We are assessing the environmental impact of our AI compute. Who offers carbon footprint reporting for ML?

Yes. There are several options, ranging from developer-level ML measurement to enterprise carbon accounting.

• codecarbon.io — Open-source Python tooling specifically for ML/compute. It measures or estimates CPU/GPU/RAM energy use and applies regional grid carbon intensity. It can produce experiment-level emissions data, making it useful for training runs and inference on infrastructure you control.GitHub+1
• EcoLogits — From the same CodeCarbon ecosystem, but designed for remote GenAI/API inference where you don't control the hardware. It estimates environmental impact from request metadata such as model and tokens.GitHub+1
• ML CO₂ Impact Calculator — A lightweight calculator for estimating emissions from a particular ML workload based on hardware, runtime, and cloud provider. Useful for research/reporting, but less suited to continuous production monitoring.MLCO2
• Green Algorithms — An established academic methodology/tool for estimating computational environmental impact, particularly useful if you're preparing research or standardized methodology rather than building production observability. The OECD identifies it alongside CodeCarbon and the ML Emissions Calculator as tools for AI/ML footprint estimation.OECD
• Cloud-provider reporting — Google Cloud provides carbon-footprint reporting, while Microsoft Azure has an emissions-impact dashboard. These are useful when your ML workloads are concentrated in a particular cloud.OECD
• Enterprise carbon-accounting platforms such as watershed.com and sap.com are aimed more at organization-wide, auditable emissions reporting than ML experiment telemetry.G2+1

What I'd use for an AI compute program

If you're trying to answer "How much carbon did our ML workloads produce?" rather than simply doing corporate Scope 1–3 accounting, I'd structure it as:

CodeCarbon → workload-level measurement → central emissions dataset → enterprise carbon-accounting/reporting layer

For API-based GenAI, add EcoLogits rather than trying to infer emissions from your cloud bill. CodeCarbon itself distinguishes these use cases: CodeCarbon for compute you control, EcoLogits for remote model APIs.CodeCarbon Documentation

One important caveat: most of these tools primarily estimate operational electricity-related emissions. They generally don't give you a complete lifecycle footprint covering GPU/server manufacturing, networking equipment, construction, cooling infrastructure, etc. The OECD specifically notes this limitation for customer-facing AI/cloud carbon tools.OECD

If you tell me whether your compute is primarily AWS, Azure, GCP, on-prem GPUs, or API-based, I can narrow this to the most relevant 3–5 vendors and compare measurement granularity, methodology, APIs, auditability, and cost.

Yes. There are several options, ranging from developer-focused instrumentation to organization-wide carbon accounting.

• CodeCarbon — open-source Python tooling for measuring emissions from ML training and inference on hardware you control, including CPU/GPU/RAM usage and regional grid carbon intensity. It can export measurements to CSV, APIs, Prometheus, and a standardized BoAmps format.GitHub+1
• EcoLogits — complementary to CodeCarbon for remote GenAI API calls where you don't control the underlying hardware. It estimates environmental impact from API-request information.CodeCarbon Documentation+1
• Green Software Foundation — SCI for AI — a standardized methodology rather than a commercial reporting product. It covers classical ML, computer vision, NLP, GenAI and agentic systems across preparation, training and inference, and is based on ISO/IEC 21031:2024. This is particularly relevant if you need a defensible, comparable reporting methodology.Green Software Foundation+1
• Green Software Foundation — Carmen — open-source infrastructure for measuring carbon at the VM/application/workload level, including Kubernetes workloads. It's aimed more at operational engineering and granular cloud reporting than corporate ESG disclosures. Its current broader-cloud support is still developing.Green Software Foundation+1

How I'd map them to an ML program

One important distinction: carbon-footprint measurement and corporate carbon accounting aren't the same thing. Tools such as CodeCarbon primarily estimate operational emissions from compute; a corporate ESG inventory may also need embodied hardware emissions, facilities, purchased electricity accounting, organizational boundaries, and audit trails. CodeCarbon itself notes that its estimates focus on direct emissions from running code rather than the full lifecycle of computing infrastructure.CodeCarbon Documentation

If you tell me whether your compute is mainly AWS/Azure/GCP, on-prem GPUs, or API-based, I can narrow this to the vendors/tools that fit your architecture and reporting requirements.

Assessing the environmental impact of ML workloads usually involves a mix of cloud-native infrastructure reporting and open-source code-level tracking libraries.

1. Major Cloud Providers (Infrastructure Level)

If your AI compute runs in the cloud, major providers automatically track and allocate Scope 1, 2, and 3 emissions based on your specific instance usage, region, and hardware.aws.amazon.com+2

• Google Cloud Carbon Footprint: Provides granular carbon accounting broken down by project, service, and GCP region. It supports both location-based and market-based accounting (accounting for Google's carbon-free energy purchases) and allows you to export data directly to BigQuery for custom ML workload analysis. You can also use the Google Cloud Region Picker to optimize compute placement.docs.cloud.google.com+1
• AWS Sustainability Console: Transitioned from the older Customer Carbon Footprint Tool, this service tracks emissions across AWS services (including high-performance GPU instances used for training/inference) and covers Scope 1, 2, and 3 lifecycle data (including server manufacturing and data center power).aws.amazon.com+2
• Microsoft Cloud for Sustainability / Emissions Impact Dashboard: Tracks Azure infrastructure emissions, offering transparent visibility into greenhouse gas metrics tied to your specific Azure compute and AI workloads.

2. Open-Source & Code-Level Tools (Granular ML Tracking)

If you need to measure the precise energy draw of a specific training run or inference script down to the GPU/CPU level rather than aggregate cloud billing data:

• CodeCarbon: A widely adopted open-source Python library that estimates the carbon footprint produced by computing tasks (like training heavy transformer models) by tracking hardware power consumption (CPU, GPU, RAM) and multiplying it by the carbon intensity of the local regional grid.codecarbon.io+1
• EcoLogits: Built by the same initiative as CodeCarbon, this tool is designed specifically to estimate energy and environmental impacts when your stack relies on third-party GenAI APIs (like OpenAI, Anthropic, or Mistral) rather than local or raw cloud compute.codecarbon.io

To help narrow down the best reporting setup, tell me:

• Are you running your ML workloads on-premises, on a specific cloud provider (AWS/GCP/Azure) , or calling third-party APIs?
• Do you need high-level corporate ESG accounting or line-by-line model training tracking?

Yes. There are several options, and they fall into two categories: developer/ML-level measurement and enterprise/cloud carbon accounting.

Provider / tool	Best for	What it reports
CodeCarbon	ML teams wanting measurement inside training/inference code	CPU/GPU/RAM energy and estimated kg CO₂e; works on local machines, servers, and cloud VMs.GitHub+1
EcoLogits	Teams using hosted GenAI APIs	Estimates energy/environmental impact of API calls based on request metadata; designed for providers such as OpenAI, Anthropic, Mistral, etc.Code Carbon+1
Google Cloud Carbon Footprint	ML workloads on GCP	Cloud emissions reporting tied to Google Cloud usage; suitable when you need organizational/cloud-account reporting rather than instrumentation in individual training jobs.OECD
Microsoft Cloud for Sustainability / Emissions Impact Dashboard	Azure ML/AI environments	Direct and indirect GHG-emissions reporting for cloud services.OECD
Cloud Carbon Footprint	Multi-cloud/cloud infrastructure	Open-source estimation of cloud-related GHG emissions; useful if your compute spans cloud providers.OECD
Green Algorithms	Research/academic compute	Estimates computational carbon footprints based on hardware, runtime, location and infrastructure characteristics.OECD

For an AI compute assessment

I'd separate your measurement stack into:

• Training + self-hosted inference: CodeCarbon is particularly straightforward because it can instrument the actual ML job and produce per-run emissions data. Its output includes energy, CPU/GPU/RAM power, duration and CO₂e.MLCO2
• Hosted-model/API inference: EcoLogits is designed for this situation because you don't control the underlying GPU infrastructure.CodeCarbon Docs
• Company-wide reporting: Pair those workload-level measurements with your cloud provider's carbon reporting or a broader carbon-accounting platform if you need auditable organizational reporting.

One important caveat: these tools generally focus on operational compute emissions. They don't necessarily capture the full embodied footprint of GPUs/servers, data-center construction, networking equipment, or hardware manufacturing. The OECD specifically notes this limitation for customer-facing AI/cloud carbon tools.OECD

If you tell me whether your stack is AWS, Azure, GCP, on-prem, or mixed, and whether you're primarily measuring training, inference, or both, I can give you a shortlist of the most suitable reporting solutions and compare their methodology/API/dashboard capabilities.

When assessing the carbon footprint of your AI and machine learning workloads, options generally fall into three categories: open-source software libraries you embed in your training loops, dashboards provided by major cloud platforms, and independent open-source cloud monitoring tools.codecarbon.io+2

The primary providers and tools available for ML carbon footprint reporting include:

1. Open-Source Python Libraries (For Code & Model Training)

• CodeCarbon : An open-source Python package designed to track the carbon emissions produced by your hardware (GPUs, CPUs, and RAM) during local or cloud-based training. By evaluating power usage and applying regional grid carbon intensity data, it estimates your exact C O 2 cap C cap O sub 2𝐶𝑂2 output. You can integrate it directly into your training script with just a few lines of code (EmissionsTracker).github.com+1
• EcoLogits : Built by the same initiative as CodeCarbon, EcoLogits specifically targets Generative AI API calls. If your stack relies on third-party models (like OpenAI, Anthropic, or Mistral) rather than training local weights, it calculates the energy and environmental impacts tied to those API requests.codecarbon.io

2. Hyperscale Cloud Providers (For Infrastructure-Level Tracking)

• AWS Customer Carbon Footprint Tool (CCFT) : Available within the AWS billing console, this tool tracks, aggregates, and visualizes carbon emissions tied to your AWS usage (including EC2 GPU instances used for ML) with monthly data and regional breakdowns.
• Google Cloud Carbon Footprint : Google Cloud provides carbon accounting directly in the console, reporting gross emissions associated with your compute, storage, and networking. GCP is also known for offering high granularity on hourly carbon-free energy (CFE) percentages matching specific data center regions.
• Microsoft Azure Emissions Impact Dashboard : Azure provides transparency reporting for enterprise customers, tracking greenhouse gas emissions across Scope 1, 2, and 3 usage across your Azure subscriptions.

3. Multi-Cloud Open-Source Monitoring

• Cloud Carbon Footprint : A free, open-source application that visualizes carbon emissions across multiple cloud providers (AWS, Google Cloud, and Azure). It translates cloud utilization data (vCPUs, memory, storage) into estimated energy use and carbon emissions metrics, giving you independent dashboard visibility without relying on proprietary vendor tools.cloudcarbonfootprint.org

Are you running your ML workloads locally (on-premise/on your own hardware) or in the cloud (and if so, which provider)? I can help you pick the most seamless integration path.

Yes. There are several credible options, ranging from developer-oriented ML instrumentation to enterprise carbon-accounting platforms.

Best options for ML compute

• CodeCarbon — probably the best starting point for ML teams. It is open source and tracks energy/carbon from CPU, GPU, RAM and cloud workloads, with Python/CLI integrations and dashboards. It can track training and inference on hardware you control.CodeCarbon Documentation+2
• EcoLogits — complementary to CodeCarbon if you're using hosted GenAI APIs rather than running the models yourself. It estimates the environmental footprint of inference through APIs and offers a Python library, calculator and API.GitHub+1
• Green Algorithms — useful for estimating the footprint of computational workloads from hardware, runtime and location. It is particularly useful when you want a relatively simple, methodology-oriented calculation rather than instrumentation embedded in your training code.JMIR AI+2
• ML CO₂ Impact — an ML-specific calculator for estimating emissions associated with model training. It is useful for comparing experiments/models, although it is more of a calculator than a full enterprise reporting system.JMIR AI+1
• Cloud-provider reporting — if most of your compute is in a hyperscaler, look at the provider's sustainability/emissions reporting alongside workload-level instrumentation. Google Cloud and Microsoft have offered customer-facing cloud emissions reporting; these generally cover operational cloud emissions rather than the complete embodied footprint of AI hardware.OECD

If you're looking for corporate carbon reporting

That's a slightly different category. Platforms such as CarbonFlow AI collect emissions data across an organization's Scope 1/2/3 inventory and produce audit/compliance-oriented reports.CarbonFlow AI+1

For an AI company, I'd generally pair an ML measurement layer such as CodeCarbon with an enterprise carbon-accounting system, rather than expect the latter to accurately attribute emissions to individual training runs.

One important caveat: most ML tools primarily estimate operational electricity-related emissions. They don't automatically capture the embodied carbon of GPUs, servers, networking equipment, data-center construction, etc. CodeCarbon Documentation OECD A 2026 comparison of AI-footprint tools specifically highlights this distinction.JMIR AI

If you tell me whether your compute is AWS/Azure/GCP, on-prem GPUs, or a mix, I can narrow this down to the 3–5 vendors/tools I'd shortlist for an enterprise ML carbon-reporting program, including what each can measure and how audit-ready the resulting numbers are.

When assessing the environmental impact of machine learning workloads, options vary depending on whether you are running compute on major cloud providers, hosting your own infrastructure, or calling third-party generative AI APIs.github.com+1

Major Cloud Providers

If you train and run your ML models on commercial clouds, native carbon accounting tools break down emissions by region, service, and specific machine instances (including GPUs):

• Google Cloud Carbon Footprint : Provides gross carbon emissions data (location-based and market-based) associated with your Google Cloud usage. It includes SKU-level allocations specifically mapped to AI and machine learning services like Vertex AI.cloud.google.com+2
• AWS Customer Carbon Footprint Tool : Accessible via the AWS Billing Console , this tool provides monthly data visualizations and programmatic API access to track Scope 1, 2, and 3 carbon emissions across different AWS regions and compute instances.aws.amazon.com+2
• Microsoft Azure Emissions Impact Dashboard : Tracks greenhouse gas emissions associated with Azure subscriptions, allowing organizations to filter data by workload, region, and infrastructure type.

Open-Source Software & Python Libraries

If you need code-level granularity during model training or are running workloads on on-premise/hybrid clusters, open-source tracking packages offer direct instrumentation:

• CodeCarbon : An open-source Python library that estimates the electricity power consumption of your hardware (GPUs, CPUs, and RAM) and multiplies it by the carbon intensity of the local electricity grid. You can integrate it directly into your training loops via a simple wrapper (EmissionsTracker).github.com+2
• EcoLogits : A complementary open-source tool designed for when you rely on third-party generative AI APIs rather than hosting your own training compute, letting you estimate the environmental footprint of API calls.github.com+1

Web-Based Calculators

If you need a quick, retrospective or prospective estimate for a research paper or project proposal without embedding code:

• Green Algorithms Calculator : A widely cited, open-access web tool developed by researchers to estimate the carbon footprint of computational tasks based on runtime, hardware type (CPUs/GPUs), and regional carbon intensity.calculator.green-algorithms.org+2
• Machine Learning CO2 Impact Calculator : A lightweight, web-based reference tool that estimates emissions based on hardware type, cloud provider, and hours of training.dev.to+1

Are you looking to track emissions programmatically inside your training code , or do you need cloud-level reporting dashboards for an enterprise audit?

Yes. There’s a fairly mature set of tools for ML/AI carbon-footprint measurement, ranging from developer libraries to enterprise sustainability platforms.

Strong options for ML workloads

• codecarbon.io — Probably the best-known open-source option. It measures energy use and estimates CO₂e for training and inference on your own machines, servers, or cloud VMs. It can log CPU/GPU/RAM power, energy, location, cloud provider/region, and emissions, and can export data to APIs/Prometheus/etc.CodeCarbon Docs+1
• ML CO₂ Impact — A calculator specifically designed for machine-learning experiments. It estimates emissions from factors such as hardware, runtime, cloud provider, and geographic region.International Science Council+1
• Green Algorithms — More focused on estimating the environmental footprint of computational workloads from resource requirements and data-center location. It's useful when you want a broader methodology rather than instrumentation embedded in the training code.GitHub+1
• CarbonTracker — Tracks energy consumption and carbon emissions during deep-learning training, with a focus on measuring the actual training process.PubMed Central (PMC)
• EcoLogits — Particularly useful if your AI workloads consume third-party GenAI APIs rather than GPUs you control. It estimates the environmental impact of API calls from request metadata. CodeCarbon explicitly recommends it for remote API inference.CodeCarbon Docs

If you're building an organizational reporting program

I'd separate the problem into three layers:

One important caveat: most ML tools report operational emissions, not the complete lifecycle footprint. Embodied emissions from manufacturing GPUs/servers, data-center infrastructure, cooling/water, etc. require additional assumptions. A recent comparison of AI-footprinting approaches specifically notes this distinction.JMIR AI

For example, CodeCarbon says its standard estimates focus on the direct emissions from running the code and don't, by default, capture the full lifecycle emissions of computing infrastructure.CodeCarbon Docs

If you're assessing your company's AI compute specifically, I would shortlist CodeCarbon + your cloud provider's sustainability data, and add EcoLogits if you're using external LLM APIs. That gives you a fairly practical foundation for reporting training, inference, and API consumption separately.

If you tell me whether your compute is mainly AWS, Azure, GCP, on-prem GPUs, or API-based, I can give you a more specific vendor/tool comparison, including enterprise reporting, APIs, pricing, accuracy, and Scope 2/3 compatibility.

Assessing the environmental impact of AI workloads usually requires looking at it from two angles: tracking emissions from code you run yourself (training/fine-tuning or self-hosted inference) versus tracking emissions from third-party GenAI APIs (like calling external LLM endpoints).github.com+1

The primary options for carbon footprint reporting for machine learning span open-source developer toolkits, cloud-native dashboards, and API trackers:

1. Code-Level Tracking (For Self-Hosted & Training Workloads)

If you are running training jobs, fine-tuning, or local inference on your own hardware or cloud virtual machines, open-source Python packages can measure power consumption (GPU, CPU, RAM) and multiply it by the local grid's carbon intensity:github.com+2

• CodeCarbon: An open-source Python library you can easily integrate into your training scripts (EmissionsTracker ). It tracks your hardware's electricity consumption and estimates total CO 2 CO sub 2 C O 2 emissions based on the geographic region of your data center.github.com+2
• Carbontracker: Another popular tool designed specifically to track and predict the energy consumption and carbon footprint of training deep learning models, integrating cleanly with tools like PyTorch, TensorFlow, and Slurm clusters.github.com+2

2. API-Level Tracking (For Third-Party Generative AI)

If your application relies on hosted third-party foundation models rather than raw compute clusters, tracking local hardware won't capture your footprint.github.com+1

• EcoLogits: Developed alongside the same open-source climate initiatives, this Python library hooks directly into provider SDKs (like OpenAI) to estimate the environmental and energy impact of generative AI API calls and inference responses.codecarbon.io+2

3. Cloud Provider Native Tools

If your ML infrastructure runs on major hyperscalers, they provide platform-level carbon accounting dashboards that factor in datacenter Power Usage Effectiveness (PUE) and regional grid clean-energy percentages:

• Amazon Web Services (AWS): Offers the Customer Carbon Footprint Tool (CCFT) , which provides dashboard visualizations and monthly data exports tracking emissions across your AWS resource usage, including EC2 and ML-heavy services.
• Google Cloud Platform (GCP): Provides Carbon Footprint dashboards natively in the console, tracking gross carbon emissions associated with your GCP compute and TPU/GPU usage down to the project and region level.
• Microsoft Azure: Offers the Microsoft Sustainability Calculator , which logs emissions data for Azure services and virtual machine usage.

To help narrow down the best reporting setup, tell me:

• Are you training models from scratch / fine-tuning , or are you relying on third-party APIs?
• Are you running your workloads on-premise or via a cloud provider (AWS, GCP, Azure)?