Cloud World Model
What is a cloud world model?
A cloud world model is a computational representation of cloud infrastructure that agents and teams can step through before deploying or changing infrastructure. Cloud World Model (CWM) simulates resources, traffic, failures, and scaling actions across AWS, GCP, Azure, OCI, and DigitalOcean, returning modeled cost, latency, errors, and resilience in a safe sandbox. MCP and REST are access interfaces; x402 is payment for eligible REST requests. Simulations do not provision resources in a user's cloud account.
FinOps calculators and cost estimates
FinOps calculators estimate cloud prices from selected services and assumptions. CWM also models performance and resilience over simulated steps; its cost output is an estimate, not an invoice or a record of actual spend.
Monitoring and operations tools
Monitoring tools report signals from deployed systems, while CWM models hypothetical infrastructure before deployment or change. CWM is not a live monitoring service or operations console.
Simulated chaos and production fault injection
CWM applies failures to a virtual model to explore a hypothesis; production fault-injection tools deliberately affect real systems under safeguards. Simulation can inform a later experiment, but it does not establish production resilience or replace tools such as AWS FIS or Gremlin.
Read the simulated-chaos guide →See the unranked tool comparison →
Before deploying or changing infrastructure
Testing a hypothetical infrastructure configuration before deploying resources or changing an existing system. It can help compare alternatives and expose assumptions, but it is not a guarantee of live cost, latency, or reliability.
Multi-cloud cost and latency simulation
Evaluating modeled deployment strategies across providers using workload assumptions and cost/latency objectives. Inputs such as traffic, region and service mapping matter; a ranked strategy is not an invoice or a production latency measurement.
RL environment and episode
A training interface linking a policy's action to the next modeled state and reward. An episode is a sequence of steps ending under a configured limit or reset. Always validate a trained policy outside the simulation before using it to control real infrastructure.
Try it before you deploy or change infrastructure
Give your agents a world model for infrastructure — run a simulation or connect via MCP before you deploy or change infrastructure.