As AI develops and digital privacy becomes a bigger concern in the EU, companies need to think carefully about how much control they actually have over their systems, especially with the political debates on sovereignty.
To strengthen Europe’s technological sovereignty, the European Union plans to carry out an extensive digital transformation: it intends to triple the capacity of its data centres and build a sustainable artificial intelligence infrastructure. However, until now, most of the European public cloud computing market has been controlled by leading American companies.
The US CLOUD Act made the discussion about local AI capacities in Europe even more urgent. The act clarifies the legal use of data outside the US and its implications for global data protection. This shift raised new questions about how safe it is for European companies to rely on American cloud providers.
The goal of the EU digital transformation is to reduce the dependence on foreign computing power and still build a strong digital ecosystem. What are the European Union’s current capabilities and what are its real prospects for implementing this idea? Let’s take a closer look.
EU Ambitions VS Slow Timelines
The EU’s plans are ambitious and necessary, but the main problem is timing.
Gradual process of digitalisation and priorities data protection we experience in the member states collide with the experience and technical capacities of the US companies. And they are very much aware of this, hence the US CLOUD Act had such an influence on the political talk between two regions.
Even though the 2025 AI Continent Action Plan talks about investing 20 billion euros in AI infrastructure to make Europe a global digital leader, getting permits and environmental approvals for data centres can take over four years. Public and private partnerships are also being considered to accelerate these projects.
First, in four years the world may already look very different, shaped much more strongly by artificial intelligence. Second, even though the inflow of AI specialists and the number of startups in the area of generative technologies is growing, they often lack resources. As ambitious as this plan is, it does not guarantee that Europe will have enough local cloud capacity ready in time.
European alternatives and current limitations
European companies already had to follow GDPR rules before the US CLOUD Act or the rise of AI. But the situation became more serious once two different legal systems started to overlap.
The US CLOUD Act is still very much against the GDPR requirements. This means that all the US companies based in the EU will have to rethink their business processes because even choosing a European data center now doesn’t mean GDPR compliance.
Companies like IONOS, Scaleway, and OVHcloud promise GDPR compliance and data sovereignty. German providers like SecureCloud and luckycloud specifically market their sovereignty advantages. But the reality is that they can’t yet compete with American providers. It also concerns technical capabilities and Germany is technologically dependent in many areas. That’s also the reason that most European providers focus on small and medium businesses – because they can’t handle the complex requirements of large enterprises.
German companies are interested in AI deployment and on-premises solutions, especially after all the concerns about US data access laws. But the reality is that building AI infrastructure inside the company is extremely difficult. Most companies restructured their IT management teams over the past decade, now all of them also have the staff with the knowledge to manage local cloud deployment.
Furthermore, AI hardware needs regular updates as technology advances. Maintaining competitive performance means constant investment in storage systems and networking infrastructure. These costs accumulate very fast over time. For small businesses, this is a tough call. And in reality, European providers still cannot match the AI service portfolios of American companies with years of development advantage.
Complexity of migration and vendor dependency
The main problems with transferring workloads to local clouds are the scale of this migration and the technical complexity involved. For more than 10 years, American companies have been offering services that go far beyond simply executing code in virtual machines.
In addition, many European companies are themselves or continue to be the US partners, so even if their customer does not work with clouds directly, they may be indirectly connected to them. This applies not only to the European Union but also to the global market.
German companies need AI strategies that balance sovereignty concerns with practical operational needs. This usually means to first evaluate specific AI use cases to determine optimal deployment locations. However, the process of extracting data from the cloud to local centers can be very costly.
Companies handling sensitive personal data or operating in regulated industries probably benefit from local deployment for critical AI applications, however they should consider total cost of ownership over three to five years rather than just initial deployment expenses. Local AI infrastructure often requires large upfront investment but might provide cost advantages for consistent, high-volume workloads.
The Path Forward: Choosing Balance Over Ideals
A realistic view is that most German companies will not achieve full independence from US cloud providers in the near future. The technology gap, infrastructure requirements, expertise demands create barriers that German companies can now hardly overcome alone.
European organizations wanting to exit the sphere of influence of American cloud operators need to consider massive investment and the total workflow restructuring.
Most successful companies are likely to follow a strategy of diversification, meaning to reduce US single-vendor dependence while maintaining access to advanced AI capabilities. This might cause distributing workloads across multiple providers, including European alternatives for less critical applications. And it sounds just as complicated as it’s in practice.
However, the companies that address these challenges through balanced deployment strategies and workforce development will probably achieve better outcomes than those pursuing idealistic sovereignty goals without considering practical implementation requirements.
