AI Infrastructure Costs in Europe: AWS vs Azure vs OVHcloud vs Hetzner 2026

AWS charges $32.77 per hour for GPU instances. Hetzner delivers comparable performance for $12.29 per hour. That's a 62% cost reduction before you optimize anything else.

For European companies running AI workloads, this pricing gap isn't a minor optimization opportunity—it's a fundamental restructuring of infrastructure economics. Yet most operators default to AWS or Azure without running the actual numbers on European alternatives.

This analysis breaks down the real costs of running AI infrastructure across four providers: AWS, Azure, OVHcloud, and Hetzner. We're using proprietary pricing data collected June 2026, real-world case studies from companies that made the switch, and detailed comparisons that account for the recent price increases European providers implemented this year.

The Growing Importance of AI Infrastructure

AI workloads consume infrastructure differently than traditional applications. Training runs spike GPU usage for hours or days. Inference demands consistent low-latency responses. Fine-tuning requires rapid iteration with different hardware configurations.

Every hour of GPU compute directly impacts your burn rate. A single H100 instance running continuously costs $287,452 annually on AWS at $32.77/hour. The same workload on Hetzner runs $107,660 annually. That's $179,792 in annual savings per GPU—enough to fund two additional engineering hires or extend your runway by months.

The infrastructure decisions you make today compound over quarters. Choose wrong, and you're locked into egress fees, proprietary tooling, and price increases you can't negotiate. Choose right, and you create operational leverage that scales with your AI ambitions.

European operators face an additional consideration: data sovereignty. GDPR compliance, data residency requirements, and increasing regulatory scrutiny around AI training data make European hosting more than a cost decision. It's a compliance and market access strategy. For a deeper dive into infrastructure decisions that impact your ability to scale AI systems, see our AI Infrastructure Guide: Decentralized Compute, GPU Hosting, and DePIN Networks.

Overview of Cloud Providers in Europe

AWS (Amazon Web Services)

AWS dominates global cloud infrastructure with the deepest service catalog and widest geographic footprint. Their European presence spans Frankfurt, Ireland, London, Paris, Milan, and Stockholm. For AI workloads, AWS offers EC2 instances with NVIDIA GPUs, managed Kubernetes through EKS, and SageMaker for ML operations.

The AWS advantage: mature tooling, extensive documentation, and broad third-party integration. The AWS disadvantage: premium pricing that assumes you value convenience over cost efficiency.

Microsoft Azure

Azure holds strong enterprise market share, particularly among organizations already committed to the Microsoft ecosystem. European regions include Amsterdam, Dublin, London, Paris, Frankfurt, Zurich, and Warsaw. Azure's AI infrastructure centers on GPU-enabled VMs, Azure Kubernetes Service, and Azure OpenAI Service for managed model access.

Azure pricing sits marginally below AWS—$29.84/hour for GPU instances versus AWS's $32.77/hour. That 9% difference rarely justifies switching between the two hyperscalers but matters when comparing against European alternatives.

OVHcloud

OVHcloud represents Europe's largest homegrown cloud provider, operating data centers across France, Germany, UK, Poland, and expanding into other regions. French ownership means decisions and jurisdiction remain within EU legal frameworks.

OVHcloud targets the middle ground: more managed services than pure infrastructure providers like Hetzner, but simpler pricing and better value than AWS or Azure. Their GPU offerings include both dedicated servers and public cloud instances, with pricing transparency that hyperscalers avoid.

Hetzner

Hetzner built its reputation on aggressive price-to-performance ratios. German-operated with data centers in Germany, Finland, and the US, Hetzner offers fewer regions than competitors but delivers fundamental compute, storage, and networking at a fraction of hyperscaler costs.

Hetzner's CPX and CCX instance families provide the core compute most applications need. For AI workloads, their GPU offerings—while more limited than AWS—cover the majority of training and inference use cases at prices that fundamentally change infrastructure economics.

Cost Analysis of AWS and Azure for AI Workloads

AWS Pricing for AI Workloads

AWS structures GPU pricing around instance families optimized for different workloads. The P-series targets training, the G-series handles graphics and inference, and the Inf-series uses custom Inferentia chips for cost-optimized inference.

Based on our proprietary pricing data collected June 9, 2026:

GPU instance pricing: $32.77/hour for standard GPU configurations. This reflects P4d or P5 instances with NVIDIA A100 or H100 GPUs suitable for transformer model training.

Standard compute pricing: Small instances (2 vCPU, 4GB RAM) run approximately $30/month on t3.medium equivalents. Medium configurations (4 vCPU, 16GB RAM) cost around $140/month.

AWS charges separately for storage, with EBS volumes billed per GB-month. A typical AI workload needs fast SSD storage for training data—io2 Block Express volumes run $0.125/GB-month plus $0.065 per provisioned IOPS. Training a large language model with 10TB of training data costs $1,250/month just for storage, before compute.

Network egress kills you slowly. AWS charges $0.09/GB for the first 10TB transferred out monthly. If your training pipeline pulls data from S3 or your inference API serves predictions to users, egress fees accumulate invisibly until your bill arrives. A service transferring 5TB monthly pays $450 in egress alone.

Kubernetes overhead: Running EKS for orchestration adds $0.10/hour per cluster ($73/month) plus the underlying EC2 costs. Most production deployments run 3+ availability zones with multiple node groups, multiplying these baseline costs.

The AWS ecosystem assumes you'll use managed services—SageMaker, Bedrock, Comprehend—that add convenience layers at premium pricing. These services make sense for enterprises with limited ML engineering capacity. They rarely make sense for teams capable of managing infrastructure directly.

Azure Pricing for AI Workloads

Azure's GPU pricing undercuts AWS marginally. Our June 2026 data shows Azure GPU instances at $29.84/hour, roughly 9% below AWS's $32.77/hour.

Standard compute pricing: Azure VM pricing parallels AWS with minimal difference. The same 2 vCPU/4GB configuration costs $28-32/month. Medium instances with 4 vCPU/16GB run $135-145/month depending on region and commitment.

Azure OpenAI Service pricing: This managed offering provides API access to GPT-4 and other OpenAI models. Pricing depends on model and token volume—GPT-4 Turbo costs approximately $10/1M input tokens and $30/1M output tokens. For companies building on OpenAI models without managing infrastructure, this represents genuine value. For companies training proprietary models, it's irrelevant.

Storage costs: Azure Premium SSD storage runs $0.12-0.15/GB-month, comparable to AWS. Bandwidth egress charges $0.087/GB for the first 10TB—slightly cheaper than AWS but still substantial at scale.

Azure Kubernetes Service (AKS): Unlike EKS, AKS doesn't charge for the control plane. You pay only for worker nodes. This saves approximately $73/month per cluster—meaningful for multi-cluster deployments, negligible compared to GPU costs.

The Azure advantage over AWS: tighter integration with existing Microsoft enterprise agreements and marginally lower egress fees. The Azure disadvantage compared to European providers: still 4-7× more expensive for equivalent compute resources.

Cost Analysis of OVHcloud and Hetzner for AI Workloads

OVHcloud Pricing for AI Workloads

OVHcloud positions itself between hyperscalers and bare-metal providers. Their Public Cloud offering resembles AWS/Azure with managed Kubernetes, object storage, and GPU instances. Their dedicated server line delivers better price-to-performance for workloads that don't need elastic scaling.

GPU pricing: OVHcloud GPU instances cost 40-60% less than AWS or Azure for equivalent NVIDIA GPU access, though exact pricing varies by GPU generation and availability.

Standard compute: OVHcloud's B2-series instances deliver 2 vCPU/4GB for approximately €5-8/month, already 3-4× cheaper than AWS. Their managed Kubernetes service doesn't charge control plane fees.

Storage and bandwidth: OVHcloud includes generous bandwidth allocations with each instance. Where AWS nickels-and-dimes every GB transferred, OVHcloud typically includes 1-5TB monthly per instance. Object storage costs €0.01/GB-month, roughly 40% below AWS S3.

2026 price adjustments: OVHcloud implemented selective price increases in 2026, driven by memory and storage supply chain pressure from AI demand. These increases affected specific instance families, particularly memory-optimized configurations. Despite adjustments, OVHcloud maintains substantial cost advantages over hyperscalers.

The OVHcloud value proposition: managed services comparable to AWS/Azure at prices closer to bare-metal providers. The trade-off: fewer regions, smaller ecosystem, less mature tooling.

Hetzner Pricing for AI Workloads

Hetzner delivers the most aggressive pricing in the European cloud market. Their cost structure assumes you can manage infrastructure yourself without expensive hand-holding or ecosystem lock-in.

GPU pricing: $12.29/hour based on our June 2026 proprietary data. This represents standard GPU configurations suitable for model training and inference—62% cheaper than AWS's $32.77/hour and 59% cheaper than Azure's $29.84/hour.

Standard compute: The price differential widens for standard instances. Hetzner's CPX line delivers 2 vCPU/4GB for €3.99-7.99/month versus AWS's $30/month. That's a 4-7× cost multiple in AWS's favor—paying $30 when you could pay €4 means spending $26/month per instance on nothing but brand preference.

Medium configurations show similar spreads. Hetzner CPX31 delivers 4 vCPU/8GB RAM for approximately $15/month. AWS charges around $140/month for 4 vCPU/16GB (double the RAM but 9× the price).

Storage: Hetzner includes NVMe SSD storage with each instance—typically 40-160GB depending on size. Additional storage costs €0.039/GB-month. The 10TB training dataset that costs $1,250/month on AWS runs $390/month on Hetzner.

Bandwidth: Hetzner includes 20TB monthly bandwidth with most instances. Overage charges run €1/TB—90× cheaper than AWS's $0.09/GB ($90/TB). For inference services serving predictions globally, this single difference can save thousands monthly.

April 2026 price increases: Hetzner raised prices up to 40% on some products in April 2026. A CPX instance that cost €5/month might now cost €7/month. Even post-increase, Hetzner maintains 4-7× cost advantages over AWS for standard compute and roughly 60% savings on GPU instances.

The increases reflected supply chain realities: AI demand stressing memory and storage markets, power costs rising at data centers, and IPv4 address scarcity. Hetzner absorbed these pressures longer than competitors before passing costs to customers.

Limitations: Fewer regions (Germany, Finland, US East/West, Singapore), less managed service variety, and self-service support except for higher plans. If you need 24/7 phone support and managed everything, Hetzner frustrates you. If you operate infrastructure competently, Hetzner delivers unmatched value.

Real-World Case Studies: Cost Savings from Switching to European Cloud Providers

Case Study 1: ML Training Pipeline Migration from AWS to Hetzner

Company profile: European fintech startup, 25-person engineering team, building fraud detection models.

AWS configuration: Three p4d.24xlarge instances running training jobs intermittently, t3.medium instances for orchestration, 5TB S3 storage, 2TB monthly egress for model distribution.

Monthly AWS costs:

• GPU instances: $32.77/hour × 720 hours × 3 instances = $70,790
• Standard compute: $30 × 10 instances = $300
• Storage: $0.125/GB × 5,000GB = $625
• Egress: $0.09/GB × 2,000GB = $180
• Total: $71,895/month

Hetzner configuration: GPU instances with equivalent NVIDIA hardware, CPX instances for orchestration, block storage, distributed model weights via included bandwidth.

Monthly Hetzner costs:

• GPU instances: $12.29/hour × 720 hours × 3 instances = $26,544
• Standard compute: €7 × 10 instances = €70 (~$75)
• Storage: €0.039/GB × 5,000GB = €195 (~$210)
• Egress: Included (2TB well within 20TB/instance allocation)
• Total: $26,829/month

Savings: $45,066/month (63% reduction)

Migration timeline: Four weeks. Two weeks planning and testing, one week gradual traffic migration, one week monitoring and optimization.

Performance impact: Negligible. Model training times remained within 5% of AWS performance. Network latency for inference API increased 15-20ms for US-based customers (acceptable given their EU-focused market).

Non-obvious benefits: Simplified billing (no surprise egress charges), faster feature iteration (spinning up test environments cost €4 instead of $30), and improved GDPR compliance story for European customers.

Trade-offs: Lost AWS-specific tooling integration (SageMaker, CloudWatch deep integrations). Solved by adopting open-source alternatives (MLflow, Prometheus/Grafana). Required two weeks of engineering time but created portable infrastructure less dependent on single-vendor ecosystems.

For more context on making these migration decisions and understanding the broader landscape of decentralized GPU marketplaces, read our Akash Network vs Centralized Cloud: Real Cost Analysis for AI Startups in 2026.

Case Study 2: Computer Vision Inference Service Migration from Azure to OVHcloud

Company profile: European manufacturing AI startup, 15-person team, providing visual quality inspection via API.

Azure configuration: GPU-optimized VMs for inference serving, AKS cluster with auto-scaling, blob storage for training images, 8TB monthly bandwidth.

Monthly Azure costs:

• GPU VMs: $29.84/hour × 24 hours × 4 instances = $2,865
• AKS worker nodes: $135 × 6 nodes = $810
• Storage: $0.12/GB × 3,000GB = $360
• Bandwidth: $0.087/GB × 8,000GB = $696
• Total: $4,731/month

OVHcloud configuration: Public Cloud GPU instances, managed Kubernetes, object storage, included bandwidth covering actual usage.

Monthly OVHcloud costs:

• GPU instances: ~$12/hour × 24 hours × 4 instances = $1,152
• Kubernetes nodes: €25 × 6 nodes = €150 (~$162)
• Storage: €0.01/GB × 3,000GB = €30 (~$32)
• Bandwidth: Included
• Total: $1,346/month

Savings: $3,385/month (72% reduction)

Migration timeline: Six weeks. Additional time required due to Kubernetes configuration differences and testing OVHcloud-specific networking setup.

Performance impact: Minimal. P95 latency increased 8ms (from 42ms to 50ms), acceptable for their use case. GPU throughput matched Azure within measurement variance.

Non-obvious benefits: Customer data never left EU jurisdiction (versus Azure's global backbone routing), simpler compliance documentation, and responsive support in European time zones.

Challenges: OVHcloud's ecosystem maturity lagged Azure. Some third-party monitoring integrations required custom configuration. Team spent approximately 40 engineering hours solving integration issues that would have been plug-and-play on Azure.

Decision factors: For a bootstrap-funded startup, saving $40,620 annually mattered more than 40 hours of engineering time. For a well-funded enterprise, that calculation might differ.

Impact of Recent Price Increases on European Cloud Providers

OVHcloud Price Increases

OVHcloud implemented targeted price adjustments in 2026, focusing on memory-intensive and storage-heavy configurations. The increases reflected upstream component costs rather than margin expansion.

Scope: Memory-optimized instances saw 15-25% increases. Standard compute instances experienced 5-15% adjustments. GPU pricing remained relatively stable, with increases in the 8-12% range.

Market context: These adjustments came as memory manufacturers prioritized AI hardware over commodity server RAM. High-bandwidth memory (HBM) needed for GPUs competed for fabrication capacity with standard DDR5 production. OVHcloud, operating with thinner margins than hyperscalers, passed costs through faster.

Impact on AI workloads: Training pipelines using memory-optimized instances for data preprocessing saw cost increases of approximately €50-150/month for typical configurations. GPU-heavy workloads (where compute time dominates costs) experienced minimal impact.

Competitive position: Even after increases, OVHcloud maintained 50-65% cost advantages versus AWS and Azure for most configurations. The price gap narrowed but remained substantial.

Hetzner Price Increases

Hetzner's April 2026 pricing reset affected a broader range of products, with some categories seeing increases up to 40%.

Affected products:

• CPX series (cloud instances): 20-30% increases
• Dedicated server configurations: 15-25% increases
• Storage products: 10-20% increases
• IPv4 addresses: Substantial increases reflecting global scarcity

Driver analysis: Hetzner absorbed cost pressures longer than competitors before adjusting. The April reset represented accumulated input cost inflation from:

• Power costs increasing 15-20% at German data centers
• Memory and storage component costs up 20-30%
• IPv4 address market prices doubling
• Cooling infrastructure upgrades needed for denser GPU deployments

Impact on AI workloads: A typical small AI startup running 10 CPX instances and 2 GPU instances saw monthly costs increase from approximately €500 to €650—a €150 ($162) increase. Meaningful in absolute terms, but still 5-6× cheaper than AWS for equivalent resources.

Customer response: Hetzner experienced minimal churn. The value proposition remained compelling enough that a 25% price increase still delivered 4-7× savings versus hyperscalers. Most customers adjusted budgets rather than migrated.

Strategic implications: The increases signal market reality: European providers can't infinitely undercut hyperscalers as input costs rise globally. The sustainable advantage lies in operational efficiency and reduced overhead, not unrealistic pricing.

For context on how these infrastructure cost dynamics play into broader deployment decisions, see our analysis of GPU Hosting Profitability Guide 2026: Maximizing ROI and Long-Term Sustainability.

Comparison Table: AWS, Azure, OVHcloud, and Hetzner

Pricing Comparison

| Configuration | AWS | Azure | OVHcloud | Hetzner | Savings vs AWS | |--------------|-----|-------|----------|---------|----------------| | Small Compute (2 vCPU / 4GB RAM) | $30/month | $28/month | €5-8/month ($5.40-8.60) | €3.99-7.99/month ($4.30-8.60) | 71-86% | | Medium Compute (4 vCPU / 8-16GB RAM) | $140/month | $135/month | €20-30/month ($21.50-32) | €15/month ($16) | 89% | | GPU Instance (per hour) | $32.77/hr | $29.84/hr | ~$12-18/hr (est.) | $12.29/hr | 62% | | Storage (per GB-month) | $0.125 | $0.12 | €0.01 ($0.0108) | €0.039 ($0.042) | 66% | | Bandwidth (per GB egress) | $0.09 | $0.087 | Included or minimal | €0.001 ($0.0011) | 99% | | Managed Kubernetes | $73/month | $0 | $0 | $0 | 100% |

Monthly cost for typical AI workload:

• 2 GPU instances running 12 hours/day
• 10 standard compute instances
• 5TB storage
• 2TB monthly egress

| Provider | Monthly Total | |----------|---------------| | AWS | $24,480 | | Azure | $22,095 | | OVHcloud | $9,250 (est.) | | Hetzner | $9,120 |

Annual savings when switching from AWS to Hetzner: $184,320

Performance Comparison

| Metric | AWS | Azure | OVHcloud | Hetzner | |--------|-----|-------|----------|---------| | Network latency (intra-region, P95) | 0.5-1ms | 0.5-1ms | 1-2ms | 1-2ms | | Storage IOPS (SSD) | Up to 256,000 | Up to 160,000 | Up to 75,000 | Up to 60,000 | | Inter-region bandwidth | Up to 100 Gbps | Up to 100 Gbps | 10-25 Gbps | 10-20 Gbps | | GPU availability | High | High | Medium | Medium | | Instance startup time | 30-60 seconds | 30-60 seconds | 60-120 seconds | 30-90 seconds | | API reliability | 99.99% | 99.99% | 99.9% | 99.9% |

Performance reality check: For most AI workloads, these performance differences don't matter. Training runs last hours or days—a 30-second instance startup difference is noise. Inference latency depends more on model optimization than cloud provider networking.

The exception: latency-sensitive applications serving global users might benefit from AWS/Azure's broader geographic distribution. But even then, you're paying 4-7× more for marginal latency improvements. Run the actual numbers for your use case.

Ecosystem and Support Comparison

| Factor | AWS | Azure | OVHcloud | Hetzner | |--------|-----|-------|----------|---------| | Regions | 30+ | 60+ | 15+ | 5+ | | Managed services | 200+ | 150+ | 30+ | 15+ | | Third-party integrations | Extensive | Extensive | Growing | Limited | | Documentation quality | Excellent | Good | Good | Good | | Support tiers | Multiple paid tiers | Multiple paid tiers | Multiple paid tiers | Limited (paid upgrades available) | | Community size | Very large | Large | Medium | Medium | | Data sovereignty | US-headquartered | US-headquartered | EU-headquartered | EU-headquartered |

Key Considerations for Choosing a Cloud Provider for AI Workloads in Europe

Cost Considerations

The 4-7× price differential between AWS and European providers isn't a rounding error. It's a strategic decision that impacts runway, profitability, and scaling dynamics.

When AWS/Azure costs make sense:

• You're tightly integrated with AWS-specific services (SageMaker, Bedrock, Lambda) that would cost more to replicate than you'd save on compute
• You need global presence in 20+ regions and sub-50ms latency everywhere
• Your team lacks infrastructure management skills and needs extensive managed services
• You're optimizing for speed-to-market over cost efficiency in early-stage validation

When European provider costs make sense:

• You're running standard infrastructure (compute, storage, networking) without heavy managed service dependencies
• Your team can manage Kubernetes, monitoring, and infrastructure automation
• You operate primarily in European markets or can accept slightly higher latency for US/Asia users
• You're past product-market fit and optimizing unit economics
• Your burn rate matters and $150K+ annual savings per GPU instance funds meaningful business development

The hybrid approach: Many sophisticated operators use AWS/Azure for specific managed services (managed Postgres, Redis, CloudFront CDN) while running core compute on European providers. This captures cost savings where they matter most (GPU hours) while leveraging hyperscaler strengths in commodity managed services.

Total cost of ownership considerations:

Don't forget migration costs. Budget 2-6 weeks of engineering time plus potential downtime risk. For most teams, this one-time investment pays back within 3-6 months of operation.

Factor in operational overhead. European providers typically offer less hand-holding. If your team isn't comfortable with infrastructure automation, the "savings" might evaporate in engineering time and incident response.

Calculate based on your actual workload. Spot GPU instances can change economics dramatically. Commitment discounts (1-3 year reserved instances) narrow price gaps. Run your specific numbers, not generic comparisons.

Performance Considerations

Performance matters when it matters—and often doesn't matter as much as you think.

For AI training:

• GPU compute speed: Equivalent across providers with same NVIDIA hardware
• GPU-to-GPU networking: AWS/Azure optimized fabrics outperform standard cloud networking for multi-GPU training
• Storage throughput: Matters for large dataset training; AWS/Azure typically faster
• Network latency: Irrelevant; training is compute-bound not network-bound

For inference serving:

• Response latency: Dominated by model size and optimization, not cloud provider
• Geographic distribution: AWS/Azure's broader regions help global low-latency serving
• Auto-scaling speed: AWS/Azure slightly faster but rarely bottleneck in practice
• Throughput: Primarily depends on instance size, similar across providers

For data pipelines:

• Storage IOPS: AWS/Azure offer higher-performance options for intensive workloads
• Inter-region transfer: AWS/Azure faster but also more expensive
• Batch processing: Performance differences minimal for typical Spark/Airflow workloads

Reality check: Most AI startups don't need AWS's performance envelope. If you're training GPT-4 scale models across thousands of GPUs, AWS's networking matters. If you're fine-tuning Llama 3 or serving inference APIs, Hetzner performs equivalently for 62% less cost.

For deeper exploration of making the right hardware choices for your AI workloads, see our guide on H100 vs A100 vs B200: Which GPU Should You Use for Production AI in 2026.

Support and Compliance

Support quality:

AWS and Azure provide extensive support tiers with 24/7 availability, dedicated account managers (at scale), and comprehensive SLAs. You pay premium prices partly for this support infrastructure.

OVHcloud offers multiple support tiers with European-timezone focus. Response times lag hyperscalers but remain professional and competent. Documentation quality is good, though community resources are smaller.

Hetzner operates more self-service. Basic support is email-based with 24-48 hour response times. Premium support plans available but still less comprehensive than hyperscalers. The trade-off: lower prices assume you won't need constant hand-holding.

Compliance considerations:

GDPR compliance: All four providers support GDPR compliance, but European providers simplify documentation. Data residency in EU datacenters under EU legal jurisdiction creates cleaner compliance stories than US-based providers operating EU regions.

Data sovereignty: OVHcloud and Hetzner offer genuine data sovereignty—EU ownership, EU jurisdiction, no Cloud Act exposure. AWS and Azure operate EU regions but remain subject to US legal authority. For some industries and customers, this distinction matters significantly.

Certifications: AWS and Azure maintain broader certification portfolios (SOC 2, ISO 27001, PCI DSS, HIPAA, etc.). OVHcloud and Hetzner hold major certifications but with narrower scope. Verify your specific compliance requirements against each provider's certifications.

AI-specific regulations: Europe's AI Act creates new compliance requirements. Providers with EU jurisdiction and operational focus may prove easier partners for navigating evolving AI governance requirements.

Contractual terms:

Hyperscalers typically impose standard terms with limited negotiation room until you hit significant scale. European providers often show more flexibility on contract terms, payment structures, and custom arrangements—particularly valuable for startups with unusual requirements.

Lock-in and Portability

AWS lock-in factors:

• Proprietary services (Lambda, SageMaker, Bedrock) don't port cleanly to other providers
• CloudFormation templates need rewriting for other platforms
• IAM and security configurations are AWS-specific
• Extensive third-party integration with AWS APIs creates dependencies

Mitigation: Use open standards where possible. Kubernetes instead of ECS. PostgreSQL instead of Aurora. MLflow instead of SageMaker. Each choice reduces lock-in.

Azure lock-in factors:

• Azure Active Directory integration creates switching friction
• Azure-specific managed services (Cosmos DB, Azure Functions)
• .NET ecosystem optimization for Azure
• Enterprise agreements often bundle services creating organizational inertia

Mitigation: Similar to AWS—prefer open standards and portable architectures.

European provider advantages:

• Simpler service catalogs reduce proprietary dependencies
• Infrastructure-as-code works across providers more easily
• Standard Linux and Kubernetes deployments port cleanly
• Less elaborate IAM means less reconfiguration when migrating

The portability principle: Design for portability from day one. Use Terraform or OpenTofu for infrastructure-as-code. Deploy applications in containers. Avoid provider-specific services unless they deliver overwhelming value. This strategy costs slightly more upfront but eliminates expensive migrations later.

FAQ

What are the main cost differences between AWS, Azure, OVHcloud, and Hetzner for AI workloads?

The fundamental difference is scale: AWS and Azure cost 4-7× more than Hetzner for standard compute instances and roughly 2.5× more for GPU instances.

Specifically:

• Small compute (2 vCPU/4GB): AWS $30/month vs Hetzner €4-8/month
• GPU instances: AWS $32.77/hour vs Hetzner $12.29/hour
• Bandwidth: AWS $0.09/GB vs Hetzner effectively $0.001/GB (included allocations cover most usage)

These differences compound across your infrastructure. A typical AI startup running 2 GPU instances 12 hours daily plus supporting infrastructure pays $24,480/month on AWS versus $9,120/month on Hetzner—$184,320 in annual savings.

Azure prices marginally below AWS (9% lower on GPUs) but remains far above European alternatives. OVHcloud sits between hyperscalers and Hetzner, typically 50-65% cheaper than AWS with more managed services than Hetzner.

How do recent price increases affect the cost of using European cloud providers like Hetzner and OVHcloud?

Hetzner's April 2026 price increases (up to 40% on some products) and OVHcloud's selective adjustments (15-25% on memory-intensive instances) narrowed but didn't eliminate the cost advantage versus hyperscalers.

Post-increase, Hetzner still costs 4-7× less than AWS for standard compute and approximately 62% less for GPU instances. The increases reflected real cost pressures—power, components, IPv4 addresses—not margin expansion.

For a typical AI workload, Hetzner's increases added approximately €150/month ($162) to bills that were previously €500/month. Meaningful in absolute terms, but still delivering massive savings versus AWS's equivalent $2,500+/month cost for the same resources.

The strategic takeaway: European providers can't infinitely undercut hyperscalers as input costs rise globally, but their structural cost advantages remain substantial even after adjustments.

What are the real-world cost savings when switching from AWS or Azure to Hetzner or OVHcloud?

Case study 1 (fintech ML training pipeline): Switching from AWS to Hetzner saved $45,066/month (63% reduction)—$540,792 annually. Migration took four weeks with negligible performance impact.

Case study 2 (computer vision inference service): Switching from Azure to OVHcloud saved $3,385/month (72% reduction)—$40,620 annually. Migration took six weeks with minimal performance impact.

The infrastructure cost decision compounds quarterly. Every month you delay switching from a hyperscaler to a European alternative, you're effectively choosing to spend 4-7× more than necessary on compute—capital that could fund engineering hires, extend runway, or accelerate product development. The migration friction is real but finite. The savings are permanent.