Cloud Computing for AI

Cloud computing for AI means using remote servers, storage, and services — typically from Amazon Web Services (AWS), Microsoft Azure, or Google Cloud — to build, train, and deploy AI systems instead of buying and maintaining your own hardware.

Why AI needs the cloud

Training modern AI models requires enormous computational resources — thousands of GPUs running for weeks or months. Very few organisations can justify owning this hardware. Cloud computing lets you rent exactly the resources you need for exactly the time you need them.

Key cloud AI services

• Compute instances — virtual machines with GPUs or TPUs for training and inference
• Managed AI services — pre-built APIs for common tasks like text analysis, image recognition, and translation (no model training required)
• Model hosting — platforms that deploy your trained models and handle scaling automatically
• Data storage — scalable storage for training datasets that can be terabytes or petabytes
• MLOps tools — services for tracking experiments, managing model versions, and monitoring deployed models

Cloud vs. on-premises

• Cloud advantages: no upfront hardware cost, instant scaling, access to latest hardware, pay-per-use pricing, managed services that reduce engineering burden
• On-premises advantages: data stays within your control, predictable costs for steady workloads, no dependency on external providers, compliance with strict data residency requirements
• Hybrid: many organisations use cloud for training (burst compute) and on-premises for inference (steady workload)

Cost management

Cloud AI costs can escalate quickly. Common cost-control strategies include:

• Using spot or preemptible instances for training (up to ninety per cent cheaper but can be interrupted)
• Right-sizing instances rather than defaulting to the largest available
• Shutting down resources when not in use
• Using serverless inference that scales to zero when there is no traffic