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corpusAI Cloud Pricing: GPU Committed-Use Term Pricing
x402
corpusAI Cloud Pricing: GPU Committed-Use Term Pricing is a paid API for AI agents from cloud.trycorpus.ai, paid per call via x402, $0.05/call, status unknown (last checked 2026-10-12).
Returns committed-use (reserved/term) rental prices for a specified GPU model across cloud providers and marketplaces, broken down by contract term, region count, and discount percentage.
Price / call$0.05
Success—
StatusUnknown
How this service works
corpusAI Cloud Pricing: Term structure per hyperscaler for one GPU model, USD per GPU-hour: spot, on-demand, 1y, 3y.
Output
A JSON object containing the queried GPU model, unit of pricing, methodology notes, and a breakdown by provider — each with the instance type, GPUs per node, number of regions covered, and an array of term options (e.g. 1-year, 3-year) each showing the median USD price per GPU-hour, low/high range, and discount percentage relative to on-demand.
Example use cases
Optimize GPU reservation budget
“I'm planning to reserve GPUs for our ML training cluster for the next year — can you pull the committed-use pricing for H100 SXM across all major cloud providers and tell me which one offers the best price per GPU-hour on a 1-year term?”
Compare hyperscaler vs neo-cloud GPU terms
“We're deciding between AWS, Azure, GCP, and some neo-clouds for a long-term A100 SXM 80GB reservation — can you look up the term pricing and show me how the discounts and per-hour costs compare across them?”
Evaluate L4 GPU cost for long-term inference workloads
“We're scaling up inference with L4 GPUs and want to commit to reserved capacity — what are the current term-based pricing options and discounts available across providers, broken down by contract length?”
Technical details
Endpoint
GEThttps://cloud.trycorpus.ai/v1/term?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need committed-use or reserved contract pricing specifically for a GPU model — as opposed to spot or on-demand rates. It is ideal for cost modeling of long-term AI/ML infrastructure commitments across hyperscalers (AWS, GCP, Azure) and neo-clouds, with multi-region and multi-term granularity. Prefer this over general cloud pricing APIs when GPU-specific term discounts and contract structures are needed.
Side effects
- Unknown or unnormalized GPU model name returns empty or error response
- GPU model with no committed-use pricing data returns empty providers object
- Malformed query parameter causes 400 bad request
- Payment not settled in USDC via x402 results in 402 Payment Required
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {}
}Tags
x402
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