In 5 years, no serious company will deploy AI agents without carbon accountability.

Track it. Route it. Remove it.

GreenLedger is the carbon accountability layer for AI agents — routing to greener models, budgeting carbon per agent, and offsetting every inference. One SDK. No prompt rewrites.

Live · Global AI CO₂
0g

of CO₂ since tab opened · +23,148 g/sec

0.03–1.14gCO₂e per AI queryUp to 70× more for reasoning
90 TWhAI data center demand 202610× increase from 2022
0real-time AI carbon trackersFor agents. Until now.
161M+ machine-to-machine transactions0.03–1.14g CO₂e per AI query70× energy of lightweight models for reasoning tasks$15T B2B agent spend projected by 202860–90% of AI lifecycle emissions from inferenceGoogle emissions up 50% — driven by AIMicrosoft up 23% · Meta up 60%AI data centers: ~90 TWh/year by 2026161M+ machine-to-machine transactions0.03–1.14g CO₂e per AI query70× energy of lightweight models for reasoning tasks$15T B2B agent spend projected by 202860–90% of AI lifecycle emissions from inferenceGoogle emissions up 50% — driven by AIMicrosoft up 23% · Meta up 60%AI data centers: ~90 TWh/year by 2026

The problem

The infrastructure for agents to spend money exists.

The infrastructure to account for their environmental cost does not.

Stripe, Google, and Coinbase have built the payment rails for autonomous agents. 161 million machine-to-machine transactions have already happened. $15 trillion in B2B spend is projected to flow through agent marketplaces by 2028. Every single transaction has a hidden carbon cost that nobody is measuring.

Google
+50%emissions increase

Driven entirely by AI infrastructure

Meta
+60%emissions increase

AI training and inference workloads

Microsoft
+23%emissions increase

Azure AI + Copilot deployment

Source: Published sustainability reports 2024–2025

The stakes

The path we take depends on removal.

Every tonne of CO₂ that doesn't get removed is a debt on the 1.5°C budget. GreenLedger routes every AI inference levy directly to verified carbon removal via Stripe Climate.

Limit temperature increase to:
Aggressive cuts + large-scale carbon removal
Historical emissions
~1.5°C path
Carbon removal
-20-100102030405019801990200020102020203020402050206020702080209021002020NET CO₂ (GtCO₂/YR)YEAR

Source: Global Carbon Project (historical) · CICERO/IPCC AR6 (pathways) · GreenLedger routes every inference levy to verified carbon removal.

How it works

Every inference. Accounted for.

01

Prompt Sent

Your agent calls GreenLedger instead of the model provider directly. One line change.

02

Router Scores

Complexity classifier evaluates the prompt. Grid carbon intensity checked by region.

03

Model Selected

Cheapest model that meets your quality bar is chosen. Haiku handles ~80% of typical tasks.

04

Response + Receipt

Inference runs. Response returned to your agent alongside a full environmental receipt.

05

Offset + Audit

Micro-levy routed to Stripe Climate. Receipt logged to dashboard. Budget deducted.

Sample Receipt

Requested

opus-4-6

Routed to

haiku-4-5

CO₂e

0.047g

Energy

0.20 Wh

Water

0.36 mL

Savings

80%

Levy

$0.000024 → Stripe Climate

The bet

In 5 years, no serious company will deploy AI agents without carbon accountability.

The same way no serious company deploys software without security today.

WE WANT TO BE THE DEFAULT WAY THAT HAPPENS.
Get started free