Provisioned AI capacity keeps costing money whether you fully use it or not. To judge whether it’s paying off, measure utilization, attribute consumption to real workloads, and check whether the reason you bought it still holds.
Provisioned AI capacity gives an organization dedicated throughput rather than relying entirely on shared, pay-as-you-go capacity.
On Microsoft Azure, provisioned deployments are billed while they remain deployed. Separately, organizations can buy a reservation that applies a term-based pricing commitment to eligible provisioned throughput.
Those aren’t the same thing. The deployment holds the capacity. The reservation affects how that capacity is billed.
That distinction matters when you evaluate ROI because you can have two different problems:
The first question is straightforward: How much of the capacity are you actually using?
The FinOps Foundation describes underused committed capacity as Idle Allocated Capacity. If a provisioned block consistently runs far below its available throughput, you're paying for capacity that workloads aren't consuming.
Also check whether each reservation still maps to an active provisioned deployment. On Azure, deployments and reservations are managed separately, so a reservation can continue billing after the deployment it was intended to cover is deleted.
That gives you two utilization checks:
High utilization doesn't automatically mean the commitment is a good deal.
Provisioned throughput can be purchased for different reasons:
And provisioned capacity isn't automatically cheaper than pay-as-you-go. FinOps Foundation analysis has shown cases where provisioned throughput costs more per token than standard pricing, depending on the provider, model, and commitment structure.
So the real question is not simply “Are we using it?”
It is “Are we using it for workloads that still justify why we bought it?”
A utilization dashboard can tell you how much provisioned capacity is being used. It can’t tell you which teams or workloads are using it, or whether they still need that capacity.
Connect usage back to:
If one workflow consumes most of the capacity, you need to know whether that workload was part of the original business case or whether usage has drifted somewhere else.
This is where Token Spend & Insights fits. Larridin attributes AI spend and usage to the teams, workflows, and activity generating it. That attribution helps evaluate capacity you already have. It’s not a substitute for capacity planning or deciding which provider commitment to purchase in the first place.
Once the capacity is in place, work through three questions.
Measure typical and peak utilization. If you also have a reservation, make sure the commitment is still being applied to active provisioned capacity.
Attribute consumption to the teams, workflows, applications, and agents generating the demand.
Then compare that usage with the workloads the capacity was originally intended to support.
If the goal was lower cost, compare the effective cost with the pay-as-you-go alternative at your actual utilization.
If the goal was throughput, latency, availability, or data-handling requirements, verify that the workloads consuming the capacity still need those guarantees.
A heavily used reservation can still be a poor fit if the same workloads could run just as well on cheaper shared capacity. Low utilization can also be defensible when the organization is deliberately paying for a performance or availability requirement it actually needs.
The right utilization level depends on why the capacity was purchased, how traffic behaves, and how the provider prices the commitment.
That’s why a single utilization percentage isn't enough to determine ROI.
The useful answer combines:
utilization + attribution + the original business requirement
That gives finance and technology leaders something much more defensible than a dashboard showing that capacity is simply “in use.”
No. Pricing depends on the provider, model, commitment, and utilization pattern. Provisioned capacity may be purchased for cost savings, but organizations also use it for throughput, latency, availability, or other operational requirements.
Provisioned capacity is the deployed throughput available to your workloads. A reservation is generally a separate pricing commitment that can apply to that provisioned capacity. The exact structure varies by provider.
Start with utilization, attribute the consumption to specific teams and workloads, then test whether those workloads still justify the cost or operational guarantees behind the commitment.
Larridin's Token Spend & Insights connects AI spend and usage to the teams, workflows, applications, and agents generating it so organizations can evaluate where committed AI capacity is actually going.