How European businesses can cut costs and avoid hyperscaler lock-in
Artificial intelligence is becoming a core enabler of business transformation, from automating workflows to powering data-driven decisions. But as U.S. hyperscalers like Microsoft, Google, and Amazon integrate AI tightly into their cloud ecosystems, European businesses face two pressing risks: lock-in and rising costs.
How can organizations build an AI-enabled ecosystem that is cost-efficient, sovereign, and flexible? Here are five strategic recommendations to guide the shift.
1. Move Toward Hybrid Architectures
Most enterprises today run the majority of their AI workloads on hyperscalers. While this offers speed and convenience, it also limits flexibility. A hybrid approach where combining hyperscaler services for elastic workloads with on-prem or EU cloud deployments for predictable inference tasks is emerging as a more resilient model.
Providers such as OVHcloud (France), Scaleway (France), and Hetzner (Germany) are positioning themselves as EU alternatives. Beyond cost control, this shift also reduces data egress fees, a hidden but significant cost of hyperscalers.
2. Prioritize High-Impact Use Cases
Customer-facing chatbots get most of the headlines, but backend and operational processes are often where the biggest cost savings lie. Organizations should begin with high-volume, repetitive tasks such as:
Forecasting and planning
Document classification and enrichment
Knowledge management and search
Pricing and optimization tasks
These areas produce rapid ROI and free resources for more experimental, strategic AI applications.
3. Balance Small and Large Language Models
Not every task requires a frontier model like GPT-4. A balanced strategy blends Small Language Models (SLMs) and Large Language Models (LLMs):
SLMs (e.g., Mistral 7B, Velvet 2B) excel at domain-specific, high-volume inference at low cost.
LLMs (e.g., OpenAI, Anthropic) should be reserved for complex, broad reasoning tasks where quality outweighs cost.
This dual approach ensures efficiency without sacrificing advanced capability where it matters.
4. Optimize Inference — Where the Real Costs Are
While training large models is expensive, in practice inference — running the model in production — is where costs accumulate. Optimizations such as FastFormers (which combine distillation, pruning, and quantization) can reduce inference costs significantly. For organizations with high transaction volumes, this is transformative.
5. Choose Partners That Understand EU Sovereignty
Given limited in-house AI capacity in many companies, consulting and integration partners are essential. But choosing the right partner is critical:
Look for expertise in hybrid cloud + on-prem deployments
Demand experience with open-source model integration
Prioritize firms familiar with GDPR and the EU AI Act
Require transparent benchmarking of TCO, performance, and accuracy across deployment options
Without this, organizations risk replicating hyperscaler lock-in under another name.
The European AI Ecosystem Is Growing
Europe now has a growing ecosystem of competitive AI models and platforms:
Mistral (France): open-weight LLMs and reasoning models
Aleph Alpha (Germany): enterprise-grade LLMs with transparency and explainability
Velvet AI (Italy): efficient, multilingual models trained on European supercomputers
EuroLLM (EU-wide research): multilingual models covering all 24 EU official languages
Salamandra (Spain): multilingual LLMs across 35 European languages and code
These initiatives provide credible alternatives for businesses seeking sovereignty, flexibility, and cost control.
AI is no longer optional, but how it is deployed will determine both competitiveness and cost structure. By rebalancing toward hybrid deployments, targeting high-volume processes first, and leveraging Europe’s emerging AI ecosystem, organizations can build a cost-effective, sovereign AI backbone.
The winners will be those who understand that while training gets the attention, inference pays the bills — and act accordingly.
