Introduction
The discussion around local AI is often framed as a counterpoint to global tech giants, but the reality is far more complex. Major providers like OpenAI, Google, and Microsoft are already firmly established in the market, making a complete withdrawal neither feasible nor sensible. Instead, a hybrid approach is needed: global cloud services are used where they offer economies of scale, while particularly sensitive or latency-critical applications are operated locally.
The Reality of Global AI Monopolies
Tech giants today possess immense infrastructure and vast customer networks. Their data centers and information pools are already so extensive that a full shift to local systems is unrealistic for most organizations. At the same time, national strategies such as “Sovereign AI” acknowledge this market dominance and aim to retain core AI components under state or local control—without entirely shutting out global services.
The Hybrid Path: Leveraging Global and Local Strengths
Global services remain essential: Cloud providers offer highly scalable models that deliver top performance, especially in generative AI. These models require enormous computing resources that are generally unavailable locally. OPEX-based cloud models allow companies to access AI capabilities without major upfront investments and to scale flexibly as needed.
Key local application areas: In sectors handling large or sensitive volumes of data—such as healthcare, government, or industry—compliance requirements can often only be reliably met through local processing. In scenarios demanding extremely low latency and high system availability, edge AI is advantageous: it reduces delays and ensures resilience against cloud outages. Moreover, full control over particularly sensitive data remains in-house, simplifying governance and data protection.
Benefits of Hybrid AI Infrastructure
A hybrid setup enables targeted resource allocation: the cloud handles high-volume, standardized tasks, while local systems take on critical and highly specialized processes. This allows sensitive data to stay on-premises, while non-critical workloads run cost-efficiently in the cloud. In the long term, investments in dedicated hardware pay off for large data volumes, while in the short term, the cloud provides a flexible OPEX option. The ability to activate or scale down local and global capacities on demand also increases resilience and helps avoid single points of failure.
Challenges & Pragmatic Solutions
Market dependence on global providers can be mitigated through cooperative hybrid models that complement rather than replace cloud services. High upfront costs for local infrastructure can be offset through funding programs and strategic budget planning, with the cloud serving as a buffer. To tackle growing complexity and lack of expertise, partnerships with technology providers, use of open-source LLMs, and comprehensive internal training are advisable. Security and maintenance processes can be ensured locally and in hybrid environments through established DevOps pipelines, regular updates, and continuous security monitoring. National and European regulatory initiatives—from the principle of “Sovereign AI” to the EU AI Act—provide the legal framework for trustworthy AI solutions.
Acting with Foresight – Shaping a Hybrid AI Future
The creation of Centers of Excellence (COEs) for local AI provides the organizational structure to maintain models and establish seamless interfaces with global APIs. Routine and mass-scale tasks can be flexibly handled in the cloud, while locally operated AI systems manage sensitive or latency-critical areas. Early investment in local infrastructure lays a stable foundation for future demands—be it increasing data volumes, evolving regulatory requirements, or new, innovative AI applications.
Our conclusion
A complete withdrawal from global providers is neither realistic nor necessary. However, in areas such as data protection, real-time processing, and national AI sovereignty, local systems are indispensable. The hybrid approach combines efficiency, security, and innovation, empowering organizations to actively shape their AI future rather than later struggling with dependencies and rigid architectures.