How to Bill for GPU Compute: A Technical Guide for AI Infrastructure Companies

GPU billing in the AI economy is complex due to the variable and continuous resource consumption it measures, which requires a sophisticated infrastructure for metering, pricing, and invoicing. Essential challenges include hardware heterogeneity, job variability, and cost of goods sold (COGS) volatility, all of which necessitate dynamic pricing models that account for GPU type, memory, and usage patterns. The guide outlines practical metering primitives such as GPU-hours, tokens, and VRAM, and explores pricing models like per-hour, per-inference, and dynamic pricing to manage these complexities. It also addresses edge cases such as idle time and preemption, along with implementation strategies for enterprise billing solutions that include contract-level controls, multi-entity invoicing, and real-time dashboards. The open-source platform Lago is highlighted as a tool that automates these processes, offering progressive billing and compliance features that enhance revenue predictability and customer satisfaction.

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