The Hidden Costs of Legacy Cloud Providers

The Financial Drain of Data Movement

Legacy cloud providers rely on complex billing structures that penalize data movement and experimentation. Start with the published list price. AWS quotes the p5.48xlarge, an eight-way NVIDIA H100 instance, at $55.04 per On-Demand hour in US East (N. Virginia), which works out at $6.88 per GPU-hour, on its EC2 On-Demand pricing page read on August 3, 2026. That is the compute line alone, before the charges layered on top of it. Egress fees charge significant amounts for data leaving the platform. If you export a 5 GB trained model daily for multi-cloud deployment or local testing, those transfer costs compound rapidly. Moving a petabyte of data can cost tens of thousands of dollars in egress charges alone. This creates an artificial barrier to multi-cloud architectures, forcing engineering teams to keep all their data locked within a single vendor ecosystem just to avoid exorbitant penalties.

Solving the GPU Utilization Problem

Beyond data transfer, utilization rates present a massive efficiency problem for machine learning teams. A provisioned GPU is billed whether or not it is computing. You pay for idle compute time while waiting for data loading, checkpointing, or CPU-bound preprocessing steps. When renting an expensive H100 instance by the hour, paying for idle time destroys your compute budget. The traditional model forces you to over-provision to handle peak loads, leaving expensive hardware sitting idle during off-peak hours.

Sovereign platforms solve this structural inefficiency through transparent pricing and intelligent scheduling. We bill per second across the board, with no subscription and no base fee. Our S3-compatible storage includes no egress fees, allowing you to move datasets and model weights freely without worrying about budget overruns. Furthermore, Lyceum's scheduling product predicts memory requirements and estimates runtime within a node, automatically selecting the right GPU for your workload. That cuts the GPU time you pay for but do not use, so you pay for the compute cycles your machine learning models actually consume. By migrating to a platform designed specifically for AI workloads, teams can reclaim their budgets and invest back into model development.

The GPU Availability Crisis

The Reality of Hardware Scarcity

Securing high-end compute has become a logistical nightmare for machine learning teams. High-end data center GPUs such as the NVIDIA H100 are allocated ahead of demand rather than sold from stock, so delivery dates move with the order book. Major US cloud providers have locked up multi-year supply contracts, meaning their available capacity is reserved almost entirely for massive enterprise clients willing to sign massive block reservations. For startups and mid-sized companies, this means being pushed to the back of the line, forced to wait months just to secure the hardware necessary to train a new foundation model or scale an inference pipeline.

The Failure of Legacy Auto-Scaling

Auto-scaling on legacy clouds often fails in practice when dealing with specialized hardware. You request specific machines, wait 20 minutes, and receive an error that no capacity is available in your designated region. This forces teams to over-provision dedicated instances, paying for 24/7 uptime to serve bursty inference traffic because they cannot trust the cloud provider to spin up new instances when traffic spikes. The compute crunch is reshaping infrastructure strategies, pushing teams to seek alternative providers who can actually guarantee availability.

Reliable Provisioning with Sovereign Infrastructure

Our platform provides a different path because Lyceum runs workloads in European data centers in Spain, Paris and the Nordics, with GPU compute billed per second and no base fee. Single virtual machines start on demand, so a short experiment does not sit in a queue. Larger clusters are planned with the account team instead: the availability tier is agreed during the proof of concept, capacity is added or removed on two to three weeks notice, and new machines take around four weeks of lead time. For inference workloads, our platform supports scale-to-zero functionality. The machine shuts down when idle, meaning you only pay when actively serving traffic. This combination of instant availability and scale-to-zero economics ensures that your machine learning workloads remain both performant and cost-effective, regardless of broader market constraints.

The Compliance Moat: GDPR and the EU AI Act

The Shift Toward European Sovereignty

Regulatory enforcement is fundamentally reshaping the cloud computing landscape. ASEE reports that EU cloud sovereignty has shifted from a mere policy aspiration to an urgent operational priority, driven by cumulative GDPR fines reaching 7.1 billion euros. The core issue lies in the deep conflict between strict European privacy laws and expansive US surveillance mandates. As artificial intelligence models process increasingly sensitive personal data, the legal risks associated with non-compliant infrastructure have become too large for modern enterprises to ignore.

The US CLOUD Act vs. GDPR

The US CLOUD Act compels American companies to produce data upon US government demand, regardless of where that data physically resides. This creates a direct collision with GDPR Article 48, which restricts the transfer of personal data to foreign jurisdictions without specific legal safeguards. For European enterprises handling sensitive data, such as medical image segmentation in healthcare or factory anomaly detection in manufacturing, hosting models on US-owned infrastructure is a deal-breaker. Relying on legacy cloud providers exposes these companies to massive fines and severe reputational damage.

Future-Proofing with Sovereign Infrastructure

Sovereign providers run their hardware in European data centers. For Lyceum that means GDPR-compliant processing in European data centers, no training on customer data, and inference prompts and outputs that are not retained after processing. Lyceum holds no ISO 27001 certificate, no SOC 2 report and no BSI C5 attestation today, and states no EU AI Act conformity position; the data center operators hold ISO certifications at facility level. We run workloads in European data centers in Spain, Paris and the Nordics, which keeps your workloads and training datasets in Europe. European regulation is rapidly becoming a competitive advantage for companies that adapt early. Building your machine learning pipelines on sovereign infrastructure future-proofs your AI products and eases selling into highly regulated industries like finance, healthcare, and government, though under GDPR Article 24 compliance responsibility remains with you as the controller and cannot be discharged by your choice of infrastructure supplier.

Open-Stack Transparency vs. Proprietary Lock-in

The Danger of Proprietary Inference Engines

Many US-based inference platforms rely heavily on black-box proprietary engines to serve machine learning models. While they often offer fast token generation and seemingly simple deployment processes, they intentionally lock you into their specific ecosystem. You cannot inspect the underlying orchestration, you cannot optimize the memory management for your specific use case, and migrating away requires significant engineering effort. This vendor lock-in restricts your ability to negotiate pricing or move to more performant hardware as your application scales.

Embracing Open-Stack Transparency

Open-stack transparency is a fundamental requirement for engineering teams. Modern platforms utilize industry-standard open-source tools like vLLM, NVIDIA Dynamo, and TensorRT-LLM to deliver high-performance inference without enforcing vendor lock-in. Customer portability is built into the platform by design. By relying on open standards, we ensure that the optimizations you build on our platform can be understood, audited, and maintained by any competent machine learning engineer. You maintain full visibility into how your models are served and how your compute resources are allocated.

Smooth Integration and Portability

Our dedicated inference engine acts as a smooth drop-in replacement for legacy APIs. You receive a dedicated URL endpoint and can continue to use the standard OpenAI SDK that your developers already know. You change the base URL in your configuration files, and your application runs with zero code changes. You maintain full control over your models, whether you choose to deploy directly from Hugging Face or use a custom Docker image tailored to your specific environment. This frictionless migration path allows you to test our sovereign infrastructure in minutes, proving the performance and cost benefits without committing to a massive refactoring project. The hardware side of that decision is covered in our guide to moving from AWS to dedicated GPUs.

A Practical Migration Framework for ML Teams

A Phased Approach to Cloud Migration

Migrating off legacy cloud providers requires a strategic, phased approach to minimize downtime and ensure continuous delivery. Hopsworks published its own move from AWS to a European cloud on March 11, 2025 and reported a 62 percent cut in its monthly hosting bill. That is a vendor reporting on itself rather than an independent benchmark, so read it as an existence proof and not a forecast: what you save depends on your workload mix and on systematic execution. Follow this proven framework for transitioning your machine learning workloads to Lyceum without disrupting your current operations.

Step 1: Validating CI and Testing Workloads

Start by moving short-lived testing workloads and continuous integration pipelines. Provision an NVIDIA H100 virtual machine via SSH for a brief 30-minute session to validate your model architecture and ensure your dependencies resolve correctly. On-demand provisioning accelerates the experimentation loop, allowing your engineers to test code changes without waiting in long queues for legacy cloud instances to spin up.

Step 2: Shifting Heavy Training and Fine-Tuning

Once testing is validated, shift your heavy training runs to our serverless execution environment. Submit a Python script or a custom Docker container, and we handle the underlying infrastructure provisioning. You avoid the high legacy cloud rates and benefit from highly competitive H100 pricing. Because we charge zero egress fees, you can pull massive training datasets from your existing S3 buckets without incurring the massive transfer penalties typically associated with multi-cloud data movement.

Step 3: Deploying Production Inference

Finally, deploy your trained models to our dedicated inference endpoints. Configure minimum and maximum replicas for robust auto-scaling, and enable scale-to-zero functionality to eliminate overnight idle costs when user traffic drops. Serverless inference with per-token billing is already available as Lyceum Inference Studio, an OpenAI-compatible API over open-source models. By migrating systematically through these three phases, you significantly reduce infrastructure costs while securing provable data residency for your European customers.

Overcoming Data Gravity in Machine Learning

The Concept of Data Gravity

In the context of machine learning, data gravity refers to the tendency of massive datasets to attract applications, compute resources, and services to their location. As your training datasets grow into the terabyte or petabyte range, moving them becomes increasingly difficult and expensive. Legacy cloud providers weaponize this concept. By offering cheap ingress but exorbitant egress fees, they ensure that once your data is on their platform, it becomes financially ruinous to move it anywhere else. This strategy forces machine learning teams to rent expensive compute instances from the same provider, regardless of whether better or cheaper hardware exists elsewhere.

Breaking the Vendor Lock-in

The Hopsworks migration write-up cited below makes the same point: moving off AWS means breaking this data gravity first. The key is decoupling your storage layer from your compute layer. When you are no longer penalized for moving data, you regain the freedom to route your workloads to the most efficient hardware available. Sovereign providers facilitate this decoupling by offering S3-compatible storage with absolutely zero egress fees. You can store your massive datasets on our sovereign infrastructure and pull them into training instances without worrying about hidden transfer costs.

Enabling True Multi-Cloud Architectures

Eliminating egress fees does more than just lower your monthly bill; it enables true multi-cloud architectures. You can train a model on Lyceum using our highly available NVIDIA H100 clusters, export the model weights for local testing, and deploy the final inference endpoint wherever it makes the most sense for your end users. This architectural freedom ensures that your infrastructure strategy is driven by performance and compliance requirements rather than artificial financial barriers erected by legacy cloud providers. By overcoming data gravity, European AI startups can finally take control of their infrastructure costs and build more resilient, flexible machine learning pipelines.

The Impact of the EU AI Act on Model Deployment

Understanding the New Regulatory Landscape

The introduction of the EU AI Act marks a critical turning point for machine learning teams operating within Europe. This comprehensive regulatory framework categorizes artificial intelligence systems by risk, imposing strict transparency, documentation, and data governance requirements on high-risk applications. As highlighted by ASEE, the push for EU cloud sovereignty is deeply intertwined with these new regulations. Companies must now prove exactly where their models are hosted, who has access to the underlying hardware, and how the training data is managed and protected.

Data Governance and Infrastructure Choices

Compliance with the EU AI Act requires a level of infrastructure transparency that legacy cloud providers struggle to offer. When you deploy a model on US-owned infrastructure, you introduce complex jurisdictional risks. The US CLOUD Act allows foreign government access to data, which directly undermines the strict data governance mandates required by European law. If an AI startup cannot guarantee the sovereignty of its hosting environment, it risks severe penalties, including fines that can cripple a growing business. Migrating to a sovereign provider is one way to align your deployment environment with these legal requirements, but under GDPR Article 24 compliance responsibility remains with you as the controller and cannot be discharged by the choice of infrastructure supplier.

Building Trust with Enterprise Customers

Beyond avoiding fines, compliance is rapidly becoming a core requirement for enterprise sales. European banks, healthcare providers, and government agencies will not procure AI software that exposes them to regulatory liability. By hosting workloads on our infrastructure you do not inherit a compliance posture: under GDPR Article 24 the controller must implement and demonstrate compliance itself, and an infrastructure provider cannot confer it. You can tell your enterprise clients which European data centers process their data, and that Lyceum does not train on customer data and does not retain inference prompts and outputs after processing. This sovereign infrastructure advantage transforms compliance from a legal burden into a strategic advantage, allowing you to close enterprise deals faster and build deep trust with privacy-conscious customers across the European market.

The Reality of the GPU Shortage

The explosion of generative artificial intelligence has triggered an unprecedented demand for specialized hardware. The AI compute crunch is reshaping infrastructure strategies across the industry. Because supply of high-end data center GPUs is allocated ahead of demand rather than sold from stock, hardware scarcity is no longer a temporary bottleneck; it is a standing operational constraint. Legacy cloud providers have responded to this crisis by prioritizing their largest enterprise customers, forcing smaller startups to sign massive, multi-year block reservations just to secure a fraction of the compute they need.

The Cost of Inflexible Commitments

Forcing machine learning teams into multi-year block reservations stifles innovation. Startups need the agility to scale up during intensive training runs and scale down during periods of optimization and testing. Locking into a rigid contract means paying for expensive hardware even when it sits idle. Furthermore, the rapid pace of hardware advancement means that a three-year commitment to current-generation GPUs might leave you stuck with outdated technology long before the contract expires. Teams need a more flexible approach to navigate the compute crunch without destroying their runway.

Agile Provisioning as a Strategic Advantage

Sovereign providers offer a strategic alternative to rigid legacy contracts. We run workloads in European data centers in Spain, Paris and the Nordics, and the catalogue covers the B300, B200, H200, H100, A100 and L40S. We do not require multi-year block reservations; reserved capacity starts at a minimum of one month on one server. Our per-second billing model and on-demand virtual machines allow you to access top-tier compute exactly when you need it, and release it the moment your job finishes. This agile approach to infrastructure allows European AI teams to navigate the global compute crunch effectively, ensuring they always have the hardware required to train and deploy competitive models without sacrificing financial flexibility.

Sources

[1] Amazon EC2 On-Demand Pricing, read August 3, 2026; [2] Jim Dowling and colleagues, Migrating from AWS to a European Cloud: How We Cut Costs by 62%, Hopsworks, March 11, 2025, read August 3, 2026; [3] EU Cloud Sovereignty: Why Businesses Are Moving Away from US Providers