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What does “on-premises vs. cloud” mean in the context of AI?
On-premise AI means that AI models run on your own infrastructure—in your own data center or on dedicated hardware. You have full control over data, models, and access—but you also bear full responsibility for operations and scaling.
Cloud AI means that models run on infrastructure provided by external vendors (Azure, AWS, GCP, OpenAI, Anthropic). You use ready-made services via API without having to operate your own hardware. The vendor handles scaling, maintenance, and model updates.
Hybrid models: Private cloud (dedicated cloud instances with hyperscalers—e.g., Azure OpenAI with an EU region), hybrid approach (sensitive data on-premises, non-critical data in the cloud), sovereign cloud (European providers with full data sovereignty).
For small and medium-sized businesses, the choice is not an ideological one, but a business decision. The cloud is usually the fastest and most cost-effective way to get started. On-premises solutions make sense when data protection, compliance, or cost considerations necessitate them—typically in cases involving high volumes and sensitive data.
Best Practices for Voting
These six principles will help you make a decision:
- Start with the use case: Don’t be ideological—choose cloud or on-premises based on your requirements.
- Data Protection First: For sensitive data, use a European cloud or on-premises solution as the standard.
- Hybrid is often the answer: It’s not black and white—hybrid models are the norm.
- Calculate the break-even point: At what volume does on-premise become cost-effective? Do the math clearly.
- Check cloud regions: Many providers offer an EU region—take advantage of it.
- Build in portability: Containers and open models allow for switching—avoid vendor lock-in.
Common Mistakes When Voting
We often see these pitfalls:
- Data protection reviewed too late: First build the cloud, then complain about data protection—an expensive rebuild.
- Cost calculations without MLOps: On-premises solutions look cheap, but operating costs are underestimated.
- Vendor lock-in: Too much reliance on a single cloud provider—making it difficult to switch.
- Everything on-premises on principle: The cloud would be cheaper and faster—but ideology stands in the way.
- Misunderstanding compliance regulations: The cloud is often possible in a GDPR-compliant way—yet it’s rejected across the board.
On-Premise vs. Private Cloud vs. Public Cloud
A comparison of three operating models:
- On-premises: Own hardware, own data center. Full control, full responsibility.
- Private Cloud: Dedicated instances at a hyperscaler. A compromise between control and convenience.
- Public Cloud: Shared infrastructure with the provider. Fastest way to get started, least control.
The Right AI Deployment Strategy with prodot
In a free initial consultation, we’ll analyze your AI projects and recommend the right operating model—cloud, on-premises, or hybrid.
As an AI partner for small and medium-sized businesses, we take a holistic approach to operations, compliance, and cost-effectiveness—and build the right infrastructure.
What We Offer
- AI Consulting — Operational Strategy and Implementation.
- GDPR and AI in the Glossary — Data Protection Fundamentals.
- AI Security in the Glossary — Security in Both Models.
- Azure OpenAI in the Glossary — The EU Cloud Option.
