The Tool Desk
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Start with the mission, not a model or a GPU request
State who needs what outcome, which process the AI system is meant to improve, how success will be measured, and who remains accountable for its use. A request for a particular model or accelerator is not a mission definition.
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Distinguish the system’s role before estimating infrastructure. A pilot, an internal productivity aid, a decision-support tool, and an operational system can have very different availability, oversight, and failure-consequence requirements. Those differences should shape the design and the level of operational support.
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Translate the task into workload requirements
Document the demands the infrastructure must meet. These are engineering prompts for applying federal guidance, not a checklist prescribed verbatim by the cited government sources.
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- Work performed: inference, fine-tuning, model training, retrieval, or batch processing.
- Traffic and data: expected request volume, concurrency, input and output types, and context or data size.
- Service needs: latency, peak demand, uptime, geographic reach, and resilience requirements.
- Operating conditions: whether the system must work with limited connectivity or in a disconnected environment, and how it will be monitored, evaluated, updated, and retired.
Estimate baseline and peak demand separately. A workload that is intermittent may not justify the same fixed capacity as a continuously busy one; a disconnected operational environment may impose constraints that a centrally hosted service cannot meet. These are workload-specific trade-offs, not universal federal rules.
Treat data as part of the infrastructure
Before choosing compute, identify the authoritative datasets, their owners, the agency’s access rights, restrictions on use, and the paths by which data will flow into and out of the system. Check quality, representativeness, maintenance needs, and whether information can lawfully be shared internally, obtained from a third party, or drawn from public sources.
OMB Memorandum M-24-10, dated March 28, 2024, calls on agencies to build capacity to share, curate, and govern data used for AI training, testing, and operation. It emphasizes quality, representativeness, bias, collection, curation, labeling, and stewardship. It states: “Any data used to help develop, test, or maintain AI applications, regardless of source, should be assessed for quality, representativeness, and bias.”
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Fund data stewardship and documentation as part of delivery. Treating them as cleanup after model or infrastructure selection can leave the agency with compute it cannot use responsibly or effectively.
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Compare deployment patterns against the workload
Compare agency-managed infrastructure, shared government capacity, and commercial cloud or managed services using the same workload requirements. The following table describes questions each pattern raises; it does not rank providers or establish that one location is generally best.
| Pattern | Questions to resolve | Relevant trade-offs |
|---|---|---|
| Agency-managed infrastructure | Can the agency support the required security authorization, staffing, updates, facilities, monitoring, and lifecycle management? | Assess control, latency, utilization, resilience, energy and facilities dependencies, and the operating burden of owning and maintaining capacity. |
| Shared government capacity | Is suitable capacity available to the agency, and do its access, authorization, connectivity, service, and scheduling arrangements fit this workload? | Assess the fit of shared capacity against the workload’s demand pattern, service needs, and operational constraints; availability and terms are agency- and service-specific. |
| Commercial cloud or managed services | Can the service meet agency security and privacy needs, contract requirements, data-access rules, and performance demands? | Assess service levels, monitoring and asset visibility, portability, licensing, data egress, surge capacity, and the effort needed to oversee the provider. |
Across the options, weigh mission fitness; data access and governance; latency and throughput; baseline and peak utilization; security and authorization; resilience and disconnected operation; portability and licensing; contract terms; staffing; lifecycle cost; energy and facilities dependencies; and the ability to monitor, evaluate, and retire the system. Give the greatest weight to the constraints that matter for the specific workload rather than assigning a universal winner.
The reviewed federal sources do not establish a general ranking of cloud providers, current provider prices, or a universally appropriate server model. Agency-specific classified workloads, legal restrictions, and operating environments require agency review.
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Plan for authorization and continuous monitoring before deployment, not as a final gate after infrastructure is selected. Include access control, security updates, incident response, changes to models and data, human review where appropriate, and retirement arrangements in the operating design.
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OMB M-24-10 advises agencies to update authorization and monitoring processes for AI and to establish safeguards and oversight for generative AI. The particular authorization path and controls depend on the agency, system, data, and operating environment; government-wide guidance does not make those determinations for every deployment.
GAO’s 2025 review found that officials cited policy compliance, limited technical resources, and budget constraints among challenges to generative-AI adoption. In interviews with officials from 12 selected agencies, 10 said existing federal policy, such as data privacy policy, could present obstacles. That finding describes the officials GAO interviewed, not a measure of every agency or every AI system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make cloud and managed-service contracts measurable
For cloud and managed services, translate operational needs into testable contract terms. GAO identified gaps in agency guidance on Cloud Smart procurement requirements and recommended sharing examples of cloud service-level agreements and contract language. Contract and acquisition teams should address:
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- Availability commitments and how performance will be measured.
- Security monitoring, privacy obligations, logging, and continuous visibility of high-value assets.
- Data access and egress, subcontractors, incident reporting, and notification responsibilities.
- Transition and exit arrangements, including how the agency will maintain continuity and retrieve its data.
A service-level promise is useful only if the contract defines the measure, reporting, and consequences in terms the agency can verify.
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Fund the people and lifecycle, not just the compute
Budget for systems engineering, data engineering, cybersecurity, product ownership, user support, evaluation, and acquisition expertise, alongside infrastructure. GAO’s 2025 interviews also identified limited technical resources and budgets, policy compliance, and the challenge of keeping appropriate-use policies current as adoption barriers.
Plan who will operate the service, handle incidents, evaluate changes, support users, and decide when to renew or retire it. A procurement that funds initial capacity but not these responsibilities leaves essential parts of the system unsupported.
Keep large-scale data-center policy in its proper scope
The federal infrastructure policy context is broader than an agency’s deployment choice. The White House’s July 2025 America’s AI Action Plan discusses chips, data centers, energy, and grid capacity, including recommendations about permitting, potential federal land use for data centers and power generation, and infrastructure supply-chain security. These are policy-plan statements and recommendations, not a universal agency architecture requirement.
Executive Order 14318, issued July 23, 2025, defines a “Data Center Project” as a facility requiring more than 100 megawatts (MW) of new load dedicated to AI inference, training, simulation, or synthetic-data generation. The order’s covered components include energy infrastructure, semiconductors, networking equipment, and data storage. Its definition and qualifying-project criteria address large infrastructure projects; the 100-MW threshold is not a test of whether an ordinary agency AI use case is worthwhile or a recommendation for agency compute capacity.
Read federal adoption figures within their scope
GAO-25-107653 reported that, across 11 selected agencies with inventories, reported AI use cases rose from 571 in 2023 to 1,110 in 2024. Generative-AI use cases in that same selected-agency group rose from 32 to 282 over those years. These are figures for GAO’s stated sample, not a census of all federal AI activity.
GAO-25-107933 identified 94 government-wide or government-impacting AI requirements and 10 executive-branch oversight or advisory groups with a role in federal AI. GAO’s September 2025 report said agencies had a requirement to develop and publicly release an AI strategy by September 30, 2025; the evidence here does not establish the implementation status of every agency-specific deliverable.
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