There is no single “AI price” that captures the cost of a public service. Build a documented, whole-of-life estimate for the service and its alternatives: define scope and time horizon, break the work into auditable cost lines, estimate one-time and recurring costs, show uncertainty, and update the forecast with actuals. A model or API rate is only one input.
Define what the estimate covers
Set the boundary before collecting prices. The estimate should describe a service and its delivery, not an abstract AI model. Record the decisions below so options can be compared on the same basis.
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- Service and outcome: what public-facing or internal service is changing, what it is expected to do, and how the result will be measured.
- Population and demand: who may use or be affected by it, expected case or transaction volumes, peak demand, and any change in demand over time.
- Deployment boundary: which systems, teams, suppliers, locations, data flows, and service stages are included. State whether the estimate covers a pilot, production service, or both.
- Planning horizon: the period used to count setup, annual operations, upgrades, and transition or closure. Use the same horizon for every alternative.
- Baseline and alternatives: document the current service and compare relevant choices such as building, buying, adapting an existing system, or using a shared service.
- Assumptions and exclusions: record demand, staffing, technical, rate, and timing assumptions. Identify existing capacity, sunk costs, shared platforms, and excluded work rather than hiding them in a total.
- Owners: name the person or team responsible for each estimate line and for approving the assumptions.
The U.S. Government Accountability Office’s Cost Estimating and Assessment Guide recommends defining purpose, scope, schedule, technical baseline, work breakdown structure, assumptions, data, methods, risks, and documentation. It is general government cost-estimating guidance, not an AI-specific tariff or formula.
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Use a work breakdown structure—a list of the work and resources needed to deliver and operate the service. The categories below are a checklist, not a claim that every project incurs every cost or that every cost is new cash expenditure.
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| Category | Include where applicable |
|---|---|
| Service definition and procurement | Discovery, options appraisal, procurement, commercial and legal work, supplier due diligence, and contract management. |
| Data | Access or acquisition, rights, collection, cleaning, labeling, migration, quality assurance, storage, governance, and continuing quality monitoring. The UK Balance Sheet Framework groups data costs across strategy, collection and procurement, legal and regulatory, technical, operational, and implicit categories; it is a non-exhaustive taxonomy, not an AI-specific chart of accounts. |
| Technology | Model or software development and licences, inference or other compute, storage, network, environments, security controls, integration, testing, and resilience. |
| People and change | Service and product ownership, domain expertise, engineering, procurement, legal, privacy and security work, frontline review, training, change management, and user support. |
| Operations and assurance | Hosting, service desk, monitoring, evaluation, incident handling, audit, model or data updates, maintenance, and supplier support. |
| Transition and closure | Migration, overlap with a legacy service, contract exit, data export, decommissioning, and replacement, if relevant. |
| Risk and contingency | Material risks and uncertain costs shown as scenarios or ranges with their basis, rather than silently buried in a point estimate. |
UK government AI procurement guidance calls for whole-of-life consideration, including ongoing support and maintenance, and attention to integration, implementation, evaluation, knowledge transfer, training, and hosting arrangements. Its recommendations were published for central government and may be useful to other public bodies; procurement rules and legal obligations still depend on jurisdiction. See the UK AI procurement guidance.
Calculate the estimate across the chosen period
For each ledger line, estimate the quantity of resource needed in each period and multiply it by a documented unit cost. Sum one-time setup and period-by-period recurring costs over the planning horizon. This is a practical application of cost-estimating and whole-of-life guidance, not a government-mandated AI formula.
- Define the unit and quantity. Examples include staff hours, data records processed, model calls, storage, environments, or support coverage. Tie usage quantities to the declared service demand.
- Record the rate. Capture the unit rate, its source and date, and any contract, location, volume, or usage conditions. Local data are needed for rates such as staff time, compute, licence, integration, hosting, and maintenance; the available evidence does not establish a comparable public-service deployment benchmark.
- Separate setup from recurrence. Mark each cost as one-time, recurring, or conditional. Place it in the period when it is expected to occur, including renewals, upgrades, and transition costs.
- Include internal resources transparently. Record staff and existing platform capacity even when no new invoice is expected. Show budget impact separately from allocated resource use and opportunity cost, and state any overhead assumptions.
- Calculate by period and total. For each period, sum quantity × unit cost for each line; then add setup and all recurring-period totals. Keep units and assumptions visible so another reviewer can reproduce the arithmetic.
- Document exclusions and uncertainty. Give uncertain quantities or rates ranges or alternative scenarios. Explain what would make a scenario more or less likely; do not imply precision the inputs cannot support.
A useful ledger includes, for every line, its description, quantity, unit, rate, rate source and date, timing, one-time or recurring status, owner, and assumption. The GAO guide also recommends sensitivity and risk analysis, documenting the estimate, and updating it with actual costs.
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Compare alternatives on equal terms
Compare build, buy, adapt, and shared-service options using the same service scope, demand assumptions, planning horizon, and intended service level. A lower initial price can be outweighed by integration, data preparation, operating support, governance, or exit costs. Show both the initial setup and lifecycle total, with the assumptions behind each.
| Comparison area | Questions for each option |
|---|---|
| Data readiness | Are the data available and usable? What rights, preparation, migration, and ongoing quality work are required? |
| Integration | Which existing systems and service processes must connect to it, and what testing or change work is needed? |
| Hosting and support | Who hosts and operates the service? What continuing support, maintenance, resilience, and supplier arrangements are included? |
| People and governance | What human review, training, monitoring, evaluation, audit, and multidisciplinary capacity will the option require? |
| Dependency and exit | What depends on a particular supplier, platform, or data format? What would migration, data export, or contract exit involve? |
| Demand and rates | How sensitive is the total to volume, model use, staffing, or future rates? Which assumptions drive the difference between options? |
| Outcomes | What service outcome is expected, what baseline will be used, and what evidence will show whether the option achieved it? |
There is no universally lowest-cost or best option established by the available guidance. The appropriate comparison depends on local service requirements, data, architecture, procurement conditions, and support needs.
Keep governance and real operating work in the estimate
Costs do not stop when a system goes live. Include work needed to assign responsibilities, document data provenance and use, retain records of design and deployment decisions, review performance, and handle incidents or changes. The UK Data and AI Ethics Framework, updated 18 December 2025, calls for records that support audit and review, including responsibility, data origin and use, and design and deployment decisions. The staffing and process required to do that work belong in the estimate where applicable.
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UK procurement guidance also recommends multidisciplinary teams, knowledge transfer, training, sustained evaluation, and ongoing communication between buyer and supplier. Treat these as planned work with owners and timing, not as informal tasks assumed to happen at no cost.
Separate projected benefits from proven savings
Keep costs and benefits in separate parts of the business case. A projected productivity gain is not automatically a cash saving for a particular agency: it becomes a budget reduction only if it is measured, attributable, and translated into a change in spending or resources.
The UK Digital and Data Benefits Framework reports an analysis by the Government Digital Service of 200,000 Civil Service job descriptions, estimating potential annual savings of £6.3 billion: £1.1 billion in potential cost reductions and £5.2 billion in productivity gains. Those figures describe potential across a broad task analysis, not measured cash savings or a forecast for an individual project. They should not be used as a deployment cost offset without project-specific evidence. See the UK Digital and Data Benefits Framework.
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Measurement remains uneven. The OECD’s Digital Government Outlook 2026 reports that 10 of 36 OECD countries (28%) report any financial or non-financial impact measurement of AI use cases in government. That is a country-level measure of reported practice, not the share of individual deployments evaluated. It also reports that 21 of 36 countries (58%) provide central support for procuring AI goods and services; this does not establish that a particular agency has such support.
Track actual costs and service outcomes
Assign owners to maintain the ledger after approval. At a regular review interval, reconcile forecast expenditure and resource use against actuals, record changes in service volume or scope, and revise estimates for remaining years. Track service outcomes against the baseline separately, including the method and evidence used to attribute any change. This makes it possible to see whether a cost variance came from demand, rates, implementation, or an assumption that proved wrong.
Do not treat an illustrative public budget as a benchmark. Public Services and Procurement Canada forecast $4,686,281 for AI model training/testing and operations and maintenance in fiscal 2025–2026 for one agency initiative; the briefing indicates that later years need ongoing funding. This is a specific initiative forecast, not a unit price or typical deployment budget. See the Public Services and Procurement Canada 2025 briefing.
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