In the final quarter of 2025, global IaaS spending reached $119.1 billion—30% year-on-year growth driven almost exclusively by artificial intelligence workloads (IT for Business, 2026). For French CIOs and CTOs choosing among four major providers, selecting AI cloud infrastructure now involves much more than comparing hourly rates. Actual GPU availability, hidden vector storage costs, GDPR compliance and exposure to the CLOUD Act have multiplied the decision criteria.
This is a practical guide. It compares AWS, Google Cloud Platform, Microsoft Azure and OVHcloud across the dimensions that matter when deploying language models, RAG pipelines or AI agents in production.
TL;DR — GCP offers the most predictable GPU pricing through automatic Sustained Use Discounts. AWS retains the richest managed AI service portfolio. Azure leads on OpenAI integration. OVHcloud is the only provider offering native European sovereignty with competitive H100 GPUs. Managed vector storage such as Pinecone and Weaviate costs 10 times less than self-hosting below 50 million vectors.
1. AI Cloud Infrastructure in 2026: A Rapidly Accelerating Market
1.1 Figures That Are Redefining IT Priorities
France's infrastructure cloud market was worth nearly €9 billion in 2024 (Xerfi). AI is driving growth: according to Gartner, European investment in sovereign cloud infrastructure is expected to rise from $6.86 billion in 2025 to $23.11 billion in 2027, tripling in two years.
Three converging factors explain this acceleration. First, widespread adoption of large language models (LLMs) in business workflows requires substantial GPU capacity. Second, the proliferation of Retrieval-Augmented Generation (RAG) architectures combines vector storage with real-time inference. Third, European regulatory pressure—GDPR, the Data Act and NIS2—is pushing businesses to reconsider their dependence on US hyperscalers.
1.2 Four Provider Profiles, Four Philosophies
The AI cloud infrastructure market is organized around four distinct approaches:
| Provider | Philosophy | Main strength | Main limitation |
|---|---|---|---|
| AWS | Maximum ecosystem breadth | 200+ services, Bedrock, SageMaker | Pricing complexity: 197 monthly adjustments |
| GCP | Simplicity and native AI | Proprietary TPUs, Vertex AI, predictable pricing | Lower market share in Europe |
| Azure | Microsoft + OpenAI integration | Exclusive GPT-4/GPT-5 access, Office 365 integration | Dependence on the Microsoft ecosystem |
| OVHcloud | European sovereignty | SecNumCloud, French/German data centers, bare-metal GPUs | Smaller managed service catalog |
2. GPU Availability: The Foundation of AI Infrastructure
2.1 GPUs Available from Each Provider
Choosing a cloud GPU for AI depends on the use case: model training, fine-tuning or production inference. Here is the situation in early 2026.
NVIDIA H100: 80 GB HBM3 — The de facto standard for LLM training and fine-tuning. AWS offers it through p5.48xlarge instances with eight GPUs, GCP through A3 Mega, and Azure through ND H100 v5. OVHcloud offers bare-metal access with full hardware control.
NVIDIA H200: 141 GB HBM3e — Up to 1.4 times faster than the H100 on memory-intensive workloads. Available from AWS, GCP and Azure. OVHcloud has announced it in its 2026 roadmap.
NVIDIA L40S: 48 GB GDDR6 — Optimized for cost-effective inference. Available from all four providers, with attractive pricing for production deployments.
NVIDIA Blackwell B200 — The new generation, announced with AI performance 2.5 times that of the H100. AWS, GCP and Azure have confirmed availability during 2026. OVHcloud has announced a partnership with NVIDIA for Blackwell B200/B300 clusters.
2.2 GPU Pricing: The Comparison That Matters
Prices vary considerably by billing model. Here is a realistic comparison for an H100 GPU in March 2026:
| Billing model | AWS | GCP | Azure | OVHcloud |
|---|---|---|---|---|
| On-demand: H100/hour | ~$6.88 | ~$5.50–6.00 | ~$6.98 | ~$4.50–5.00 |
| Spot/preemptible | -60–90% | -60–70% | -60–80% | Unavailable |
| One-year reservation | -40–50% | -37%: CUD | -40–50% | -20–30% |
| Three-year reservation | -60–72% | -55%: CUD | -60–72% | By quotation |
Critical point: GCP automatically applies a 30% Sustained Use Discount without a commitment once usage exceeds a monthly threshold. No other provider offers this benefit without user action. For teams consuming GPUs irregularly, as is typical in phased fine-tuning projects, this is a real differentiator.
2.3 Actual Availability: Beyond the Catalog
A catalog listing does not guarantee availability. In practice, obtaining on-demand H100 GPUs remains difficult with all three hyperscalers, particularly in European regions. Waiting lists can stretch to several weeks for clusters of eight or more GPUs.
OVHcloud stands out through its bare-metal model: you reserve a dedicated physical server, guaranteeing access to unshared hardware. The trade-off is less flexibility to scale up and no autoscaling.
Practical recommendation: for a training project requiring H100 GPUs for two to four weeks, reserve instances at least 30 days ahead with AWS, GCP or Azure. OVHcloud bare-metal provisioning generally takes under 48 hours.
3. Vector Storage: The Hidden Cost of RAG Architectures
3.1 Why Vector Storage Has Become a Critical Budget Item
Every enterprise generative AI application eventually needs vector storage. Whether you are building a document chatbot, semantic search engine or AI agent with contextual memory, embeddings must be stored, indexed and queried in milliseconds.
This cost does not appear on a single billing line. It combines raw vector storage, cost per search query, bandwidth between the vector database and inference service, and the compute needed to keep indexes in memory.
3.2 Managed Versus Self-Hosted Vector Databases: The Real Comparison
Here is a cost comparison for a concrete case: 10 million vectors with 1,536 dimensions—the OpenAI text-embedding-3-large format—and 50 GB of metadata.
| Solution | Type | Estimated monthly cost | Main advantage |
|---|---|---|---|
| Pinecone Serverless | Managed SaaS | ~$64 | No operations overhead, automatic scaling |
| Weaviate Cloud | Managed SaaS | ~$85 | Hybrid keyword and vector search included |
| Self-hosted Qdrant on AWS | Self-hosted on EC2 | ~$660 | Full control, no vendor lock-in |
| pgvector on RDS | PostgreSQL extension | ~$180–350 | Native integration with your existing database |
| pgvector on Cloud SQL: GCP | PostgreSQL extension | ~$150–300 | The same benefits in the GCP ecosystem |
| Azure AI Search | Managed Azure service | ~$250–500 | Native Azure OpenAI integration |
The crossover point is around 60–80 million queries per month or 100 million vectors. Below that, managed SaaS solutions such as Pinecone and Weaviate are dramatically cheaper than self-hosting because they eliminate hidden DevOps costs. Above it, every additional query adds to the SaaS bill while self-hosted costs remain fixed.
3.3 Vector Storage and Cloud Selection: Native Integrations
Each hyperscaler promotes its own vector search solution, with very different maturity levels:
AWS offers Amazon OpenSearch Serverless with vector support and Amazon Bedrock Knowledge Bases, which natively integrates vector storage for RAG pipelines. The benefit is keeping everything in the AWS ecosystem with unified billing. The downside is lock-in.
GCP focuses on Vertex AI Vector Search, formerly Matching Engine, with direct Vertex AI integration for training and inference workflows. Performance is excellent at high volumes, but the learning curve is steep.
Azure offers Azure AI Search with native vector indexing, directly connected to Azure OpenAI Service. It is the most integrated option if you already use GPT models through Azure.
OVHcloud does not offer a native managed vector database. You deploy Qdrant, Weaviate or pgvector on standard compute instances. This requires more operational work but avoids vendor lock-in at the data layer.
4. Object Storage and Training Data: The Costs Nobody Examines
4.1 Standard Storage Pricing
AI projects generate massive data volumes: training datasets, model checkpoints—a single 70B LLM checkpoint weighs 130–150 GB—inference logs and archived embeddings. Object storage is the foundation of this infrastructure.
| Service | Standard storage: $/GB/month | Archive: $/GB/month | Egress: $/GB for first 10 TB |
|---|---|---|---|
| AWS S3 Standard | 0.023 | 0.00099: Glacier Deep Archive | 0.09 |
| GCS Standard | 0.020 | 0.0012: Archive | 0.12 |
| Azure Blob Hot | 0.0208 | 0.00099: Archive | 0.087 |
| OVHcloud Object Storage | 0.011 | 0.0024: Archive | Free: included |
4.2 The Egress Trap: Why OVHcloud Changes the Equation
Data transfer out, or egress, is the most underestimated expense in cloud AI projects. At 100 TB of outbound transfers per month, egress bills reach $8,500–10,000 with US hyperscalers, often exceeding storage costs themselves.
OVHcloud includes outbound traffic in its offerings at no additional charge. For an AI pipeline regularly transferring datasets between storage locations and compute clusters, this difference can save 20–40% of total infrastructure cost.
Concrete scenario: a French mid-sized company training a business model on 5 TB of proprietary data, with three training cycles a month and checkpoint transfers between regions, saves $1,500–3,000 monthly on egress alone by choosing OVHcloud over AWS or GCP.
4.3 The European Data Act's Impact on Storage Strategy
The Data Act comes fully into application in September 2026, with specific cloud data portability provisions. Providers will have to facilitate transfers between clouds, and excessive egress fees will be regulated.
This regulation should lower exit costs at US hyperscalers. It also creates a technical portability obligation: training data, models and vector indexes must be exportable in standard formats. This is another reason to favor open formats—ONNX for models and Parquet for datasets—from the design stage of your AI architecture.
5. Data Sovereignty: A Criterion You Can No Longer Ignore
5.1 CLOUD Act Versus GDPR: Structural Incompatibility
The US CLOUD Act of 2018 allows US authorities to demand access to data stored by US companies, even when the data is physically hosted in Europe. AWS, Azure and GCP are all subject to this law; hosting data in an AWS Paris data center does not protect you.
GDPR requires control and protection of European personal data. These two legal frameworks are structurally incompatible: complying with the CLOUD Act means violating GDPR, and vice versa.
For regulated sectors such as healthcare, finance, defense and public administration, this contradiction directly determines cloud provider selection.
5.2 SecNumCloud: France's Trusted Standard
SecNumCloud is the cybersecurity qualification issued by ANSSI. Version 3.2, currently in force, covers nearly 1,200 control points and explicitly aims to protect against extraterritorial laws.
| Provider | SecNumCloud qualification | CLOUD Act immunity | Data centers in France |
|---|---|---|---|
| OVHcloud | In progress: Nutanix-qualified offering planned | Yes: European ownership | Yes: Gravelines, Strasbourg, Roubaix |
| AWS | No | No | Yes: Paris, three AZs |
| GCP | No: through S3NS/Thales | No, except S3NS | Yes: Paris, three AZs |
| Azure | No: through Bleu/Orange-Capgemini | No, except Bleu | Yes: Paris, Marseille |
Hybrid models are emerging: S3NS, combining Thales and Google, offers a trusted cloud using GCP technology in a SecNumCloud-qualified environment. Bleu, from Orange and Capgemini, does the same for Azure services. These offerings combine hyperscaler functionality with a sovereignty layer, but they are still being deployed and their AI service catalogs remain limited compared with native offerings.
5.3 A Sovereignty Decision Matrix
Your sovereignty requirements depend on your sector and the nature of the data your AI processes:
Level 1 — Non-sensitive data: public data, marketing content and technical documentation. Any provider with European hosting suffices. AWS Paris, GCP Paris or Azure France meets the need.
Level 2 — Confidential business data: customer data, financial data and intellectual property. Favor a provider under European legal control. OVHcloud is the first choice, or S3NS/Bleu once their AI catalogs are complete.
Level 3 — Regulated data: healthcare under HDS, defense data and public administration. SecNumCloud is mandatory. Choose OVHcloud with SecNumCloud qualification, or the forthcoming sovereign S3NS and Bleu offerings.
6. Managed AI Services: The Ecosystem Beyond Compute
6.1 Each Provider's AI Service Offering
AI cloud infrastructure extends beyond GPUs and storage. Managed services—MLOps platforms, inference APIs and fine-tuning tools—determine how quickly teams can deliver AI features into production.
AWS leads in catalog breadth: Amazon Bedrock for access to Claude, Llama, Mistral and other foundation models; SageMaker as a complete MLOps platform; Kendra for enterprise search; Textract; Comprehend; and Rekognition. It has the most mature and extensively documented ecosystem.
GCP stands out through Vertex AI, a unified platform covering the full lifecycle: training, fine-tuning, evaluation, deployment and monitoring. Native Gemini access and BigQuery integration for AI analytics are strengths. Proprietary Tensor Processing Units (TPUs) offer better performance per dollar than NVIDIA GPUs for some training workloads.
Azure builds on its exclusive OpenAI partnership: Azure OpenAI Service provides access to GPT-4, GPT-4o and GPT-5 with enterprise guarantees covering SLAs, compliance and data isolation. Integration is natural for organizations already invested in Microsoft 365 and Copilot.
OVHcloud offers AI Deploy for model deployment and AI Training for GPU training, with a smaller but rapidly growing catalog. Its SambaNova partnership for low-latency inference and the SHAI AI agent program announced at Summit 2025 demonstrate an expanding offering.
6.2 MLOps Capabilities Compared
| Capability | AWS | GCP | Azure | OVHcloud |
|---|---|---|---|---|
| Integrated MLOps platform | SageMaker | Vertex AI | Azure ML | AI Training + AI Deploy |
| Managed foundation models | Bedrock: multiple providers | Model Garden: Gemini, etc. | Azure OpenAI: exclusive GPT | Through partnerships: SambaNova |
| Managed fine-tuning | Yes | Yes | Yes | Manual: notebooks |
| Feature store | SageMaker Feature Store | Vertex AI Feature Store | Azure ML Feature Store | No |
| Model monitoring | SageMaker Model Monitor | Vertex AI Model Monitoring | Azure ML Monitoring | Not native |
| Native ML CI/CD | SageMaker Pipelines | Vertex AI Pipelines | Azure ML Pipelines | Not native |
7. Decision Scenarios: Which Cloud for Which AI Project?
7.1 Scenario 1 — An SME Deploying an Internal RAG Chatbot
Context: 500 employees, 50,000 internal documents, an LLM chatbot with retrieval augmentation. AI budget: €2,000–5,000/month.
Recommendation: Azure with Azure OpenAI Service and Azure AI Search. GPT-4 integration is the simplest to implement, Azure AI Search handles vector indexing natively, and SharePoint/Teams integration facilitates adoption.
Sovereign alternative: OVHcloud with self-hosted Mistral AI and Qdrant. Lower GPU costs and assured sovereignty, but greater integration effort.
7.2 Scenario 2 — A Mid-Sized Company Training a Proprietary Business Model
Context: a manufacturing company with 5 TB of proprietary technical data, fine-tuning a 7B–13B LLM for technical assistance. Budget: €10,000–20,000/month.
Recommendation: GCP with Vertex AI. Sustained Use Discounts automatically reduce the cost of long training cycles, Vertex AI simplifies the MLOps pipeline, and TPU v5 provides excellent price/performance for fine-tuning.
Sovereign alternative: OVHcloud bare-metal H100 with an open-source stack: PyTorch, MLflow and Weights & Biases. Free egress significantly reduces the transfer costs of large datasets.
7.3 Scenario 3 — A SaaS Startup Integrating AI into Its Product
Context: a B2B SaaS product needing highly available LLM inference, variable scaling to accommodate usage spikes, and an optimized budget. Budget: €3,000–8,000/month.
Recommendation: AWS with Bedrock. Multiple model options—Claude, Mistral and Llama—avoid dependence on a single model provider. Autoscaling is the most mature, and the AWS ecosystem remains the most requested by enterprise customers.
7.4 Scenario 4 — A Public Body or Regulated Sector
Context: a public administration or healthcare company with sensitive data under HDS or SecNumCloud obligations. Budget varies.
Recommendation: OVHcloud for infrastructure, supplemented by future S3NS or Bleu offerings once managed AI services are available in a qualified environment. In the short term, deploy an open-source stack such as Mistral or Llama on SecNumCloud-qualified OVHcloud bare metal.
8. Summary Decision Matrix
To choose among the four providers, here are weighted criteria based on field experience:
| Criterion | Weight | AWS | GCP | Azure | OVHcloud |
|---|---|---|---|---|---|
| GPU availability | 25% | ★★★★ | ★★★★ | ★★★★ | ★★★ |
| GPU cost: TCO | 20% | ★★★ | ★★★★ | ★★★ | ★★★★★ |
| Managed AI services | 20% | ★★★★★ | ★★★★ | ★★★★ | ★★ |
| Native vector storage | 10% | ★★★★ | ★★★★ | ★★★★★ | ★★ |
| Data sovereignty | 15% | ★★ | ★★ | ★★ | ★★★★★ |
| Egress/transfer costs | 10% | ★★ | ★★ | ★★ | ★★★★★ |
Interpretation: no provider leads on every criterion. The optimal choice depends on how you weight functional breadth—AWS, GCP or Azure—against sovereignty and cost control through OVHcloud.
FAQ
Which cloud is cheapest for training an AI model in 2026?
On raw H100 GPU hourly rates, OVHcloud offers the lowest prices among the four major providers at around $4.50–5.00/hour on demand. However, GCP can be more economical through automatic 30% Sustained Use Discounts and proprietary TPUs. Total cost also depends on egress and associated storage.
Which offers the best managed AI services: AWS, Azure or GCP?
AWS offers the broadest catalog, including Bedrock, SageMaker and more than 15 AI services. Azure stands out through exclusive OpenAI GPT access with enterprise guarantees. GCP offers the most integrated MLOps platform through Vertex AI. The best choice depends on your existing stack and the foundation model you want to use.
Is OVHcloud a credible AI alternative to hyperscalers?
OVHcloud offers competitive H100 and L40S GPUs, with a decisive advantage in sovereignty and free egress. Its managed AI service catalog remains smaller, requiring more integration work. For organizations subject to SecNumCloud or concerned with sovereignty, it is the most direct choice.
How should you choose between managed and self-hosted vector databases?
Below 50 million vectors and 60–80 million monthly queries, SaaS solutions—Pinecone at around $64/month for 10 million vectors and Weaviate at around $85—cost substantially less than self-hosting at around $660 on EC2. Beyond those thresholds, SaaS costs grow linearly while self-hosted costs remain fixed.
Does the CLOUD Act affect AI data hosted in Europe by AWS?
Yes. The CLOUD Act applies to every US company regardless of the data's physical location. Hosting data in AWS's Paris region does not protect you from a US authority's access request. Only a provider with European ownership, such as OVHcloud, guarantees legal immunity from the CLOUD Act.
What is the best multi-cloud strategy for AI?
The most pragmatic strategy combines a hyperscaler—AWS, GCP or Azure—for managed AI services and high-volume inference with OVHcloud for sensitive data storage and training workloads with high egress costs. Use open formats such as ONNX and Parquet and portable vector databases such as pgvector or Qdrant to maintain portability.
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