There are thousands of ways to calculate carbon that are all valid, that’s why a similar usage amount in AWS and Azure will give you wildly different numbers. We prioritise consistency, coverage, and transparency. If the users understand where the numbers come from, and we are applying the similar data science across all clouds, then you have comparable numbers. We get our numbers audited by 3rd parties regularly to ensure robustness and credibility, but an accurate number for your entire AWS environment isn’t useful if you are just trying to calculate the difference between an AMD instance family, and a Graviton instance family. This is where we focus our methodology and why it works inside of Infracost.
A big focus now is applying this same level of rigorousness to different AI models and their impact. Batching, caching, model size and manufacturer are the choices engineers are making now. We want to ensure that choices being made are cost and carbon efficient.
Curious to know what decision you're making at the moment that's triggered you looking into your own methodology?