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Cloud World Model – Analyze Bottlenecks
x402
Cloud World Model – Analyze Bottlenecks 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).
Analyzes a running cloud infrastructure simulation to identify performance bottlenecks and provides actionable recommendations 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 with a natural-language 'analysis' field describing the primary bottleneck (e.g. database connection pool pressure, p95 latency stats, CPU utilization levels) and a provider-specific 'doRecommendation' suggesting a concrete upgrade or configuration change (e.g. switching to a managed database with read replicas on DigitalOcean) along with estimated performance improvements.
Example use cases
Database tier bottleneck diagnosis
“I'm running a cloud simulation on Cloud World Model with ID sim_xyz789 and my app feels sluggish — can you analyze it for bottlenecks and tell me if it's a database issue?”
Learning cloud architecture tradeoffs
“I'm studying for my cloud certification and want to understand what bottlenecks look like — can you run a beginner-mode bottleneck analysis on my simulation sim_learn42 and explain the results simply?”
Pre-launch infrastructure review
“Before I push my architecture to production, I want to stress-test it in simulation — analyze bottlenecks on sim_prod99 and tell me which tier I should upgrade first and what specific change to make.”
Technical details
Endpoint
POSThttps://www.cloudworldmodel.ai/api/simulations/%7BsimulationId%7D/analyze-bottlenecksWhen to prefer
Choose this endpoint when you have an existing Cloud World Model simulation and need to programmatically identify which infrastructure tier is the performance bottleneck and get a concrete, provider-specific recommendation for improvement — all without incurring real cloud provisioning costs. Prefer this over general cloud cost tools when the goal is latency and throughput diagnosis within a sandboxed simulation, especially useful for learners, pre-production architecture reviews, or AI agents iterating on cloud designs.
Side effects
- simulationId not found or expired — returns 404 with error message
- simulation still initializing — returns 409 conflict or 503
- malformed request body — returns 400 with validation error
- payment not received or insufficient — returns 402 Payment Required
- internal simulation engine error — returns 500
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| beginnerMode | boolean | Return simplified bottleneck analysis |
Output
Schema · Response
{
"type": "json",
"example": {
"analysis": "The primary bottleneck is the database tier: connection pool pressure is at 94% and p95 latency has climbed to 340 ms. The compute nodes are healthy at 55% CPU. Migrating to a managed database with read replicas would reduce latency by an estimated 40%.",
"doRecommendation": "Upgrade to a DigitalOcean Managed Database (db-s-2vcpu-4gb) with one read replica to offload analytics queries and reduce connection pool pressure."
}
}Tags
x402
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