Comparison
CodeCarbon vs EcoLogits
CodeCarbon vs EcoLogits: Open-source Python library that estimates the carbon emissions of compute. Open-source library and API for estimating the…
| Field | CodeCarbon Open-source Python library that estimates the carbon emissions of compute. | 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 (Code Carbon NGO); install with pip and run locally or on cloud instances. | 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 | Yes | Yes |
| 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 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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