Comparison
Carbontracker vs Experiment Impact Tracker
Carbontracker vs Experiment Impact Tracker: Open-source tool that tracks and predicts energy use and carbon footprint of model training. Drop-in tracker…
| Field | Carbontracker Open-source tool that tracks and predicts energy use and carbon footprint of model training. | Experiment Impact Tracker Drop-in tracker for the energy use, carbon emissions and compute utilization of ML experiments. |
|---|---|---|
| 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 Python package published on PyPI as experiment-impact-tracker. |
| Free tier | Yes | Yes |
| Assets covered | ||
| Platforms | On-premise, Cloud | On-premise |
| 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 Experiment Impact Tracker
Fits research groups that want a drop-in tracker producing energy, carbon and compute-utilisation logs, an online appendix and a generated carbon impact statement for papers. Hardware support is specific: Linux hosts with Intel RAPL or powergadget interfaces and NVIDIA GPUs, with real-time Californian grid intensity from caiso.com in that jurisdiction. The trade-off is scope: emissions are estimated from logged power and an intensity source, and the tool is research-oriented, so pipelines, dashboards and organisational rollups are not included. Teams outside those hardware or grid constraints should check support before adopting.
Read the Experiment Impact Tracker listingJudgement is written by the editor. Paid placements never change the ranking or the recorded field values.
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