Switching from AWS to a European GPU Cloud: A Technical Guide
Aurelien Bloch
February 23, 2026 · Head of Research at Lyceum Technologies
Infrastructure choices for ML teams often prioritize initial convenience over long-term strategy. AWS, GCP, and Azure provide an easy starting point, especially with generous startup credits. However, as models move from experimentation to production, the technical and financial limitations of these hyperscalers become apparent. High egress fees, complex IAM configurations, and the legal ambiguity of the US Cloud Act create significant friction for European enterprises. Migrating to a sovereign European GPU cloud allows specialized orchestration to solve hardware underutilization while keeping sensitive data within the European Union.
The Hyperscaler Tax: Why AWS Becomes a Bottleneck
AWS appeals to AI teams through its vast ecosystem and seamless integration between S3 and EC2. The ecosystem is vast, and the integration between S3 and EC2 seems seamless at first glance. However, as AI teams scale, they often encounter the 'hyperscaler tax.' This tax is not just financial: it is architectural. AWS SageMaker and EC2 P4/P5 instances require significant DevOps overhead to manage effectively. Engineers spend more time configuring VPCs, IAM roles, and security groups than they do refining their model architectures. This complexity often leads to a 'set it and forget it' mentality where instances are left running longer than necessary, or oversized hardware is provisioned to avoid Out-of-Memory (OOM) errors.
The billing structure of hyperscalers is designed for general-purpose computing, not the bursty, high-intensity nature of ML training. When you factor in the cost of data transfer, the financial burden increases. Moving large datasets or model weights out of the AWS ecosystem triggers substantial egress fees, effectively making data easy to ingest but expensive to export. For European companies, this is compounded by the legal risk of the US Cloud Act, which allows US authorities to request data stored by US companies, even if that data is physically located in a European data center. Switching to a provider like Lyceum, which operates out of Berlin and Zurich, eliminates these sovereignty concerns while providing a more streamlined, AI-focused developer experience.
Data Sovereignty and the EU AI Act Compliance
Data residency is no longer a secondary concern for AI startups and enterprises in Europe. With the implementation of the EU AI Act and the ongoing requirements of GDPR, the physical and legal location of your compute resources is a critical compliance factor. When using AWS, even in the eu-central-1 (Frankfurt) region, the underlying provider is a US-based entity. This creates a potential conflict with European data protection standards, particularly regarding the transfer of personal data to third countries. For teams working on sensitive applications in healthcare, finance, or government, this legal ambiguity is a non-starter.
A sovereign European GPU cloud provides a 'GDPR by design' infrastructure. By choosing a provider with headquarters and data centers exclusively within the EU or Switzerland, teams ensure that their data remains under European jurisdiction. This simplifies the Data Protection Impact Assessment (DPIA) and provides peace of mind to stakeholders and customers. Beyond legal compliance, sovereignty also means better local support and infrastructure tailored to the European market. Instead of navigating the labyrinthine support tiers of a global giant, engineers can work with a provider that understands the specific regulatory and technical landscape of the European AI ecosystem. This localized focus allows for faster iterations and a more collaborative approach to infrastructure management.
Solving the 40 Percent GPU Utilization Problem
Average GPU cluster utilization typically hovers around 40 percent. This means that for every dollar spent on compute, 60 cents are essentially wasted on idle cycles. This waste occurs because of static provisioning: engineers reserve a specific instance type (like a p4d.24xlarge) and keep it active throughout the entire development cycle, including periods of data preparation, debugging, and idle time between experiments. AWS does not provide the granular, workload-aware orchestration needed to solve this problem out of the box.
Lyceum addresses this by introducing a workload-aware pricing model and an automated hardware selection engine. Instead of guessing which GPU is best for a specific job, the platform analyzes the workload requirements before the job runs. It predicts the memory footprint and runtime, selecting the most cost-optimized or performance-optimized hardware based on the user's constraints. For example, if a training job is not time-sensitive, it can be scheduled on hardware that offers the best price-to-performance ratio. Conversely, if a deadline is approaching, the system can prioritize the fastest available silicon. By dynamically matching workloads to resources, teams can push their utilization far beyond the 40 percent industry average, effectively getting more compute power for the same budget. This level of precision is impossible on generic cloud platforms that treat GPUs as just another virtual machine type.
