Dashboard
One view of tokens, cost and carbon across your providers, projects and coding agents, with CSV and JSON export.
Most teams guess their AI carbon footprint from cloud spend. CrbonFree meters every call, per model and per provider, with published factors and an uncertainty band you can defend.
Tokens · 365 days
33.4B
365 days to 18 Sep 2026
CO₂e · 365 days
2.6 t
lifecycle · methodology v1.2
Providers
9
connected
Usage by provider
Usage over time
Oct 2025 to Aug 2026
A sample account on live data, open to explore without a login
Every major AI provider and gateway, metered the same way
Start with the provider you use most. Connect it with a read-only key, an SDK, the MCP server, the CLI or the browser extension, and the first tokens land in seconds.
Dashboard, SDKs, MCP, CLI and API share one contract, so a token counted anywhere lands in the same report.
One view of tokens, cost and carbon across your providers, projects and coding agents, with CSV and JSON export.
A typed client for your usage, emissions, receipts and audit packs from Node.js or TypeScript.
npm install @crbonfree/sdkSync and async clients for your usage, emissions and receipts from any Python service.
pip install crbonfreeSeven read-only tools, so your agent can answer questions about your footprint in plain language.
npx -y @crbonfree/mcp loginMeter your local AI coding agents automatically, synced to the dashboard every hour.
npm install -g @crbonfree/cli && crbon installEstimates token usage in your AI chat sessions across ChatGPT, Gemini and the Claude apps, including Claude Code. Guest mode keeps everything on your machine, and signing in keeps the history.
Add to Chrome or EdgeTwelve read endpoints for usage, emissions and receipts, with the OpenAPI spec published.
Numbers that hold up to questions
Each receipt lists the credit serial numbers on the American Carbon Registry or the BCarbon Registry.
Anyone can check a receipt at verify.crbonlabs.com.
A monthly zip with a manifest hash you can verify yourself.
Assets to back the claims you publish.
Receipts, audit packs and trust assets are on paid plans.
One gateway routes 379 trillion tokens a month, which works out to 83.7 kt of CO₂e, up 17x in a year, and none of it shows up in anyone’s Scope 3 disclosure. Your own curve has the same shape.
Tokens · monthly
379T
CO₂e · measured
83.7 kt
Growth · YoY
17x
Source: OpenRouter (openrouter.ai/rankings), as of 2026-09-18, fetched 18 sep 2026, 13:22 utc. Carbon computed with methodology v1.2 assuming a 75/25 input to output split.
Tokens routed per month
Oct 2025 to Aug 2026
However your team already runs AI, that is how you connect. Paste a read-only provider key, drop in an SDK, add the MCP server, install the CLI or switch on the browser extension, and the first measured tokens reach your dashboard within seconds.
Free plan · first tokens in seconds
Before you connect anything
It is a measurement with a stated uncertainty. Every token is counted, converted with published factors that carry a version number, and reported with a plus or minus 28.3% band, the IPCC Tier 1 combination of 20% on activity and 20% on the factors, so you know how far to trust the figure.
No. CrbonFree receives usage metadata only, meaning the model, the token counts and the timestamps. Prompt and completion content never reaches us.
Three layers: the energy the model itself used, the data centre around it, and the hardware and training carbon behind it. Each layer is reported on its own, so nothing is blended in silently.
Questions
These are the short answers. The longer ones, with the factors and the arithmetic, are on the methodology page.
Read the methodologyIt depends on the model and the length of the exchange. Under methodology v1.2 a medium tier model spends about 0.3 joules per input token and 0.5 joules per output token on the accelerator, so a typical exchange of a few hundred tokens uses well under a watt hour of active energy before the facility around it is counted. The factors for every tier are published.
See the per-tier factorsJoules become kilowatt hours, then CO₂e through the facility’s power usage effectiveness and the grid’s carbon intensity. Two further layers add host servers and cluster utilisation, then embodied hardware and model training, following Watershed’s open framework.
Yes. Data centres use water for cooling and the power plants behind them use more. The dashboard reports water alongside energy and carbon, derived from the measured energy, so the three numbers always describe the same usage.
They draw different boundaries. Some count only the accelerator, some the whole facility, some the hardware and training as well, and few say which. CrbonFree reports all three layers separately with the factor version on every figure, so two numbers can be compared on the same footing.
AI inference runs in someone else’s data centre, which makes it a purchased service under Scope 3. As disclosure rules tighten and data centres head toward roughly 12% of US electricity by 2028, auditors have started asking for the number, and a spend-based guess does not survive the question.
Yes. The methodology is public, with every factor, formula and source, and the sample account shows a year of real OpenRouter traffic metered through the same pipeline, with no login required.
Open the sample accountTracking usage, energy and carbon is free, and you will not need a card or a sales call.