Guide · AI & GPU · Gated

The AI and GPU Cost Control Guide

The buyer side reference that turns our AI and GPU guide into a working playbook: training and inference levers, the spot and commitment mix, and the build versus buy math worked out.

GPU hours are the most expensive line on a modern cloud bill and the easiest to waste, with accelerators routinely sitting idle while jobs load data or notebooks run overnight. This guide is the practical companion we hand to ML and platform teams who want to control AI infrastructure cost without slowing the work down. It is vendor neutral and written from the customer's side of the table.

What is inside

• The anatomy of an AI bill: training, inference and API spend
• Why GPU utilization is the number that matters most
• Training levers: data loading, checkpointing, instance sizing
• Fine-tuning vs prompting: the cost decision tree
• Inference: batching, quantization and endpoint sizing
• Spot GPUs and reserved capacity: when each pays
• Managed AI services vs self-hosted: the crossover math
• Allocating shared GPU cost and the FinOps scope for AI
• Forecasting AI spend that finance can defend

Prefer help applying it? FinOps implementation stands up the operating model that keeps AI spend governed, on a fixed fee or no savings, no fee basis.