How accurate is this estimate?

It is an order-of-magnitude estimate, not a precise measurement. Per-query energy varies widely with prompt length, output length, model size, and data-center efficiency, and most providers do not publish exact figures. Use it for directional sustainability reporting, not as an audited number.

Where do the per-query energy figures come from?

They are drawn from published research and provider disclosures on inference energy use, which generally place a typical short text query in the range of a fraction of a watt-hour for small models up to a few watt-hours for large models, with image generation higher. The tool uses representative midpoints.

What carbon intensity does it use?

It converts energy to emissions using a configurable grid carbon-intensity figure (grams of CO2 per kWh). You can set it to your data-center region's value; the default reflects a typical mixed grid. Cloud regions on low-carbon grids will be substantially lower.

Does this include training, or just inference?

Only inference — the energy of running queries. Training a large model is a large one-time cost amortized across all users, and is generally reported separately by the model provider rather than by you as an API consumer.

Can I use this for an ESG or CSRD report?

It is a reasonable starting estimate and a transparent methodology, but formal ESG/CSRD reporting should use provider-supplied figures where available and be reviewed by your sustainability team. Document your assumptions alongside the number.

AI Environmental Impact Estimator

AI environmental impact estimator

Generative AI has a real energy cost, and sustainability teams increasingly need a number for it. This estimator turns your usage pattern — model tier and monthly query volume — into an estimated energy use and carbon footprint you can put in a report. It uses representative per-query energy figures from published research and provider disclosures, then converts energy to emissions using a grid carbon-intensity value you can tune to your region.

How it works

Inference energy scales roughly with model size and output length. The tool assigns a representative per-query energy figure to each model tier — small, mid, and large text models, plus image generation, which costs considerably more per output. It multiplies by your monthly volume to get kilowatt-hours, then multiplies kWh by your chosen grid carbon intensity (grams CO2 per kWh) to get CO2-equivalent emissions. Relatable comparisons (such as kilometres driven) make the number meaningful in a report. All calculation is local.

Tips and notes

It is an estimate. Per-query energy varies with prompt and output length; treat the result as order-of-magnitude, not audited.
Set your real grid intensity. A low-carbon cloud region can be five to ten times cleaner than a coal-heavy grid — this dominates the result.
Inference only. Training is a separate, provider-side cost, not yours as an API consumer.
Document assumptions. For ESG/CSRD reporting, record your inputs and prefer provider-supplied figures where they exist.