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Cloud World Model – Validate Simulation
x402
Cloud World Model – Validate Simulation is a paid API for AI agents from www.cloudworldmodel.ai, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-14).
Validates a cloud infrastructure simulation run by submitting a traffic forecast and simulation configuration for asynchronous evaluation against AWS, GCP, Azure, OCI, or DigitalOcean environments.
Price / call$0.005
Success—
StatusUnknown
How this service works
Simulate AWS, GCP, Azure, OCI, and DigitalOcean cloud infrastructure without provisioning real resources. Built for Canvas Cloud AI learners and agents.
Output
Returns a JSON object containing a job with a unique job ID, job type ('validate'), status ('pending'), and creation timestamp, along with a confirmation message that the validation job has been created and processing has started. The agent must poll or wait for job completion separately.
Example use cases
AWS peak traffic stress validation
“Validate simulation sim-aws-456 on Cloud World Model — the traffic forecast averages 300 RPS with spikes up to 1500 RPS at timestamp 1700000000, and I want to run 200 test steps to see if my AWS auto-scaling config holds up.”
GCP capacity planning for product launch
“I'm planning a product launch and need to validate my GCP infrastructure simulation (ID: sim-gcp-789) against a forecast of 800 average RPS and 3000 peak RPS — run 100 test steps and tell me if the setup can hold.”
DigitalOcean architecture validation for RL training
“Run a Cloud World Model validation on simulation sim-do-321 using a traffic forecast named 'baseline-load' with 200 avg RPS, 600 peak RPS, and 50 test steps — I'm training an RL agent and need the environment validated before I start.”
Technical details
Endpoint
POSThttps://www.cloudworldmodel.ai/api/predictions/validateWhen to prefer
Use this endpoint when you need to validate a cloud infrastructure simulation with a specific traffic forecast without provisioning real cloud resources. Prefer this over live cloud testing when cost, safety, or speed of iteration matters — especially for AI/RL agent training loops or educational cloud architecture exercises on Canvas Cloud.
Side effects
- Missing required trafficForecast fields (name or dataPoints) returns a validation error
- Invalid simulationId returns a not-found error
- Malformed dataPoints array (missing rps or timestamp) causes a 400 Bad Request
- Insufficient USDC balance causes a 402 Payment Required error
- Service unavailability may return a 503 with no job created
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| testSteps | number | Simulation steps to run (default 100) |
| simulationId | string | |
| trafficForecast | object |
Output
Schema · Response
{
"type": "json",
"example": {
"job": {
"id": "job-id",
"type": "validate",
"status": "pending",
"createdAt": "2024-01-01T00:00:00.000Z"
},
"message": "Validation job created and processing started"
}
}Tags
x402
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