Design AI compute, storage, networking, identity, and recovery boundaries. Document operating assumptions and control owners, with government-cloud requirements
Last reviewed: 2026-10-03
Cloud architecture for AI connects compute, storage, networking, identity, model services, and operational ownership. Map data and permissions across those components before choosing a topology. Record the availability, capacity, cost, and recovery assumptions the design depends on.
Government cloud security and authorization depend on jurisdiction, agency, information, and system scope. As a US example, FedRAMP scope is determined by the agency's use case; a provider offering alone does not settle every system requirement. This broader architecture guide is not a government-compliance certification or a substitute for the responsible authority's review.
GPU scarcity economics, token-metered services, data gravity for retrieval, and a new platform tier (model gateways, vector stores, eval infra), plus cost variance that demands FinOps from day one.
Compare operational capacity, data movement, service availability, cost, and recovery needs for the actual workload. Document the trade-offs rather than assuming one topology is always preferable.