Beyond the GPU-Hour Rate: A Clearer Case for AI Infrastructure

The challenge

GPU-hour rates did not tell AI buyers what a cluster would cost for their own workload—or how much productive work it would deliver. Our client had commissioned independent research on the wider costs of reliability, setup, storage, networking and support, but needed customer-facing tools that could apply those findings to each buyer’s requirements. Spreadsheet-led comparisons were difficult to scale and hard for buyers to examine for themselves.

What we delivered

• A client-owned suite of pricing, total cost of ownership (TCO) and Goodput calculators that turns rates and research findings into an interactive buyer journey.
• Guided inputs for workload, GPU cluster size and commitment term, with indicative pricing and an itemised cost breakdown.
• TCO comparisons covering compute, storage, networking, support, setup and internal engineering, with editable assumptions and provider scenarios.
• Goodput modelling to show how failures, recovery and lost productive GPU time can change the effective cost of completing a workload.
• Visible assumptions and source references, plus downloadable reports for buyer discussions and sales follow-up.
• Admin controls so our client can update rates, assumptions and references as the market changes, with review before publication.

Business value

Buyers can evaluate estimated total cost and productive output for their intended workload, rather than compare GPU-hour rates alone. Technical and finance stakeholders can examine the assumptions behind each comparison. The sales team can create consistent, customer-specific business cases without rebuilding each analysis in a spreadsheet, while our client retains control of the published tools and appropriate attribution to an independent research firm.