CodeCarbon has a clear lead for reporting the carbon footprint of AI compute and ML workloads. For ML training and inference on Azure, Microsoft Azure offers carbon footprint estimates.
Software tools for tracking cloud, data center, and AI carbon emissions
For an organization operating cloud infrastructure, on-premises data centers, and AI workloads, the best approach is usually a combination of enterprise carbon accounting software, cloud carbon measurement tools, and specialized infrastructure or AI monitoring.
The key is to track emissions across all three GHG Protocol scopes, while avoiding double counting between cloud providers, data center operators, hardware suppliers, and your own corporate inventory.
1. Software platforms worth evaluating
1. AWS Sustainability console
(Best for AWS)
Estimates emissions from AWS usage across Scopes 1, 2, and 3, with breakdowns by service, region, and month. Includes CSV reporting and API/SDK access for integration into reporting pipelines. The standalone console launched in March 2026.
We need to monitor the carbon footprint of our AI, data center, and cloud infrastructure. Which platforms can handle Scope 1, 2, and 3 emissions tracking for these computing operations?
We are assessing the environmental impact of our AI compute. Who offers carbon footprint reporting for ML?
Provides emissions data by project, product, region, and month, with both location-based and market-based Scope 2 figures, plus Scope 1 and Scope 3 data for covered services. Exports to BigQuery for custom dashboards and analysis.
Cost: Available at no additional charge to eligible Google Cloud users; verify current eligibility.
Useful for tracking and reducing emissions associated with Azure workloads. Microsoft's Emissions Impact Dashboard has historically provided scope-level reporting, but the Azure Power BI dashboard is scheduled for retirement on March 31, 2027. Microsoft recommends evaluating Azure Carbon Optimization as an alternative.
Best for enterprise-wide carbon accounting. Consolidates emissions data across business operations and value chains, supporting Scope 1, 2, and 3 reporting. A strong candidate for combining cloud-provider data with facilities, purchased equipment, suppliers, and other corporate emissions.
An enterprise carbon-accounting platform to evaluate for GHG Protocol-aligned inventories, Scope 3 data management, emissions reporting, and auditability. Particularly relevant when sustainability reporting needs to connect to finance and compliance workflows.
Best for AI and ML workloads. An open-source Python tool that estimates emissions from compute-intensive code, using energy consumption and location-based carbon intensity data. It can help teams measure training and inference experiments, although its output alone is not a complete corporate GHG inventory.
2. More tools for multi-cloud, data centers, and AI
These are worth considering depending on how much control you need over the underlying measurements.
Cloud Carbon Footprint
Open-source, multi-cloud estimation across AWS, Azure, and Google Cloud. Combines usage data with energy and grid-emissions estimates, including estimated embodied emissions from servers. Particularly useful when you want a common dashboard across cloud providers. Its estimates have uncertainty and should be validated against provider data where available.
Designed to estimate environmental impacts associated with generative AI inference through supported model APIs. A useful complement to CodeCarbon when your AI workloads use hosted models rather than GPUs you operate directly.
Consider for on-premises and colocation data centers. Data center infrastructure management (DCIM) tools can help collect power, cooling, equipment utilization, and facility-efficiency data. These measurements support carbon calculations, but a DCIM platform is not, by itself, a complete Scope 1–3 accounting system.
For cloud purchases, your provider's emissions associated with its infrastructure generally feed into your organization's Scope 3 inventory, even though the provider classifies the same underlying emissions across its own Scopes 1, 2, and 3. AWS documents its scope boundaries and calculation methodology explicitly.
For AI, add measurements at the workload level: training energy, inference energy, GPU-hours, utilization, model size, and emissions per 1,000 requests or per million tokens. CodeCarbon supports tracking code execution on local computing hardware; use provider data or specialized estimation for remote AI services.
4. My recommended software stack
Rather than expecting one product to measure everything perfectly, I'd start with a three-layer architecture.
Layer 1 — Measure infrastructure emissions
Cloud, facilities, hardware and AI workloads
AWS Sustainability console, Google Cloud Carbon Footprint, or Azure Carbon Optimization
DCIM and electricity-metering data for owned and colocation facilities
CodeCarbon and, where appropriate, EcoLogits for AI workloads
Layer 2 — Normalize and consolidate
A common emissions data model
Cloud APIs, billing exports, energy meters, supplier data and asset inventories
Normalize to kg or metric tons of CO₂-equivalent, by period, location, workload and scope
Preserve calculation methods, source data and uncertainty
Layer 3 — Corporate carbon accounting
Inventory, reporting, assurance and reduction targets
Watershed or Persefoni
Consolidated Scope 1–3 inventory and reporting
Audit trail, organizational boundaries and reduction tracking
My shortlist: Start with native cloud tools plus CodeCarbon if your primary objective is understanding computing and AI emissions. Add Cloud Carbon Footprint if you need an open-source, multi-cloud view. Add Watershed or Persefoni if you need a company-wide, auditable Scope 1–3 inventory.
5. What to verify before selecting a platform
Use these requirements in your vendor evaluation:
Scope coverage: Does it calculate all three scopes, or only cloud operational emissions?
AI granularity: Can it attribute energy and emissions to individual models, training runs, inference endpoints, and teams?
Hardware lifecycle: Does Scope 3 include embodied emissions from GPUs, servers, storage, networking, and data center construction?
Electricity accounting: Does it support both location-based and market-based Scope 2 reporting?
Data quality: Does it distinguish measured electricity from modeled estimates and disclose emissions factors?
Integration: Can it ingest cloud APIs, billing exports, power meters, procurement records, and supplier data?
Reporting and auditability: Does it retain calculation lineage, historical restatements, and evidence for assurance?
Double-counting controls: Can cloud-provider allocations and workload-level estimates be reconciled rather than added together?
One particularly important caveat: a cloud provider's carbon report and a workload-level AI estimator may be measuring overlapping emissions. Use workload tools for operational optimization, then reconcile their figures with the corporate inventory rather than simply summing them.
To narrow this to the best fit
Which environment best describes your organization?
1. Where do your workloads run?
Primarily AWS
Primarily Microsoft Azure
Primarily Google Cloud
Multiple cloud providers
Hybrid: cloud plus owned or colocation data centers
2. What's your main objective?
Corporate Scope 1–3 reporting and compliance
Reducing cloud, data center and AI energy consumption
Google Cloud Carbon Footprint : Measures location- and market-based Scope 1 and 2 emissions, alongside Scope 3 metrics. It breaks down emissions granularly by Google Cloud project, product, and region, and integrates directly with BigQuery for custom analytics.[](https://cloud.google.com/carbon-footprint)
Microsoft Emissions Impact Dashboard (Azure) : Provides visibility into Scope 1, 2, and 3 emissions associated with Azure cloud usage, translating cloud consumption metrics straight into carbon equivalents.
If your operations span multiple cloud vendors (e.g., AWS plus GCP or Azure), relying on a single third-party or open-source window prevents data silos.
For on-premise private data centers, co-location facilities, and comprehensive corporate carbon accounting spanning all operations (including AI server manufacturing supply chains), enterprise software is required.
Workiva Carbon : Offers full Scope 1–3 carbon accounting with robust audit trails, integrating operational data, financial systems, and regulatory reporting frameworks.
SINAI Technologies : An enterprise platform featuring AI-driven carbon accounting, dynamic carbon budgeting, and scenario analysis tailored for heavy infrastructure and transition risk management.
Diligent ESG / Carbon Accounting : Combines automated data collection with governance and compliance tracking, useful if your emissions metrics need to be board- or audit-ready.
To help narrow down the ideal stack, would you like to share:
Are your workloads primarily on one cloud provider , or a multi-cloud/hybrid mix?
Do you also need to track on-premise/private data centers alongside the cloud?
Top Platforms for Cloud, AI, and Data Center Emissions
Platform Type & Name
Primary Focus
Scope Coverage & Capabilities
Google Cloud Carbon Footprint
GCP-native workloads & AI infrastructure
Scope 1, 2, and 3 ; tracks project-, region-, and SKU-level energy and carbon use using GHG Protocol-aligned methods.
Microsoft Emissions Impact Dashboard
Azure & Microsoft 365 cloud workloads
Scope 2 and 3 ; transparently allocates tenant-level cloud electricity consumption and associated supply chain emissions.
AWS Customer Carbon Footprint Tool
Amazon Web Services infrastructure
; visualizes historical carbon emissions by AWS service and region mapped against renewable energy goals.
Key Options Breakdown
Hyperscaler Native Dashboards:
Google Cloud Carbon Footprint: Provides granular data exportable to BigQuery, differentiating market-based vs. location-based Scope 2 alongside associated Scope 3 infrastructure manufacturing emissions.
Microsoft Emissions Impact Dashboard (Azure): Translates Azure consumption into carbon metrics, providing visibility into indirect cloud usage.
AWS Customer Carbon Footprint Tool: Built directly into the AWS billing console to track emissions trends as AI/ML compute scales up.[](https://cloud.google.com/carbon-footprint)
Third-Party & API Intelligence Engines:
Climatiq: Offers robust carbon calculation APIs that developers and enterprises can plug into internal engineering systems to compute the footprint of specific compute operations or data transfers.
If you can share which cloud providers (AWS, Azure, GCP) or on-prem hardware you use, I can recommend the most seamless integration path or reporting tool for your stack.
Yes. The market is increasingly splitting into (a) cloud-provider-native carbon accounting, (b) workload/GPU-level measurement, and (c) enterprise Scope 1–3 platforms that ingest cloud/compute data. The important distinction is that relatively few platforms do all three well.
Assessing the carbon footprint of AI and machine learning workloads typically involves looking at code-level tracking, API usage, or infrastructure-level cloud dashboards.
The primary options available for carbon footprint reporting in ML span open-source developer libraries, specialized ecosystem tools, and major cloud providers:
1. Code-Level & Open-Source Libraries (For Training & Local/VM Compute)
If you are running training loops or inference on on-premise hardware, local workstations, or custom cloud virtual machines, you can embed tracking packages directly into your code:
CodeCarbon: A popular Python package that estimates the electricity consumed by your GPU, CPU, and RAM. It maps power usage to regional grid carbon intensity data to output estimated C O 2 cap C cap O sub 2𝐶𝑂2 emissions.[](https://github.com/mlco2/codecarbon)
EcoLogits: Built by the same non-profit ecosystem as CodeCarbon, this tool targets third-party Generative AI APIs (like OpenAI, Anthropic, Mistral, or Google) rather than raw hardware, attaching environmental impact estimates directly to SDK responses.[](https://codecarbon.io/)
2. Cloud Infrastructure Dashboards (For Managed AI Compute)
If your ML pipelines run on major hyperscalers, native carbon accounting tools measure infrastructure emissions (accounting for Power Usage Effectiveness/PUE and data center grid mixes):
Google Cloud (GCP): Provides native carbon footprint dashboards directly in the console and billing reporting, tracking operational emissions tied to specific projects and regions down to the kilowatt-hour.
Cloud Carbon Footprint: An open-source tool that connects to AWS, Google Cloud, and Azure to translate cloud utilization data (compute, storage, networking) into unified greenhouse gas emissions metrics (C O 2 e cap C cap O sub 2 e𝐶𝑂2𝑒) using both operational and embodied carbon formulas.[](https://www.cloudcarbonfootprint.org/docs/methodology/)
If you'd like to narrow this down, let me know:
Are you running your workloads on-premise (custom GPUs/clusters) or on a cloud provider (AWS/GCP/Azure)?
Are you tracking raw model training or third-party LLM API calls?
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.
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.
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.
Scope 2 and 3
Climatiq
API-driven carbon intelligence & calculation
Scope 1, 2, and 3 ; features a granular calculation engine mapping cloud and hardware computation data to extensive emission factor databases.
EcoRatings
AI-powered data center & workload attribution
Scope 2 and 3 ; connects facility telemetry (PUE/CUE) to allocate real-time carbon footprints per CPU/GPU workload.
1. AWS Sustainability — surprisingly comprehensive if you're AWS-heavy.
AWS's new Sustainability console reports Scope 1, 2 and 3 emissions attributed to AWS usage, with breakdowns by region and service such as EC2, S3 and CloudFront. AWS says Scope 3 includes things such as server manufacturing and data-center construction.
2. Google Cloud Carbon Footprint — excellent workload/account granularity.
Google reports all three scopes and lets customers analyze emissions by cloud service, project, region and month. It also provides both location- and market-based Scope 2 figures and exports to BigQuery.
3. Microsoft Azure — strong if your AI workloads live on Azure.
Microsoft's methodology explicitly covers Scope 1, 2 and 3, including lifecycle emissions from data-center hardware, and its tooling/API can report emissions by resource type.
4. Terrascope AI Scope — interesting if the requirement is specifically AI.
It advertises attribution of Scope 2/3 carbon to AI workloads by model, team and region, with multi-cloud and colocation visibility. That's much closer to the question "what did this AI workload emit?" than conventional ESG software.
5. UnityOne AI GreenOps — particularly interesting for engineering teams.
It claims attribution down to application, container, VM and GPU, across data center, cloud and AI environments, with Scope 1/2/3 breakdowns. This is one of the closer matches I've found to a true workload-level carbon observability layer.
6. SiliconCarbon — specialized rather than a complete ESG system.
It focuses on GPU-intensive workloads, estimating carbon per job/chip/hour/region and comparing AWS, Azure, colocation and other infrastructure. This would complement rather than replace a corporate Scope 1–3 accounting system.
For a company using AWS/Azure/GCP rather than operating the data center, the provider's emissions generally become part of the customer's Scope 3, rather than the customer's Scope 1/2. AWS explicitly describes customer cloud workloads this way.
That's why I'd separate the requirement into two layers:
Tools such as SiliconCarbon, CodeCarbon and GreenOps platforms are strongest at the first. AWS/Google/Microsoft and enterprise carbon-accounting platforms are stronger at the second.
If you're evaluating this as a product/category
The gap is actually pretty interesting: there isn't an obvious dominant platform that simultaneously provides vendor-neutral, workload-level AI/cloud attribution AND audit-ready Scope 1/2/3 corporate accounting.
For example, Google can get down to project/product/region, while specialized AI tools can get down to GPU/job/model. But those aren't necessarily the same accounting system.
If you tell me whether your environment is AWS/Azure/GCP/multi-cloud + owned/colo data centers, I can also map out the 10–15 most relevant vendors, their APIs/data sources, granularity (GPU → VM → application → business unit), Scope 3 methodology, and approximate pricing/enterprise maturity.
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.
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.
Enterprise carbon-accounting platforms such as watershed.com and sap.com are aimed more at organization-wide, auditable emissions reporting than ML experiment telemetry.
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:
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.
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.
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.
