Comparison
Carbontracker vs ML CO2 Impact
Carbontracker vs ML CO2 Impact: Open-source tool that tracks and predicts energy use and carbon footprint of model training. Calculator and reporting…
AI Energy Monitoring · both listings verified
| Field | Carbontracker Open-source tool that tracks and predicts energy use and carbon footprint of model training. | ML CO2 Impact Calculator and reporting template for the carbon emissions of machine learning training. |
|---|---|---|
| Pricing model | Free | Free |
| Pricing published | Public pricing | Public pricing |
| Starting price | Free | Free |
| Licensing | Open source | Open source |
| Pricing details | MIT licensed; installable from PyPI with pip install carbontracker, and the optional pdfreport extra adds PDF report generation. | MIT licensed; the calculator, reporting template and emissions data are free to use, and the data is hosted on GitHub for external contributions. |
| Free tier | Yes | Yes |
| Assets covered | ||
| Platforms | On-premise, Cloud | Web |
| Region support | Global | Global |
| Compliance | Not stated | Not stated |
| API available | No | No |
| Last verified | 2026-10-22 | 2026-10-22 |
Editor's take on Carbontracker
Fits ML teams training on GPU or CPU hosts that want per-run energy and carbon figures plus a forecast of the remaining epochs, with logs and PDF reports for write-ups. The trade-off is attribution and maintenance: consumption is derived from monitored GPU and CPU power and a configured grid intensity, so results follow those inputs rather than provider billing, and it wraps the training script, which means owning the integration and its dependencies. Reporting is run-level, so organisation-wide rollups need another platform. Workloads without visible power telemetry should verify support first.
Read the Carbontracker listingEditor's take on ML CO2 Impact
Fits researchers who need a quick emissions estimate for training runs and a citation-ready template for reporting them in papers, blog posts and publications. Inputs are GPU hours, hardware type and cloud region, so the output is an approximation built from provider carbon efficiency and offset data rather than a measurement of the run. The trade-off is precision: it suits publication and comparison, not audited corporate accounting, and the web calculator has no API of its own, with runtime integration running through CodeCarbon. Teams needing continuous tracking should use a monitoring library.
Read the ML CO2 Impact listingJudgement is written by the editor. Paid placements never change the ranking or the recorded field values.
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