They should do it before procurement approval, especially when developers, analysts or agents are likely to generate uneven workloads. Usage-based modelling matters most when a small number of heavy users can dominate cost, because the list price can hide a much larger operating expense.
Why usage-based modelling should come before procurement approval
Usage-based modelling belongs in the buying process, not after it. A seat count can look tidy on paper while hiding the real cost profile of developers, analysts or agents who create spiky, bursty demand. Model the likely consumption pattern first, then test whether the vendor’s commercial model still fits the way work will actually happen.
The practical question is not how many named users exist, but who can generate load, how often they do it, and whether that activity scales predictably or in bursts. A small cohort of heavy users, automation jobs, or agent-driven workflows can dominate the bill even when the organisation believes it is buying for a modest headcount.
This matters most where the service is metered by calls, tasks, compute time, tokens, credits, throughput or other variable units. In those cases, the budget should reflect behaviour and volume, not just access rights. Seat-based budgeting is still useful when usage is stable and evenly distributed, but it becomes a poor proxy when demand varies widely across roles or teams.
Where seat-count budgeting breaks down
Seat-count budgeting assumes that one person equals one roughly similar cost unit. That assumption fails when users differ sharply in intensity, when some functions are automated, or when a single workflow can generate repeated consumption throughout the day. The result is not just forecast error, it is a procurement decision that can understate total cost of ownership from day one.
Uneven workload patterns are the clearest warning sign. If developers are testing frequently, analysts are running large batches, or agents are invoking tools repeatedly, the commercial driver is activity volume rather than population size. The list price may still look attractive, but the operational expense can move far beyond the initial headcount model.
Usage-based modelling also changes governance. It forces teams to define what will be measured, who owns the meter, what thresholds trigger review, and how exceptions are handled when one function creates disproportionate spend. That discipline is useful before contract signature because once adoption begins, commercial behaviour is much harder to unwind.
How to estimate the real cost profile
The simplest useful approach is to model usage by role and by workload type. Break the population into light users, moderate users, heavy users and automated consumers, then estimate expected activity per group. That gives a much better view of cost concentration than a flat seat assumption.
For products that expose APIs, compute credits or agent actions, build scenarios around normal, peak and stress periods. The right budget is often the one that survives the busiest month, not the average month. This is especially important when a small number of power users or autonomous workflows can create most of the spend.
CIS Controls v8 is useful here because procurement should be tied to account inventory, access governance and ongoing monitoring of who is actually using the service. That operational view helps teams separate named-user licensing from the broader cost impact of real consumption.
Risk and Threat Considerations
Usage-based pricing can create governance risk when cost visibility lags behind adoption. A service that looks affordable at purchase time may become expensive after a handful of users, scripts or agents begin generating sustained activity, especially if the commercial model lacks clear caps or alerts.
Failure mechanism: The organisation budgets for access, but the vendor bills for activity, so a small number of intensive users or automated workflows drive an outsized recurring charge.
Impact: Costs can scale faster than adoption, forcing surprise overspend, delayed expansion, emergency rationalisation or a rushed change of platform terms.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
CIS Controls v8 provides the primary governance reference for this topic.
| Framework | Control / Reference | Relevance |
|---|---|---|
| CIS Controls v8 | CIS-5 — Account Management | Usage modelling depends on knowing who can consume the service and how access is assigned. |
| Recommendation — Track active accounts and usage patterns before committing to seat-based pricing. | ||
Practitioner Guidance
What to prioritise: Build the commercial model before approval, not after rollout. The decision point is whether the workload pattern is stable enough for seat pricing or variable enough to require usage modelling.
What to verify: Test the estimate against role-based scenarios, peak-period demand and any automation that can multiply consumption. If a few users can drive most of the volume, treat the usage model as the primary budget input.
Decision rule: If the service is metered by transactions, compute, tokens or actions, and workload intensity is uneven, budget from usage first and seats second. If consumption is genuinely uniform, seat-count budgeting may still be adequate.
Practitioner takeaway: The most reliable budget is the one that follows workload behaviour, not organisational headcount, because cost concentration is usually what breaks procurement assumptions.
Related resources from NHI Mgmt Group
- Should organisations prioritise external exposure or internal credential governance first?
- How can organisations decide whether to move from seat-based to usage-based identity pricing?
- When should organisations prioritise gateway-based model evaluation over vendor benchmark numbers alone?
- When should organisations prioritise real-time bank data over document-based verification?