Comparison
Eco2AI vs EcoLogits
Eco2AI vs EcoLogits: Python library for tracking the CO2 emissions of machine learning models. Open-source library and API for estimating the…
| Field | Eco2AI Python library for tracking the CO2 emissions of machine learning models. | 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 | Released under the Apache License 2.0 and installable from PyPI. | 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 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 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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