For years, hyperscalers such as AWS, Microsoft Azure, and Google Cloud have dominated the market by providing a comprehensive ecosystem tailored to the needs of businesses of all sizes. These platforms deliver agility and global access, attracting enterprises with promises of simplified infrastructure, flexibility, and efficiency. However, time has exposed significant flaws in this approach, especially cost transparency, system control, and operational independence. Now, as enterprises aim to expand their artificial intelligence systems and regain control of their infrastructure, sovereign clouds are rapidly transforming the landscape.
A key factor driving this change is cost. Although public cloud services initially appeared to be cost-effective, companies are increasingly faced with hidden expenses. Growing workloads, higher data egress fees, and the intense computational demands of training and deploying AI models are making hyperscaler infrastructure very expensive. AI systems are especially known for their resource-heavy nature, requiring specialized hardware such as GPUs, powerful computing resources, and large storage capacity to operate efficiently.
While hyperscalers provide AI-focused services, many organizations are shifting toward sovereign cloud solutions because they offer customizable models with more transparent pricing. Sovereign cloud providers are better positioned to tailor their platforms to meet specific enterprise AI needs, often at lower costs. By migrating AI workloads to sovereign clouds, companies gain the ability to scale freely without facing high vendor lock-in fees or unclear billing practices that can drain budgets.
