Research & Intelligence
The Real Cost of AI Compute
GPU & LLM Pricing Comparison Workbook for AI Infrastructure
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The cheapest advertised GPU-hour can still produce the most expensive completed workload.
The Real Cost of AI Compute is a 46-page buying and decision workbook for developers, AI founders, researchers and infrastructure teams comparing cloud GPUs and LLM APIs.
Instead of comparing sticker prices alone, the book teaches you to calculate the effective cost of the outcome you actually need.
Topics include GPU-hour normalization, token pricing, VRAM requirements, precision support, throughput, utilization, queue time, minimum billing, storage, data transfer, failed jobs, retries, orchestration costs, rate limits and pricing freshness.
Scenario analysis shows how a seemingly more expensive provider can become cheaper once completion time and operational overhead are included.
Inside
- GPU and LLM cost-normalization methods
- Hidden-cost checklist
- Workload-fit and VRAM analysis
- Expected / optimistic / adverse scenarios
- Procurement and provider-comparison worksheets