Cloud Infrastructure (AWS, Azure, GCP)

The major cloud platforms - Amazon Web Services, Microsoft Azure, and Google Cloud Platform - are the primary infrastructure for most AI workloads. Each offers GPU and specialised AI chip instances for training and inference, managed machine learning platforms (SageMaker, Azure ML, Vertex AI), pre-built AI services for common tasks, and the surrounding infrastructure - storage, networking, monitoring - needed to run AI systems in production. AWS has the largest market share and broadest service catalogue. Azure has deep integration with Microsoft's enterprise tools and an exclusive relationship with OpenAI. Google Cloud offers TPU access and strong integration with open-source AI frameworks that Google has developed. The choice between them often comes down to existing relationships and infrastructure rather than AI-specific capabilities. All three are investing heavily in AI infrastructure, and the gap between them narrows with each release cycle. For most organisations, the cloud is the pragmatic choice for AI workloads. It offers flexibility to scale up and down, access to the latest hardware without capital expenditure, and managed services that reduce operational burden. The trade-offs are ongoing costs that can grow quickly if not managed carefully, potential vendor lock-in, and less control over the underlying infrastructure. Many organisations adopt a primary cloud provider while keeping the option to use others for specific workloads.