Comparison
Carbontracker vs CodeCarbon
Carbontracker vs CodeCarbon: Open-source tool that tracks and predicts energy use and carbon footprint of model training. Open-source Python library that…
| Field | Carbontracker Open-source tool that tracks and predicts energy use and carbon footprint of model training. | CodeCarbon Open-source Python library that estimates the carbon emissions of compute. |
|---|---|---|
| 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 (Code Carbon NGO); install with pip and run locally or on cloud instances. |
| Free tier | Yes | Yes |
| Assets covered | ||
| Platforms | On-premise, Cloud | On-premise, Cloud |
| Region support | Global | Global |
| Compliance | Not stated | Not stated |
| API available | No | Yes |
| 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 CodeCarbon
Fits ML engineers who want per-run emissions estimates embedded in training or inference code with a few lines of Python and no procurement step. The trade-off is what a library can know: results rest on measured CPU, GPU and RAM power combined with regional grid intensity data it ships or fetches, so accuracy follows its calibration and factor sources rather than any provider billing detail, and someone still owns dashboards, upgrades and factor refreshes when it is self-hosted. Organisations needing audited, organisation-wide accounting should feed its output into a reporting platform.
Read the CodeCarbon listingJudgement is written by the editor. Paid placements never change the ranking or the recorded field values.
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