Has anyone successfully migrated big AI workloads off AWS/Azure while staying in Europe?

2026-05-19 · Source: Machine Learning ML & Generative AI News · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Cybersecurity & Data Privacy · Depth: Advanced, quick

Summary

European AI teams are increasingly exploring alternatives to major US cloud providers like AWS and Azure due to persistent issues such as extended GPU wait times, prohibitive egress fees, and critical data residency concerns. This inquiry seeks firsthand accounts from organizations that have successfully migrated substantial AI training or inference workloads to Europe-focused cloud infrastructure. The goal is to understand the practicalities of such migrations, including unforeseen challenges ("gotchas"), and the resulting impact on operational costs, network latency, and regulatory compliance post-transition. Real-world experiences are sought to inform others considering similar strategic shifts.

Key takeaway

For CTOs and VP of Engineering overseeing AI initiatives in Europe, evaluating a migration from US-centric cloud providers to European alternatives is becoming critical. Your teams should investigate local cloud options to mitigate GPU scarcity, reduce high egress costs, and ensure data residency compliance, potentially improving both operational efficiency and regulatory posture.

Key insights

European AI teams face challenges with US cloud providers, prompting interest in local alternatives.

Principles

• Data residency is a key driver for cloud migration.
• Egress fees significantly impact cloud cost models.

In practice

• Evaluate European cloud providers for AI workloads.
• Assess GPU availability and wait times.
• Compare egress fees across cloud platforms.

Topics

• AI Workloads
• Cloud Migration
• Data Residency
• GPU Availability
• Egress Fees

Best for: CTO, VP of Engineering/Data, MLOps Engineer, AI Architect, Director of AI/ML

Related on AIssential

• It’s not just Nscale. Here are all of Europe’s neoclouds, mapped — Europe's "neoclouds" are rapidly expanding data center capacity to meet AI-driven computing demand.
• Why cloud value depends on what happens after migration — Cloud value stems from post-migration strategy, governance, and optimization, not just workload movement.
• Lambda’s multi-cloud blueprint for high-performance AI infrastructure — Multi-cloud AI infrastructure mitigates single-cloud risks, offering flexibility, cost optimization, and compliance for GPU-intensive workloads.
• AI-Assisted Migration Tool Helps Teams Move from ingress-nginx to Higress in Minutes — AI-assisted tools automate Kubernetes infrastructure migrations, transforming manual reconstruction into validation.
• AWS revenue continues to soar as cloud demand remains high — AWS achieved its highest quarterly growth in three years, driven by new client agreements and AI workloads.
• How AI Keeps Europe Hooked on US Cloud — European AI sovereignty is hindered by structural dependence on US hyperscalers controlling distribution, proprietary models, and inference infrastructure.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning ML & Generative AI News.