Comparison
CodeCarbon vs Eco2AI
CodeCarbon vs Eco2AI: Open-source Python library that estimates the carbon emissions of compute. Python library for tracking the CO2 emissions of machine…
| Field | CodeCarbon Open-source Python library that estimates the carbon emissions of compute. | 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 (Code Carbon NGO); install with pip and run locally or on cloud instances. | 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 | Yes | No |
| Last verified | 2026-10-22 | 2026-10-22 |
Editor'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 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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