Comparison
Carbontracker vs EcoLogits
Carbontracker vs EcoLogits: Open-source tool that tracks and predicts energy use and carbon footprint of model training. Open-source library and API for…
| Field | Carbontracker Open-source tool that tracks and predicts energy use and carbon footprint of model training. | EcoLogits Open-source library and API for estimating the environmental footprint of generative AI inference. |
|---|---|---|
| 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. | Mozilla Public License 2.0; the Python library, JavaScript library, calculator and hosted API service are released as open-source tools. |
| Free tier | Yes | Yes |
| Assets covered | ||
| Platforms | On-premise, Cloud | Web, API, On-premise |
| 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 EcoLogits
Aimed at teams calling hosted generative AI APIs that want per-request footprint estimates without provider cooperation. It applies life-cycle assessment methodology to model API calls and returns energy and greenhouse gas figures through Python and JavaScript libraries, a web calculator and a hosted API. The trade-off is estimation depth: figures are modelled from published methodology and provider parameters rather than measured on provider infrastructure, so accuracy depends on those assumptions and on provider coverage. It covers inference per request; organisation-wide totals and assurance reporting still need a reporting platform.
Read the EcoLogits listingJudgement is written by the editor. Paid placements never change the ranking or the recorded field values.
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