1. Introduction
Purpose, audience, and context of the methodology.
This document describes Greenpixie's methodology for estimating the energy consumption, carbon emissions, and water consumption of cloud-based AI inference workloads. It is intended for customers, partners, procurement teams, auditors, regulators, and technically literate readers.
Organisations that consume hosted AI models through cloud provider APIs generate energy demand and associated carbon emissions with every request. As AI adoption grows and enterprise usage shifts toward higher-volume inference, this demand increases. At enterprise volumes, the cumulative energy cost of inference is widely estimated to exceed that of model training.
Greenpixie provides per-token energy and embodied emissions estimates for cloud AI inference, supporting decisions about model selection, deployment configuration, and sustainability reporting. This methodology is a companion to the Greenpixie Methodology for Cloud Emission Measurement, which details the broader cloud emissions framework. The methodology also estimates the water consumption associated with cloud AI inference, derived from the same energy values, using the approach set out in that methodology.