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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 2026-10-28).
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
Multi-cloud infrastructure simulation for cost, latency, chaos testing, and AI agent training before provisioning.
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/optimize?utm_source=zero.xyzWhen 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"
}Tags
x402
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