public data. You are productive in minutes, but you accept processing at the provider.
Private AI is worth it as soon as confidential documents, customer data or regulated content are involved. Models like Llama or Mistral run on your infrastructure, not a single data point leaves the company.
CCsolutions runs private LLMs with Retrieval Augmented Generation over your internal documents, including access control and an audit trail. That way you use AI productively and stay GDPR and BaFin compliant.
Our advice: split by data class. Public cases may go to the cloud, anything confidential runs privately. This hybrid split gives you speed and privacy at the same time.
Technologies
Frequently asked questions
Is private AI worse than GPT?
For many business tasks like search, summarisation and questions over internal documents, open models deliver very good results, especially with good retrieval.
Is private AI more expensive?
With continuous use often not. You pay for infrastructure instead of per token, and sensitive data stays in house without legal risk.
Do I need my own GPUs?
Not necessarily. Depending on the model and load, a reasonable GPU instance in your cloud or your own data center is often enough.
Can I combine both?
Yes. A hybrid architecture uses cloud AI for non-critical cases and private AI for confidential data.
Ready to get started?
We analyse your situation for free and show what is possible in your specific case.
Free AI consultation