AWS vs Azure vs GCP: Choosing the Right Cloud for Your AI Workloads

Selecting the right cloud provider for your AI infrastructure is one of the most consequential decisions you'll make. Each platform has distinct strengths, and the right choice depends on your specific requirements.

Quick Comparison

AWS: The Flexibility Champion

Amazon Web Services offers the broadest service catalogue and the most mature ecosystem.

Strengths for AI

• Amazon SageMaker: End-to-end ML platform with built-in algorithms
• Amazon Bedrock: Access to foundation models (Claude, Llama, Titan)
• Largest GPU selection: Widest variety of instance types
• Mature DevOps tooling: Well-established CI/CD pipelines

Best When

• You need maximum flexibility
• Your team has AWS experience
• You require specific GPU configurations
• Integration with other AWS services is valuable

Azure: The Enterprise Integrator

Microsoft Azure excels in enterprise environments, particularly those already invested in the Microsoft ecosystem.

Strengths for AI

• Azure OpenAI Service: Direct access to GPT-4 with enterprise compliance
• Microsoft 365 integration: Copilot capabilities across Office suite
• Hybrid cloud: Seamless on-premises to cloud connectivity
• Enterprise security: Built-in compliance for regulated industries

Best When

• Your organisation uses Microsoft 365
• Enterprise compliance is non-negotiable
• Hybrid deployment is required
• You want direct OpenAI API access with SLAs

GCP: The Data and ML Native

Google Cloud Platform was built with data and machine learning at its core.

Strengths for AI

• Vertex AI: Unified ML platform with AutoML capabilities
• TPU access: Custom AI accelerators for training and inference
• BigQuery ML: Train models directly on your data warehouse
• Gemini integration: Native access to Google's latest AI models

Best When

• Data analytics is central to your workloads
• You need cutting-edge ML research capabilities
• Cost optimisation for sustained workloads is priority
• Google Workspace is your productivity suite

Our Recommendation

There's no universally "best" cloud. The right choice depends on:

1. Existing investments: What's your current technology stack?
2. Team expertise: Where does your team have experience?
3. Specific requirements: Compliance, GPU needs, integration requirements
4. Long-term strategy: Where is your organisation heading?

Multi-Cloud Considerations

Many enterprises now adopt multi-cloud strategies, using different providers for different workloads. This adds complexity but provides:

• Vendor negotiation leverage
• Best-of-breed service selection
• Disaster recovery options
• Geographic flexibility

Getting It Right

The cloud decision isn't just technical—it's strategic. We help organisations evaluate their options, design cloud architecture, and implement AI solutions on whichever platform best fits their needs.

Need help choosing and implementing your cloud strategy? Let's talk.