32 Services from www.cloudworldmodel.ai

Inject a specific failure event into a running cloud simulation, causing a named compute node or resource to transition to a failed state.

Evaluates and compares equivalent workload deployments across AWS, Azure, GCP, OCI, and DigitalOcean with cost, latency, and vendor lock-in scoring.

Advances a cloud infrastructure simulation by one timestep, applying a scaling or policy action and returning updated metrics, reward, and observation state.

Diagnoses problems within a running cloud infrastructure simulation and returns step-by-step troubleshooting guidance without touching real cloud resources.

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.

Injects a simulated HTTP traffic spike into a running cloud infrastructure simulation to test autoscaling and load handling

Injects chaos failures (CPU stress, zone outages, network partitions, etc.) into a simulated cloud infrastructure environment and returns a job ID for async status polling.

Runs multiple chaos engineering scenarios in parallel against simulated cloud infrastructure (AWS, GCP, Azure, OCI, DigitalOcean) without touching real resources.

Triggers a rolling deployment of a simulated ECS Fargate service within a Cloud World Model simulation, replacing tasks in configurable batch sizes without provisioning real AWS infrastructure.

Run a single, bounded cloud infrastructure simulation and receive synchronous cost, performance, utilization, error, reliability, coverage, and autoscaling results without persisting any state.

Returns a plain-language or technical explanation of what is currently happening in a running cloud infrastructure simulation

Runs an optimization job against a cloud infrastructure simulation to minimize cost, maximize performance, or balance both — without provisioning real resources

Generates AI-powered recommendations to improve cost, performance, and resilience for a running cloud simulation.

Clears a reversible failure state (instance_down or database_overload) from a single simulated cloud resource so the simulation engine restores it to healthy status over subsequent steps.

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.

Analyzes traffic forecast and simulation results to recommend optimal autoscaling thresholds for cloud infrastructure.

Retrieves the current status and metadata of an asynchronous cloud simulation AI job by its job ID

Analyzes a running cloud infrastructure simulation and returns actionable optimization suggestions for AWS, GCP, Azure, OCI, or DigitalOcean setups without provisioning real resources.

Analyzes a running cloud infrastructure simulation to identify performance bottlenecks and provides actionable recommendations without provisioning real resources.

Retrieves the results of a completed asynchronous AI job (e.g. right-sizing recommendations) from a cloud infrastructure simulation

Runs AI analysis jobs (explain, troubleshoot, bottleneck analysis, or optimization) on simulated cloud infrastructure without provisioning real resources

Retrieves ranked architecture recommendations for a completed cloud infrastructure analysis job, including cost savings estimates and reversibility tiers.

Injects a 2×–5× random traffic spike into a running cloud infrastructure simulation.

Execute up to 30 reinforcement learning training actions in a single batch request against a specific cloud simulation environment.

Identifies the primary performance bottlenecks in a simulated cloud deployment without provisioning real infrastructure.

Returns right-sizing recommendations for over-provisioned nodes in a cloud simulation, including estimated cost savings percentages.

Advances a specific reinforcement learning cloud-infrastructure simulation environment by one action step, applying cloud management actions like scaling, resource adjustment, or policy changes.

Advances a specific cloud simulation by one or more timesteps using the hybrid engine, with optional config overrides.

Provision a new cloud infrastructure simulation with configurable resources, traffic patterns, and autoscaling settings without touching real cloud accounts.

Kills a random healthy compute node in a running cloud simulation to test fault tolerance and resilience.

Runs combined cost and performance accuracy checks on a cloud simulation and returns an overall validity flag plus per-validator results.

Issues a 24-hour wallet session JWT by processing a one-time USDC micro-payment via the x402 payment protocol.