Comparison
Carbontracker vs Eco2AI
Carbontracker vs Eco2AI: Open-source tool that tracks and predicts energy use and carbon footprint of model training. Python library for tracking the CO2…
| Field | Carbontracker Open-source tool that tracks and predicts energy use and carbon footprint of model training. | Eco2AI Python library for tracking the CO2 emissions of machine learning models. |
|---|---|---|
| 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. | Released under the Apache License 2.0 and installable from PyPI. |
| 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 | 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 Eco2AI
Built for Python ML practitioners who want emissions logged per experiment with a decorator or explicit start and stop calls, writing a local session record with project, duration, power, hardware and country. The trade-off is modelling scope: the CO2 figure combines measured CPU and GPU power with a regional emission coefficient, so it reflects that coefficient and local power draw rather than metered grid supply, and the log stays local, leaving aggregation and reporting to you. It tracks runs, not infrastructure. Teams needing fleet-wide or audited accounting should feed its logs into a reporting platform.
Read the Eco2AI listingJudgement is written by the editor. Paid placements never change the ranking or the recorded field values.
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