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

Assessing the environmental impact of AI compute typically involves using a mix of cloud provider dashboards, open-source Python libraries, and specialized carbon accounting platforms.

Options for reporting and tracking the carbon footprint of machine learning workflows fall into three main categories:

• Major Cloud Providers (Infrastructure Level):
  • Google Cloud Carbon Footprint: Provides location- and market-based emissions data derived directly from your GCP usage, broken down by project, product, and region. Google also offers tools to schedule workloads in cleaner energy regions.
  • Microsoft Azure / AWS: Both offer sustainability dashboards and carbon reporting tools (such as the AWS Customer Carbon Footprint Tool) that track infrastructure usage emissions, though granular per-workload AI metrics often require supplementary tracking.
• Open-Source & Code-Level Tools (Model Level):
  • CodeCarbon: A widely adopted Python library developed by partners including Mila and BCG. It hooks directly into your training or inference code to measure power consumption from individual CPUs, GPUs, and RAM, combining it with regional grid carbon intensity data.
  • ML CO 2 CO sub 2𝐂𝐎𝟐 Impact Calculator: A lightweight, web-based estimator where you plug in your hardware type, runtime hours, and cloud region to approximate emissions.
  • EcoLogits / CarbonTracker / Eco2AI: Specialized open-source libraries focused on tracking specific deep-learning training jobs or Generative AI API calls.
• Enterprise Carbon Accounting Platforms (Aggregated / Scope 3):
  • Generate Zero: Connects directly to cloud billing data to dynamically track and audit AI and cloud workloads across multi-cloud environments (AWS, Azure, GCP).
  • CO2 AI (by BCG): An enterprise platform that automates emissions tracking across complex value chains, handling comprehensive Scope 1, 2, and 3 accounting.

If you'd like to narrow this down, tell me:

• Are you primarily running your AI compute on cloud infrastructure (AWS/Azure/GCP) or on-premise hardware?
• Are you looking to track emissions during model training or inference?

I can help recommend the most seamless integration for your team.

Yes. There are several options, ranging from ML-specific instrumentation to cloud-wide carbon accounting:

What I'd choose

For an AI/ML organization, I'd probably use a combination:

• CodeCarbon for per-training-run / per-model / inference measurements.
• Cloud Carbon Footprint or your cloud provider's native reporting for the organization-wide infrastructure footprint.
• Store the ML measurements alongside experiment metadata so you can report things like kgCO₂e per training run, per million inferences, or per model release.

One important distinction: cloud carbon accounting and ML carbon measurement aren't interchangeable. A cloud dashboard can tell you the footprint of a project or service, but it generally won't tell you which training run, model, or experiment caused it. CodeCarbon is much closer to that ML-level granularity.

If you tell me whether you're running AWS, Azure, GCP, on-prem GPUs, or a mix, I can narrow this to the best 3–5 options and compare accuracy, API access, dashboards, GHG Protocol support, and pricing.

When measuring and reporting the carbon footprint of machine learning (ML) workloads, options generally fall into three categories: major cloud provider dashboards, open-source Python tracking packages, and standalone estimation tools.

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

1. Major Cloud Providers (Infrastructure-Level Reporting)

If you run your training or inference jobs on major cloud platforms, built-in enterprise tools track broad Scope 1, Scope 2, and Scope 3 emissions tied to your account usage:

• Google Cloud Carbon Footprint: Provides granular tracking of gross carbon emissions by project, product, and region. It uniquely separates location-based emissions from market-based emissions (accounting for Google's direct renewable energy purchases) and meets GHG Protocol standards.
• AWS Customer Carbon Footprint Tool (CCFT): Found inside the AWS Billing and Sustainability Consoles. It maps infrastructure resource consumption (like EC2 instances and GPU clusters) to regional carbon data, including Scope 1, 2, and 3 supply chain metrics, with API access for programmatic reporting.
• Microsoft Azure Carbon Optimization: Integrated into the Azure portal, this feature visualizes historical trends and carbon data across subscriptions, providing power-usage data and insights aligned with enterprise ESG reporting.

2. Open-Source & Code-Level Tracking Tools

For precise, model-by-model tracking directly inside your Python development and MLOps workflows, open-source libraries monitor active compute resources in real-time:

• CodeCarbon: A widely adopted Python library built by a coalition including Mila and BCG. By wrapping your training or inference scripts with a few lines of code, it tracks CPU, GPU (via NVIDIA NVML), and RAM power consumption, multiplying it by the regional grid's carbon intensity.
• EcoLogits: Created by the same community behind CodeCarbon specifically to track the environmental footprint of remote Generative AI API calls (such as queries to OpenAI, Anthropic, or Mistral).
• Cloud Carbon Footprint: An open-source enterprise tool and dashboard that links directly to your multi-cloud billing data (AWS, GCP, Azure) to estimate energy use and carbon output, even factoring in embodied emissions from hardware manufacturing.

3. Predictive Calculators (Planning-Stage Estimation)

• Machine Learning CO2 Impact Calculator: A lightweight, web-based tool where you can plug in your planned hardware type (e.g., A100 vs. V100), expected runtime hours, and cloud region to estimate anticipated emissions before running heavy experiments.

If you want to narrow this down, let me know:

• Are you running your ML workloads locally/on-premise or via a cloud provider (AWS, GCP, Azure)?
• Are you looking to track training vs. inference?

If your goal is specifically carbon footprint reporting for machine learning and AI workloads, the ecosystem falls into four main categories:

ML-native reporting tools

These integrate directly into ML workflows.

• CodeCarbon – Probably the most widely adopted open-source library. Tracks CPU/GPU energy use during training and estimates CO₂ emissions using regional electricity carbon intensity. Good for experiments and model development.
• CarbonTracker – Estimates energy consumption during model training and can predict future emissions while a run is in progress.
• experiment-impact-tracker – Records energy, hardware utilization, and estimated emissions for ML experiments.
• ML CO2 Impact – A calculator for estimating emissions from training runs when detailed instrumentation isn't available.

Recent reviews continue to identify these as the core tooling used in ML research.

Cloud provider reporting

If most compute runs in managed cloud services, the provider's reporting is often the easiest starting point.

• urlGoogle Cloud Carbon Footprintturn0search0

  • Reports emissions by project, product, and region.
  • Includes both location-based and market-based accounting.
  • Can export data for ESG reporting.

• AWS Customer Carbon Footprint Tool

  • Reports cloud emissions attributable to AWS usage.
  • Useful for enterprise reporting, though historically it has provided less granularity than Google Cloud.

• Microsoft Emissions Impact Dashboard

  • Similar capability for Azure and Microsoft cloud services.

Enterprise sustainability platforms

If AI compute is only one component of a company's emissions inventory, these platforms combine cloud data with broader ESG reporting.

• • Enterprise carbon accounting
  • Cloud integrations
  • Regulatory reporting (CSRD, CDP, etc.)

• • Scope 1–3 accounting
  • Assurance-grade reporting
  • Sustainability management platform

These are designed for corporate disclosures rather than experiment-level ML measurement.

AI-focused monitoring

A newer category focuses specifically on LLMs and inference.

• EcoInfer

  • Real-time monitoring of LLM calls
  • Estimates emissions per request
  • Produces ESG-oriented reports

• AIMeter

  • Research toolkit measuring power, energy, and carbon across AI workloads
  • Produces detailed benchmark reports and visualizations

Which is best?

• For ML researchers: CodeCarbon remains the de facto standard.
• For production AI on public cloud: Use your cloud provider's native carbon reporting, supplemented with workload instrumentation if you need model-level attribution.
• For enterprise ESG: Watershed or Persefoni are among the leading platforms.
• For LLM operations teams: AI-specific monitoring tools like EcoInfer are emerging to provide request-level carbon estimates.

A common enterprise architecture is to combine these layers: instrument experiments with CodeCarbon (or similar), ingest cloud emissions from Google Cloud/AWS/Azure, and consolidate everything into an ESG platform such as Watershed or Persefoni for organizational reporting.

If your goal is specifically carbon footprint reporting for machine learning and AI workloads, there are several categories of solutions, depending on how much detail you need (cloud-level reporting vs. experiment-level tracking vs. enterprise ESG).

Several specialized tools and major cloud providers offer carbon footprint reporting specifically designed for machine learning (ML) training and inference. These tools help track energy usage (kWh) and convert it into C O 2 cap C cap O sub 2𝐶𝑂2 equivalent emissions, taking into account hardware, runtime, and grid intensity.

Open-Source & Specialized Tracking Tools

• CodeCarbon: A widely used Python library that estimates the carbon footprint of compute-intensive tasks, such as training models, by tracking energy consumption of hardware components (CPU, GPU).
• ML CO2 Impact Calculator: A popular tool that allows you to input your training details (region, hardware, time) to estimate the C O 2 e cap C cap O sub 2 e𝐶𝑂2𝑒 produced.
• Eco2AI: An open-source library that allows you to evaluate the energy consumption of your code and corresponding C O 2 cap C cap O sub 2𝐶𝑂2 emissions, optimized for tracking with high measurement frequency.
• Cloud Carbon Footprint: An open-source tool that analyzes energy usage and emissions based on actual cloud usage data.
• Carbonlog / CNaught: A plugin for tracking energy/emissions of coding sessions, including inference time.

Cloud Provider Sustainability Dashboards
Cloud providers publish PUE (Power Usage Effectiveness) and carbon data for their regions, allowing for more precise reporting of Scope 3 emissions.

• Google Cloud Carbon Footprint: Offers insights into the carbon footprint of Google Cloud usage, including specific metrics for AI inference (energy, water, CO2e).
• Microsoft Azure Emissions Impact Dashboard: Provides insights into emissions associated with Azure services.
• AWS Customer Carbon Footprint Tool: Enables users to track, review, and forecast emissions from AWS usage.

Key Considerations for Measurement

• Operational vs. Embodied Carbon: Tools usually track operational emissions (electricity use), but a full assessment should include embodied carbon (manufacturing hardware and building data centers).
• Regional Grid Intensity: The carbon impact varies significantly based on where the computing happens, with cleaner grids (like hydro-heavy regions) creating fewer emissions.
• Efficient Hardware: Using specialized chips like TPUs can be more efficient than general-purpose GPUs.

If you can tell me:

• Which cloud provider(s) you primarily use (e.g., AWS, GCP, Azure, or on-premise)
• What stage of ML you are primarily monitoring (training, fine-tuning, or inference)

I can recommend the most effective tool or reporting dashboard for your specific situation.

Several tools, open-source libraries, and cloud providers offer carbon footprint reporting specifically for machine learning (ML) training and inference. These tools generally calculate emissions based on hardware usage, training duration, and the energy grid intensity.

Here are the key providers and tools as of 2026:

1. Open-Source Libraries (Code-Integrated)

• CodeCarbon: A widely used Python library that estimates CO2 emissions from computational workloads (code/models). It tracks energy usage from CPUs and GPUs and provides data on carbon intensity based on geographical location.
• ML CO2 Impact Calculator: A tool developed to help researchers estimate and report the carbon emissions of their ML models, including hardware specs and cloud provider regions.
• Eco2AI: Similar to CodeCarbon, this library is specifically aimed at tracking energy consumption and carbon emissions for AI projects.
• Experiment Impact Tracker: Tracks energy consumption, GPU usage, and carbon emissions for machine learning experiments.
• Green Algorithms: A calculator designed to estimate the carbon footprint of computational jobs.

2. Cloud Service Provider (CSP) Sustainability Tools

• Google Cloud Carbon Footprint: Provides reporting on emissions associated with Google Cloud usage, including AI and ML infrastructure.
• AWS Customer Carbon Footprint Tool: Offers metrics to help organizations measure and report on the emissions impact of their AWS usage.
• Microsoft Azure Emissions Impact Dashboard: Tracks carbon emissions associated with Azure services.

3. Enterprise Sustainability Platforms

• CO2 AI: A platform for collecting emissions data from suppliers, calculating Product Carbon Footprints (PCFs), and providing AI-powered sustainability management.

Key Considerations for Measurement

• Factors: Carbon footprint is calculated using: C o m p u t e H o u r s×P o w e r(k W)×P U E×G r i d I n t e n s i t y.
• Scope: Look for tools that account for both operational emissions (electricity usage) and embodied emissions (hardware manufacturing).
• Data Center PUE: Cloud providers offer Power Usage Effectiveness (PUE) metrics, which are crucial for accurate reporting.

To better assist you, I can help you:

• Select the right tool for your specific cloud provider (AWS, GCP, or Azure).
• Understand the difference between training and inference reporting.
• Identify strategies to lower your AI's carbon footprint.

Let me know if you are focusing on model training or inference!

If your goal is specifically to understand the carbon footprint of machine learning training and inference, the ecosystem falls into three categories:

ML-specific reporting tools

These are designed for researchers and ML engineering teams.

• CodeCarbon

  • Automatically estimates energy use and CO₂ emissions during model training.
  • Integrates with Python, PyTorch, TensorFlow, Hugging Face, and notebooks.
  • Produces experiment-level reports suitable for research papers or internal sustainability dashboards.

• CarbonTracker

  • Tracks GPU/CPU energy usage while training.
  • Predicts expected emissions before long runs finish.
  • Useful for optimizing training efficiency.

• experiment-impact-tracker

  • Records energy, hardware utilization, location, and estimated emissions.
  • Generates structured logs for reproducibility.

These remain the most commonly cited academic tools for ML carbon reporting.

Cloud-native reporting

If most AI compute runs in the cloud, provider tools can report infrastructure emissions.

• AWS Customer Carbon Footprint Tool

  • Estimates emissions associated with AWS usage.
  • Useful when training on EC2, SageMaker, or EKS.

• Google Cloud Carbon Footprint

  • Reports emissions by project and region.
  • Includes more granular regional information than some competing offerings.

• Microsoft Emissions Impact Dashboard

  • Tracks Azure-related emissions for enterprise customers.

These tools generally report cloud infrastructure emissions rather than model-level emissions.

Enterprise sustainability platforms

If you're producing ESG reports or tracking enterprise emissions, these vendors integrate cloud usage alongside broader Scope 1–3 accounting.

Examples include:

• Watershed
• Persefoni
• SINAI
• Unravel Carbon

These platforms typically provide:

• Scope 1, 2, and 3 accounting
• Cloud emissions ingestion
• Audit-ready reporting
• Reduction planning and scenario analysis

What many AI teams do

A common approach is to combine multiple layers:

1. CodeCarbon (or similar) for experiment-level emissions.
2. Cloud provider carbon reporting for infrastructure totals.
3. An enterprise carbon platform for corporate ESG reporting.

This provides both engineering visibility ("this model training emitted 42 kg CO₂e") and executive reporting ("AI infrastructure accounted for X% of company emissions").

If you're evaluating vendors, useful comparison criteria include:

• Experiment-level vs. infrastructure-level measurement
• GPU and accelerator support
• Multi-cloud compatibility
• Real-time vs. estimated emissions
• Regional electricity carbon intensity
• Scope 3 support (hardware embodied emissions)
• APIs for integrating with ML pipelines (MLflow, Kubeflow, Airflow, etc.)
• Auditability for ESG reporting

If your goal is specifically measuring and reporting the carbon footprint of machine learning workloads (training and inference), the landscape falls into three categories:

ML-specific reporting tools

• CodeCarbon

  • Widely used in research and production.
  • Integrates with Python ML workflows.
  • Estimates CO₂ from GPU/CPU utilization and regional electricity mix.
  • Can generate experiment-level carbon reports.
  • Often considered the de facto open-source standard.

• CarbonTracker

  • Tracks energy consumption during training.
  • Helps estimate both energy use and associated emissions.
  • Supports optimization by identifying unnecessary training time.

• MLCO2 Impact

  • Web-based estimator.
  • Useful before running experiments to estimate expected emissions.
  • Frequently referenced in academic reporting.

• EcoInfer

  • Focused specifically on LLM inference.
  • Provides real-time monitoring, optimization recommendations, and ESG-oriented reporting for API-based AI workloads.

Cloud-native reporting

If most of your ML runs on a hyperscaler, their native reporting is often the most accurate for infrastructure emissions.

• Google Cloud offers Carbon Footprint, with project-, region-, and service-level emissions reporting suitable for cloud-hosted ML workloads.
• Amazon Web Services provides the Customer Carbon Footprint Tool and sustainability reporting for AWS usage, including ML services such as SageMaker.
• Microsoft provides sustainability reporting through its cloud sustainability offerings for Azure workloads.

Enterprise ESG platforms

If AI compute is only one component of a broader corporate carbon inventory:

• Climatiq supplies emissions-factor APIs that many organizations embed into internal reporting systems.
• SINAI provides enterprise-grade Scope 1–3 accounting and audit-ready reporting.
• Watershed and Persefoni are also commonly used for enterprise carbon accounting, though AI-specific workload tracking is generally integrated rather than their primary focus.

Emerging AI-specific reporting

A newer class of vendors focuses on AI usage itself rather than general cloud infrastructure:

• GreenGate tracks emissions from AI API calls (e.g. OpenAI, Anthropic, Google AI) and produces CSRD-oriented reports.
• AIrth estimates the carbon, energy, and water footprint of AI assistant usage across multiple services.

Recommendation by use case

• Research lab or model developers: CodeCarbon + CarbonTracker.
• Training on Google Cloud: Google Cloud Carbon Footprint plus CodeCarbon for experiment-level attribution.
• Training on AWS: AWS Customer Carbon Footprint Tool plus CodeCarbon.
• LLM inference at scale: EcoInfer or GreenGate.
• Corporate ESG reporting: Climatiq, SINAI, Watershed, or Persefoni, supplemented with workload-level ML measurements.

A growing trend is to combine workload-level telemetry (energy used by individual ML jobs) with cloud-provider emissions data (carbon intensity by region and electricity mix). This produces more defensible carbon accounting than relying on generalized emissions estimates alone.

Several cloud providers and open-source tools offer carbon footprint reporting and estimation for machine learning (ML) training and inference. As of 2026, the focus has shifted toward measuring both training emissions and operational, per-query inference emissions.

Top Tools & Platforms for AI Carbon Reporting

• ML CO2 Impact: A popular, open-source tool designed to estimate carbon emissions for ML models. It allows you to compute GPU carbon emissions, include results in publications, and integrate estimations directly into your Python workflow.
• Cloud Provider Dashboards: Major providers now provide sustainability dashboards to help measure emissions:
  • Google Cloud: Provides tools to measure energy, emissions, and water usage for AI inference.
  • Microsoft Azure & Amazon Web Services (AWS): These providers offer tools to track and manage emissions from cloud resources.
• Specialized Calculators:
  • Seedling Earth: An AI carbon footprint calculator for businesses, focusing on Scope 3 emissions (indirect emissions from AI tool usage).
  • AI Footprints: An app for calculating the environmental impact of AI tasks, including text, image, and code generation.

Key Data to Track

To accurately report your AI's carbon impact, you should focus on these metrics:

• Compute Hours & Type: The amount of time and type of GPUs/TPUs used.
• Power Usage Effectiveness (PUE): The efficiency of the data center.
• Grid Carbon Intensity: The cleanliness of the energy grid where your compute is located.
• Water Usage: The volume of water used for cooling the servers.

Best Practices for Mitigation

• Choose Low-Carbon Regions: Run training jobs in cloud regions with high renewable energy usage.
• Select Efficient Models: Match model complexity to the task to reduce power consumption.
• Consider Model Distillation: Use smaller, distilled models for inference to reduce the carbon cost per query.

To help you get the best tool, could you tell me:

• Are you primarily tracking training or inference (API usage) emissions?
• Do you primarily use cloud (AWS, GCP, Azure) or on-premise hardware?