Cloud and Hybrid Architecture for AI — AWS, Azure, GCP and On-Premise

The Cloud as the Foundation of Modern AI

Amsterdam, with one of the densest datacenter ecosystems in the world, perfectly embodies the convergence between cloud infrastructure and artificial intelligence. The three hyperscalers — AWS, Azure, and GCP — operate major regions there, and European companies are massively deploying their AI workloads.

But choosing a cloud architecture for AI goes beyond selecting a provider. It means designing an infrastructure capable of supporting model training, large-scale inference, massive data storage, and regulatory compliance — all with controlled costs.

Cloud Provider Comparison for AI

AWS (Amazon Web Services)

AI Strengths:

• SageMaker: end-to-end ML platform (notebooks, training, deployment)
• Bedrock: access to foundation models (Claude, Llama, Titan)
• Inferentia/Trainium: custom chips for AI inference and training
• S3 + Glue: robust data lake and ETL

Key AI Services:

| Service | Usage | |---------|-------| | SageMaker | ML training and deployment | | Bedrock | LLMs as a Service | | Comprehend | NLP | | Rekognition | Computer vision | | Lex | Conversational chatbots | | Kendra | Enterprise search (RAG) |

Azure (Microsoft)

AI Strengths:

• Azure OpenAI Service: native access to GPT-4, DALL-E with enterprise compliance
• Azure ML: ML platform with AutoML and pipelines
• Microsoft 365 Integration: Copilot within the Office ecosystem
• Cognitive Services: pre-built AI APIs

Distinctive advantage: Integration with the Microsoft ecosystem (Active Directory, Teams, Office) makes Azure the natural choice for enterprises already on the Microsoft stack.

GCP (Google Cloud Platform)

AI Strengths:

• Vertex AI: unified ML platform with AutoML and custom training
• TPUs: specialized hardware for training large models
• BigQuery ML: ML directly in the data warehouse
• Gemini API: access to Google models

Distinctive advantage: Google's legacy in AI/ML (TensorFlow, BERT, Transformer) translates into particularly mature tools for deep learning.

Global Comparison Table

| Criterion | AWS | Azure | GCP | |-----------|-----|-------|-----| | ML Maturity | Very high | High | Very high | | Native LLMs | Bedrock (multi) | OpenAI (exclusive) | Gemini | | AI Hardware | Inferentia, Trainium | Nvidia GPUs | TPUs, Nvidia GPUs | | Data ecosystem | S3, Glue, Redshift | Data Lake, Synapse | BigQuery, Dataflow | | European regions | 8+ | 12+ | 6+ | | GPU pricing | $$$ | $$$ | $$ | | Enterprise features | Excellent | Excellent | Good |

Hybrid Architecture: The Best of Both Worlds

Why Hybrid for AI?

Hybrid architecture combines public cloud and on-premise infrastructure (or private cloud). For AI, this approach addresses specific needs:

• Data sovereignty: certain data cannot leave the territory (GDPR, Swiss DPA, health data)
• Latency: edge inference requires physical proximity
• Costs: occasional training justifies the cloud, while continuous inference can be cheaper on-premise
• Compliance: certain regulations require physical control of servers

Hybrid Architecture Patterns

Pattern 1: Train in Cloud, Infer On-Premise

Cloud (AWS/Azure/GCP)          On-Premise
├── Training GPU cluster       ├── Inference servers
├── Data preprocessing         ├── Model cache
├── Experiment tracking        ├── API endpoints
└── Model registry       →→→   └── Monitoring

Training, which is GPU-intensive, is done in the cloud. The trained model is deployed on-premise for inference, ensuring data sovereignty in production.

Pattern 2: Data On-Premise, Compute in Cloud

Sensitive data stays on-premise. Only anonymized or synthetic data is sent to the cloud for training. Swiss companies, supported by IA PME Suisse, frequently adopt this pattern to comply with the DPA.

Pattern 3: Multi-Cloud with Orchestration

Leveraging each provider's strengths:

• Azure for LLMs (OpenAI Service)
• AWS for data lake and ML pipeline (SageMaker)
• GCP for high-performance training (TPUs)
• On-premise for sensitive data and edge inference

Multi-Cloud Orchestration

| Tool | Function | |------|----------| | Kubernetes (K8s) | Cross-cloud container orchestration | | Terraform | Infrastructure as Code multi-provider | | MLflow | Model registry and tracking cross-environment | | KubeFlow | ML pipelines on Kubernetes | | Anthos / Arc / Omni | Hyperscaler hybrid solutions |

GPU Infrastructure for AI

Hardware Selection

GPU hardware is the main limiting factor in AI architectures:

| GPU | VRAM | Usage | Cloud Price (h) | |-----|------|-------|-----------------| | Nvidia A100 | 80 GB | Training + Inference | $3-5 | | Nvidia H100 | 80 GB | High-perf training | $5-8 | | Nvidia L4 | 24 GB | Optimized inference | $0.7-1.2 | | Nvidia T4 | 16 GB | Budget inference | $0.3-0.5 | | AWS Inferentia2 | 32 GB | AWS inference | $0.7-1.0 | | Google TPU v5 | 16-96 GB | Google training | $1.5-4.0 |

GPU Sizing for LLMs

LLMs require VRAM proportional to their size:

• 7B parameters (Llama 3 7B): 1x A100 or 1x L4 (quantized)
• 13B parameters: 1x A100 80GB
• 70B parameters: 2-4x A100 or 1x H100
• 405B parameters: 8x H100 (cluster)

For inference, quantization (INT4/INT8) divides memory requirements by 2 to 4.

Security and Compliance

Zero-Trust Architecture for AI

Security of cloud and hybrid AI architectures is based on the Zero Trust principle:

• Encryption: data encrypted at rest and in transit (TLS 1.3, AES-256)
• Identity & Access: granular IAM, MFA, least privilege
• Network: VPC, private endpoints, no public model exposure
• Audit: exhaustive logging of all model and data access

Trustly-AI emphasizes that trust in AI starts with a secure infrastructure, especially in hybrid architectures where data moves between environments.

GDPR and AI Act Compliance

Architecture must integrate from the design phase:

• Data residency: data stays in the appropriate region
• Right to erasure: ability to delete a user's data from the training set
• Audit trail: tracing personal data usage in the ML pipeline
• Risk assessment: AI system classification according to the European AI Act

Cloud AI Cost Optimization

Cost Reduction Strategies

1. Spot/Preemptible instances: up to -90% for training (with checkpointing)
2. Reserved instances: -30 to -60% for continuous inference
3. Auto-scaling: adapt resources to demand
4. Model optimization: quantization and distillation to reduce GPU needs
5. Data tiering: hot/cold storage based on access frequency

Example Cloud AI Budget

For an SMB deploying a RAG system with chatbot:

| Component | Service | Monthly Cost | |-----------|---------|-------------| | Vector DB | Pinecone Starter | $70 | | LLM API | Claude 3 Haiku | $200 | | Compute | AWS Lambda | $50 | | Storage | S3 | $30 | | Monitoring | CloudWatch | $20 | | Total | | $370/month |

An accessible budget demonstrating that AI in production is no longer reserved for large enterprises.

2025 Trends

Serverless AI

Serverless functions (Lambda, Cloud Functions) increasingly integrate native AI capabilities, eliminating infrastructure management.

European Sovereign AI

Sovereign cloud initiatives (Gaia-X, NumSpot, S3NS) offer European alternatives for sensitive AI workloads.

GPU-as-a-Service

Players like CoreWeave, Lambda Labs, and Together AI offer on-demand GPU specialized for AI, often cheaper than the hyperscalers.

Conclusion

The choice between cloud, on-premise, and hybrid for AI depends on your specific constraints: data volume, latency requirements, budget, compliance, and internal skills. Hybrid architecture is emerging as the dominant pattern in Europe, combining the power of cloud for training and on-premise control for sensitive data.

Deepen your knowledge with our guide on AI architecture fundamentals and discover the AI landscape in Europe.

For more depth, consult AI security architecture and our guide on MLOps pipelines. Read also: Edge AI and IoT and the AI landscape in Switzerland.