The economics of running AI at scale

Why it matters

As AI moves into production, the costs of compute, data and operations become a significant line in technology budgets. Companies are learning to manage the unit economics of AI as closely as cloud spending.

Early AI projects often focus on capability, with cost considered later. At scale, choices about models, infrastructure and usage patterns have a large effect on cost, so finance and technology teams are managing AI spending together.

What's changing

• Inference costs grow with usage and can exceed training costs over time.
• Smaller and specialised models are cheaper to run for many tasks.
• FinOps practices are being extended to AI workloads.

How leaders are responding

• Tracking cost per transaction or task for each AI application.
• Matching model size to the task.
• Caching, batching and routing requests to reduce compute.

What good looks like

• The smallest model that meets the quality bar, chosen per task
• Cost per outcome tracked, not only cost per token
• Caching, routing and batching built into the platform

What to watch

• Pricing changes from model and cloud providers.
• Smaller and open models in production.
• Hardware availability and data-centre capacity.

Where to start