The Tool Desk
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What should government AI procurement pay for?
It should pay for an AI service that meets a defined need—not treat the volume of model usage as proof of value. Agencies first need to identify the task, establish what acceptable performance looks like, and determine how they will measure it during a pilot and throughout the contract.
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That does not mean every AI contract should use a single outcome-based payment formula. Some outcomes are difficult to attribute to a model, and a poorly chosen metric can reward a proxy rather than public value. The practical goal is to make the mission and acceptance criteria explicit, then choose payment terms that support flexibility, accountability, and cost control.
How token pricing differs from performance-based acquisition
Consumption pricing charges for measured use, such as tokens processed. Performance-based acquisition instead ties requirements, evaluation, or incentives to accepted deliverables, service levels, or defined results. These approaches can coexist: a contract may charge for usage while also imposing quality thresholds, reporting duties, or remedies when service requirements are not met.
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| Procurement question | Consumption-based pricing | Performance-based techniques |
|---|---|---|
| What is the payment unit? | Measured usage, such as tokens. The specific agreement determines rates and any access charges. | Accepted deliverables, service levels, milestones, or incentives tied to specified performance. |
| What does payment establish? | That the contracted usage occurred; it does not by itself establish mission value or quality. | That specified criteria were met, if requirements and verification methods are sound. |
| How can costs be controlled? | Usage limits, consumption reports, and transparent rates; integration and oversight costs also matter. | Milestones, incentives, or service-level controls, alongside oversight and integration costs. |
| How does the arrangement handle change? | It can allow use to vary as needs or models change, subject to contract terms and transition costs. | Requirements and acceptance criteria can be updated through contract mechanisms, but changes need clear governance. |
| What risks need explicit treatment? | Unexpected usage, service interruption, model changes, and costs that do not correspond to useful work. | Whether results can be attributed and verified, and who bears the risk of errors, degradation, or missed criteria. |
The U.S. Government Accountability Office (GAO) recommends that agencies consider performance-based acquisition techniques for AI, including statements of objectives, performance work statements, quality assurance surveillance plans, and contract incentives. It also identifies the need for transparent pricing, knowledge transfer, data and model portability, and clear licensing terms. GAO’s April 2026 report discusses acquisition challenges, but does not establish that performance-based contracts are universally cheaper or better than consumption-based ones. Read GAO’s report on AI acquisitions.
What the current federal token-pricing example shows—and does not show
On September 10, 2026, the General Services Administration (GSA) announced a OneGov agreement offering federal agencies consumption-based access to OpenAI’s ChatGPT. The announced offer is expected to begin October 1, 2026, and run for 27 months. Its stated terms include a 50% discount on token usage, no platform-access fee, no minimum orders, and no spend commitment. These are terms of this agreement, not standard terms across AI procurement.
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GSA also reported approximately $1.68 billion in OneGov savings, of which about $1.4 billion was attributed to AI agreements, and access expanded to roughly 3.5 million federal employees. Those are GSA’s reported figures in its September 2026 release, not independently audited figures in that release. They describe reported savings and access, not evidence that a particular AI service delivered a specified mission outcome. See GSA’s announcement and agreement terms.
The example illustrates why the payment unit and the result should be evaluated separately. No minimum purchase or spend commitment can give agencies flexibility; discounted usage can make access less expensive under the agreement’s terms. But neither condition answers whether a tool improves a public task, how well it performs, or what it costs to integrate, monitor, and govern.
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Start with the mission, then test the service
GSA’s buyer guidance asks agencies to consider, “What tasks or processes would benefit most from AI assistance?” It advises buyers to define the task before selecting technology, test in sandboxes or pilots with small user groups before scaling, understand data flows and protections, coordinate with relevant officials, and monitor costs using limits and consumption reports. Review GSA’s Buy AI guidance.
- Write the mission problem. Describe the process, users, intended benefit, and constraints before naming a model or supplier. Identify a non-AI alternative where that helps establish a fair baseline.
- Set a baseline and acceptance criteria. Decide what will count as acceptable quality, reliability, security, and user impact. Specify how results will be sampled and checked, who evaluates them, and what happens when criteria are missed.
- Pilot with a small group. Test representative tasks and users in a controlled environment. Measure accuracy and reliability as well as time, cost, errors, and operational burden; do not infer production readiness from a demonstration alone.
- Make usage costs visible. For consumption-priced services, require clear rates, consumption reporting, and practical usage limits or alerts. Track related integration and oversight costs so the agency is not judging value from token charges alone.
- Protect the ability to change course. Address data rights, model and data portability, licensing, knowledge transfer, and change or exit plans. Specify access to evidence needed to evaluate performance over time.
- Coordinate before deployment. Involve acquisition, IT, AI, data, security, and privacy officials, and confirm applicable authorization requirements. GSA’s guidance says cloud service providers must be FedRAMP-authorized or in the process of obtaining authorization, and recommends coordination with agency security staff.
- Monitor performance throughout the contract. Models, data, and operating conditions can change. Reassess quality and reliability, check reported usage against mission needs, and use the contract’s quality-assurance and change-management processes to respond.
This checklist reflects GAO recommendations and GSA guidance; it is not a claim that every listed clause is mandatory in every procurement.
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What agencies should watch for in an outcome-based contract
Outcome-based language is only useful when the result is meaningful and verifiable. A contract that rewards an easy-to-count proxy can encourage the wrong behavior, while a target that depends on factors outside the contractor’s control can create disputes rather than accountability.
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- Make evaluation repeatable. Document test cases, sampling, quality thresholds, and the process for resolving disagreements about acceptance.
- Separate model performance from surrounding process effects. Identify dependencies such as staff review, data quality, and workflow changes so that claimed improvements are not attributed to the AI without evidence.
- Plan for model and service changes. Specify how the agency will detect degradation or changes in behavior and what evidence the supplier must provide.
- Preserve competition and government control. GAO identifies data rights, competition, performance monitoring, model reliability over time, and understanding AI-related costs as acquisition challenges. Portability, clear licensing, and knowledge transfer can reduce dependence on a single service and help agencies evaluate alternatives.
A GSA use case is an aim, not a proven result
GSA’s 2025 AI use-case register describes a pre-deployment, experimental retrieval-augmented generation (RAG) assistant intended to coach acquisition staff as they draft outcome-based requirements. The listed goal is feedback comparable in accuracy and reliability to experienced human facilitators. That is an intended target under evaluation, not evidence of successful production results. See GSA’s 2025 AI use-case register.
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The example is relevant because it applies AI to the work of defining better requirements. It also highlights the distinction procurement must preserve: an AI tool can be intended to support outcome-based acquisition without itself having demonstrated that it achieves the intended result.
Why the procurement question matters now
GAO reports that federal agencies more than doubled their use of AI from 2023 to 2024. It also reports that investment in the AI sector grew more than thirteenfold since 2014. The investment figure is background context, not a measure of procurement effectiveness. Together, the figures underline the need for agencies to keep evaluation and acquisition practices aligned with expanding use.
For government buyers, the key decision is not simply whether tokens are a sensible way to meter access. It is whether the contract makes the public task, the evidence of acceptable performance, the cost of operation, and the means of changing or exiting a service clear enough to manage.
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