For years, hyperscaler has dominated as AWS, Microsoft Azure and Google Cloud by providing an ecosystem understanding adapted to the needs of business of all sizes. These platforms provide agility and global approach and attract companies promised from simplified infrastructure, flexibility and efficiency. However, time in this consent of the exhibition, which is to mean shortcomings, in particular the transparency of costs, control of the system and operating independence. Now, because businesses aim to expand their artificial intelligence systems and regain control over their infrastructure, sovereign clouds quickly transform the landscape.
The key factor that controls this change is the cost. Although the public cloud initially seemed to be cost -effective, companies are increasingly facing hidden expenditures. Increasing workload, higher data escape fees and intensive computing requirements for training and deployment of AI models make the hyperscaler infrastructure very expensive. AI systems are particularly known in their nature of heavy resources that require specialized hardware, such as GPUs, powerful computing sources and large storage capacity for efficient operation.
While hyperscalers provide AI services, many organizations are moving towards sovereign cloud solutions because they offer customizable models with more transparent prices. Cloud cloud providers are better placed to adapt their platforms to meet the specific needs of the company AI, often at lower costs. Migration of AI working load on sovereign clouds acquires the ability of free scale without facing high fees for locking the supplier or unclear billing procedures that can drain budgets.