How to offset AI and digital usage
A small software product or heavy AI/cloud workload emits roughly 5–20 tonnes of CO₂e a year from data-centre energy. Here’s how to offset it.
What drives the footprint
AI and digital products run on data centres, and their footprint is dominated by electricity — for cloud compute, model training and inference, storage and data transfer. A small SaaS or AI product can emit 5–20 tonnes of CO₂e a year; heavy training workloads far more.
Efficiency work (right-sizing, efficient models, clean regions) reduces the footprint at source; renewable-energy credits offset the remainder with a claim that matches the energy driver.
Three steps, start to certificate.
Small SaaS / AI product, one year: about 10 tCO₂e. Use our calculator for your exact number.
We recommend Renewable energy at $12/tonne for this use case — Gold Standard.
We buy the credits at wholesale, retire them on the public registry on your behalf, and email you a verifiable certificate.
Cut what you can before you offset.
Offsetting is for the emissions you can’t yet eliminate. A few practical ways to lower this footprint at source:
- →Run workloads in low-carbon cloud regions.
- →Right-size infrastructure and use efficient models.
- →Schedule heavy jobs for times and grids with cleaner power.
How do I estimate my cloud footprint?
Most providers expose a carbon dashboard; use that figure, or our business calculator for a directional estimate.
Is this credible for an AI company?
Yes — pair renewable-energy retirement with efficiency work and you have a defensible, registry-traceable claim.
Does offsetting cover model training?
Yes — include training and inference energy in your estimate, then retire credits to match. Reducing waste in training comes first.