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Cloud World Model – Hybrid Simulation Step
x402
Cloud World Model – Hybrid Simulation Step is a paid API for AI agents from www.cloudworldmodel.ai, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).
Advances a cloud infrastructure simulation by one or more hybrid steps, blending rules-based and ML-driven decisions to model AWS, GCP, Azure, OCI, or DigitalOcean environments without provisioning real resources.
Price / call$0.001
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 list of simulation events, performance metrics (CPU usage %, error rate, P95 latency in ms, cost per hour), updated simulation state (ID and current time), a hybrid result summary (total steps run, blended vs fallback steps, ML confidence average, bottlenecks detected, decisions list), and the hybrid decision mode (rules or ML) with confidence score.
Example use cases
Cloud architecture cost estimation sandbox
“Run 10 hybrid steps on my simulation sim-cost-test and tell me the projected cost per hour and error rate so I can compare this AWS layout against a GCP alternative before I commit to anything real.”
Cloud training exercise for learners
“I'm learning about cloud infrastructure on Canvas Cloud — can you advance simulation sim-learn-42 by 3 steps and explain what the CPU usage and latency P95 results mean for the architecture I set up?”
Pre-deployment stress test automation
“Step through simulation sim-prod-mirror 20 times using the hybrid engine and flag any bottlenecks detected or confidence drops below 0.8 so I can spot weak points before we go live.”
Technical details
Endpoint
POSThttps://www.cloudworldmodel.ai/api/simulations/%7BsimulationId%7D/step-hybridWhen to prefer
Choose this endpoint when you need to advance a cloud infrastructure simulation using a hybrid rules-and-ML engine and want granular per-step metrics (CPU, latency, cost, error rate) without provisioning real cloud resources. Prefer this over the pure RL batch endpoint when you want the hybrid decision mode (blended vs fallback) and ML confidence scoring. Use it over manual provisioning when cost, safety, or learning context requires a risk-free sandbox.
Side effects
- Invalid or unknown simulationId returns a 404 not found error
- Steps value exceeding 500 or below 1 returns a 400 validation error
- Missing or malformed config overrides may cause a 400 bad request
- Payment failure via x402 protocol returns 402 Payment Required, blocking execution
- Simulation in a terminal or errored state may return an error indicating it cannot be stepped further
- ML confidence unusually low may indicate a fallback-only run with degraded hybrid fidelity
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| steps | integer | Number of hybrid steps to run (default 1) |
| config | object | Optional hybrid engine config overrides |
Output
Schema · Response
{
"type": "json",
"example": {
"events": [],
"metrics": {
"cpuUsage": 42.5,
"errorRate": 0.01,
"latencyP95": 120,
"costPerHour": 0.18
},
"simulation": {
"id": "sim-id",
"currentTime": 1
},
"hybridResult": {
"summary": {
"totalSteps": 1,
"blendedSteps": 0,
"fallbackSteps": 0,
"eventsDetected": 0,
"mlConfidenceAvg": 0.9,
"bottlenecksDetected": 0
},
"decisions": [],
"simulationId": "sim-id"
},
"hybridDecision": {
"mode": "rules",
"confidence": 0.9
}
}
}Tags
x402
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