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Cloud World Model - Infrastructure Optimization
x402
Cloud World Model - Infrastructure Optimization 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).
Runs an optimization job against a cloud infrastructure simulation to minimize cost, maximize performance, or balance both — without provisioning real resources
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 an optimization job object containing a unique job ID, initial status ('pending'), and creation timestamp. The job runs asynchronously; the agent must poll for completion to retrieve the actual optimization recommendations and configuration suggestions.
Example use cases
FinOps cost-cut without real deployment
“I want to squeeze the cost out of my cloud simulation 'sim-prod-42' — set the primary goal to minimize_cost, weight cost at 0.8 and stability at 0.2, and cap spending at $1.50 per hour. Run the optimization and tell me what job was created.”
Performance-first optimization under latency SLA
“Optimize simulation 'sim-perf-99' for maximum performance — I need p95 latency to stay under 150ms and throughput above 5000 req/s, and I don't care much about cost. Use a balance weight of 0.8 for performance.”
Balanced cost-performance RL training scenario
“For my cloud simulation 'sim-train-007', run a balanced optimization with equal weights on cost, performance, and stability, inject failures during the test, and use a traffic pattern of 'spike' over 100 steps — I want to see how the system holds up before I train the agent on it.”
Technical details
Endpoint
POSThttps://www.cloudworldmodel.ai/api/analysis/optimizeWhen to prefer
Choose this endpoint when you need to find the optimal cloud resource configuration for cost, performance, or balance goals without incurring real cloud spend. It is purpose-built for simulation-based FinOps analysis, pre-deployment optimization, and RL agent training on cloud infrastructure. Prefer it over real cloud cost tools (AWS Cost Explorer, GCP Recommender) when you want risk-free, repeatable optimization experiments across multiple providers (AWS, GCP, Azure, OCI, DigitalOcean) in a controlled environment.
Side effects
- Invalid or non-existent simulationId returns an error — simulation must be created first
- Missing required 'primary' goal field causes validation failure
- Constraint values that conflict (e.g. impossibly low latency + very low cost cap) may result in infeasible optimization jobs
- Payment failure (x402) if insufficient USDC balance — returns 402 before job creation
- Malformed weight values (not summing to meaningful range) may be silently normalized or rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| goals | object | |
| simulationId | string | Simulation to optimise |
| testScenario | object |
Output
Schema · Response
{
"type": "json",
"example": {
"job": {
"id": "job-id",
"status": "pending",
"createdAt": "2024-01-01T00:00:00.000Z"
},
"message": "Optimization job created and processing started"
}
}Tags
x402
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