About Cloud World Model
Why Cloud World Model builds pre-provision multi-cloud simulations for infrastructure teams and AI agents, and how it communicates model limitations.
Why we built it
Infrastructure decisions often happen before there is a safe live environment in which to try them. Cloud World Model provides a virtual place to explore cost, performance, scaling and failure scenarios without provisioning production resources. It is built for engineers and agents that need to compare possible next states, not just draw a static diagram. A team can ask how a proposed resource graph behaves under changing traffic, or whether a modeled failure exposes a capacity or recovery concern, before deciding what to validate in a real environment.
Explore the productWhat the platform does
Create a resource graph, advance it under traffic, test a failure, or train an agent using an observe–act–reward loop. The platform exposes these workflows through REST and a hosted MCP server, with documentation for API, SDK and command-line access. Outputs are modeled estimates grounded in documented assumptions, not live-provider commitments. A simulation works with virtual resources; its actions do not create, resize or delete infrastructure in a user's cloud account. The walkthrough and API reference show the actual integration surface.
Start with the docsA multi-cloud model with explicit coverage
Cloud World Model describes modeled services across AWS, GCP, Azure, OCI and DigitalOcean. Coverage is not identical across providers or service families. The provider-coverage reference lists services, size counts, base reference values and whether service behavior is marked measured or extrapolated. That distinction helps readers see when a service has direct benchmark validation and when its behavior is derived from the model. It is a map of stated coverage, not a promise that every resource or workload is represented.
Review provider coverage and evidence labelsEvidence before certainty
A simulator is useful only when its limits are visible. We separate reference-backed pricing evidence from unvalidated performance assumptions and publish accuracy and fidelity detail for scrutiny. The current owned accuracy campaign is tied to a specific AWS reference architecture and workload; it should not be generalized to every provider or application. The separate multi-cloud burst report also describes a particular simulated scenario rather than a universal ranking. We do not present simulation output as a customer deployment result, invoice, service-level objective or guarantee.
Read the accuracy methodologyWho it is for—and what comes next
The product is intended first for AI agents and the people who build them, and also supports infrastructure and platform teams, cloud architects, learners and reinforcement-learning users. It gives these users a repeatable place to form a hypothesis, inspect a modeled outcome, and decide what deserves further investigation. A useful result is a planning input: teams should compare it with their own workload, provider prices and telemetry before making a production change. CWM does not replace monitoring, cloud-provider tools or guarded production experiments.
Explore documented use casesGet in touch
For a first experiment, use the simulation walkthrough or hosted playground. For pricing and account details, consult the pricing page; for integration questions, start with the API docs. The public documentation also links to model coverage, accuracy evidence, guides and the API reference so readers can inspect how a workflow works before using it.
View pricingHow to evaluate a modeled result
Start by checking that the virtual resource types, region and traffic assumptions match the question you want to explore. Read the evidence label for a service and identify whether the relevant behavior is measured, extrapolated or an explicit assumption. Compare alternatives using the same workload, and treat cost and performance as estimates. When the decision matters to a live system, validate against provider prices, real telemetry and appropriately controlled experiments.
Read the model fidelity guide