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Vealth AI Carbon Retire Bundler
x402
Vealth AI Carbon Retire Bundler is a paid API for AI agents from vealth.net, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Calculates the carbon footprint of an AI model inference call and generates a retirement plan for verified carbon credits to offset it
Price / call$0.001
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object with two sections: (1) footprint — the energy consumed in kWh and CO2e emissions in tonnes for the given model inference; (2) retirement_plan — the recommended Regen Network carbon credit class ID, the number of credit units needed to offset the emissions, the estimated cost in USDC, and a direct marketplace URL to complete the retirement.
Example use cases
Offset per-inference AI emissions
“I just ran a batch of GPT-4 completions using about 5000 input tokens and 3000 output tokens — can you calculate the carbon footprint and get me a Regen Network retirement plan to offset it?”
Sustainability audit for AI startup
“We use Claude-3 Sonnet in our product and want to offset our emissions. Run a carbon footprint estimate for a typical call with 800 input tokens and 600 output tokens and show me how much it would cost to retire credits on Regen.”
Automated green AI workflow
“After every LLM call in my pipeline using mistral-7b with roughly 400 input tokens and 200 output tokens, I want to automatically get a carbon credit retirement plan — what's the footprint and which credit class should I retire?”
Technical details
Endpoint
POSThttps://vealth.net/.well-known/vealth/ai-retire-bundlerWhen to prefer
Choose this endpoint when you need a combined carbon footprint calculation AND a ready-to-act retirement plan specifically linked to verified Regen Network carbon credits, all in a single call. It is ideal for AI agents that want to automatically offset LLM inference emissions per-call or in batch, with a direct marketplace link for execution. Prefer it over generic carbon calculators when you need token-level granularity for AI model calls and a Regen Network-compatible offset plan in one step.
Side effects
- Unknown model name may result in fallback estimates or errors
- Zero or negative token counts may cause validation failures
- Payment of 0.001 USDC required per call — missing payment yields 402 response
- Regen Network marketplace URL may be unavailable if the credit class is retired or delisted
- Extremely small token counts may produce cost estimates below minimum purchasable credit units
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| model | string | |
| input_tokens | number | |
| output_tokens | number |
Output
Schema · Response
{
"type": "json",
"example": {
"footprint": {
"kwh": 0.0042,
"co2e_tonnes": 0.0000018
},
"retirement_plan": {
"class_id": "C01",
"credit_units": 0.0000018,
"est_cost_usdc": 0.0001,
"marketplace_url": "https://app.regen.network/credits/C01"
}
}
}Tags
x402
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