Key points
- If right-sizing the mix and caching cut a proposed $1.2M annual commit's run rate by 25% before signature, the same discount lands on a $900,000 commit, and $300,000 a year never gets pledged to capacity you would have forfeited.
Measure first, because the vendor's forecast is not yours
The method starts by refusing the vendor's number. A consumption commit is a bet on your future usage, and the vendor writes the odds by forecasting that usage upward and pricing the discount to a level you may never reach. The only honest input is your own measured usage: what you actually consume today, by model or by seat or by action, and the realistic trajectory after the optimizations you already have planned. A pilot beats any projection here, because AI adoption is uneven and the gap between "we turned it on" and "it handles real volume" is exactly where over-commitments are born.
This measurement is what the optimizer tools exist to do, and it is the same first step regardless of provider. Feed in your real usage and the commit gets sized against your demand and a set of realistic bands rather than an aspirational curve. The vendor wants you to size to the optimistic case; the method sizes to the case your usage actually supports, so the discount you earn applies to consumption you will really incur rather than capacity you will forfeit. Start from your meter, not their story, and the rest of the sizing follows.
app.isvcosell.com/tooling
The same first move for any AI vendor: measured usage in, a commitment sized against realistic bands, the overshoot flagged.
THE SAME JOB, TWICE
TODAY, BY HAND
The AI vendor's proposal arrives sized to its own growth forecast: your usage is climbing, commit to where you are going, the bigger the commit the better the rate.
An analyst extrapolates a usage curve in Excel from two months of pilot data and the vendor's optimism.
Nobody right-sizes the model mix or pulls the caching and batching levers first, so the commit gets sized to unoptimized consumption.
The deal signs big, usage lands smaller, and the vendor keeps whatever was pledged and not consumed.
Days of forecasting theater, then a multi-year bet on the vendor's curve
WITH ISVCOSELL
Feed your real measured usage into the optimizer, by model, seat, or action, and let the commit size against your demand and realistic bands rather than an aspirational curve.
Right-size what you run: route the easy work to cheaper models or tiers and reserve the premium for the hard work.
Pull the free levers, cache repeated context and batch non-urgent work, so the run rate you commit against is the optimized one.
Commit in a band your realistic usage burns with margin, and benchmark the rate, because new units have no folk knowledge of a fair price yet.
Hours from meter to a banded, benchmarked commit
What changes: the commit gets sized to your meter instead of their story. If right-sizing the mix and caching cut a proposed $1.2M annual commit's run rate by 25% before signature, the same discount lands on a $900,000 commit, and $300,000 a year never gets pledged to capacity you would have forfeited.
PART TWO
Right-size and pull the free levers before you commit
Before sizing the number, lower it, because a commit built on unoptimized usage is a commit sized to a figure you could have made smaller for free. Two levers apply across AI vendors. The first is right-sizing what you run: not every workload needs the most capable, most expensive model or the top seat tier, and routing the easy work to a cheaper option while reserving the premium for the hard work often moves the bill more than any discount. The second is the structural levers, caching repeated context, batching non-urgent work, that cut the effective price of the same output without a single concession from the vendor.
Pulling these before you commit is the step most buyers skip, and it compounds with the sizing. A team that right-sizes the mix and caches the repeated work arrives at the negotiation with a materially lower run rate, which means a smaller, safer commitment against an optimized number rather than an inflated one. The vendor would prefer you commit big against your raw usage, because that locks in more guaranteed spend; the levers are how you commit right against your optimized usage instead, and they are entirely on your side of the table.
"Every AI vendor sells the same deal: commit to where you are going, not where you are. Every one of them is hoping you never look at your own meter."
PART THREE
Commit in a band, and benchmark the rate
With the run rate optimized, size the commitment in a band rather than at a point. High enough to earn the discount on your realistic usage, low enough that normal growth does not tip you over into the penalty pricing that lurks past most consumption commits, where usage above the commit is billed at full rates that erase the discount. The two errors, committing too little and forfeiting the rate, committing too much and forfeiting the balance, both cost money, and the vendor's forecast reliably points you toward the expensive one. The safe commit is the one your realistic usage burns with margin, letting growth handle the upside.
The last move is to benchmark the rate, which matters more on AI than almost anywhere because the units are new and there is no folk knowledge of a fair price yet. The committed-spend discount you are offered sits in a distribution of what comparable buyers achieved, and benchmarking it is the only way to know whether the deal is strong or merely large. Measured usage, an optimized mix, the free levers pulled, a commitment sized to a band, and a benchmarked rate: five moves, one method, applied to whichever AI vendor is across the table this quarter.
app.isvcosell.com/tooling
The commit sized in a band and benchmarked on rate, so the discount is earned on usage you will burn, not lost to forfeiture.
THE METHOD
One way to size any AI commit
1 Measure your usage. Start from what you actually consume and a pilot, not the vendor's growth forecast. The forecast is priced to make you overshoot.
2 Right-size what you run. Route easy work to cheaper models or seats and reserve the premium for the hard work. The mix moves the bill more than the discount.
3 Pull the free levers. Cache repeated context and batch non-urgent work before committing, cutting the effective price with no concession from the vendor.
4 Commit in a band, benchmarked. Size to a band your realistic usage burns with margin, and benchmark the rate, because new units have no fair price to anchor on.
THE HONEST LIMIT
A method, not a crystal ball
Sizing an AI commit is still forecasting, and AI usage can surprise you, a project cancelled, an adoption curve that never bends, a workload that migrates faster than planned. The method bounds the risk with margin and scenarios; it does not remove it, and on a fast-moving AI spend the sensible commit is a conservative one that leaves room to grow rather than a bet sized to the best case. Some of the uncertainty here is genuinely irreducible.
What the method removes is the vendor's structural advantage, which is a novel unit, a growth story, and a consumption commit combining to produce an oversized deal you cannot easily unwind. Applied consistently, measure, right-size, pull the levers, commit to a band, benchmark, it turns every AI negotiation into the same solvable problem regardless of which provider is selling. The AI vendors have standardized their pitch; standardizing your response is how you stop paying for the growth story and start paying for the usage.
About the author
Fredrik Filipsson, Cofounder, ISVCOSELL
Fredrik has spent more than twenty years in enterprise software, with time at Oracle, IBM, SAP, and Salesforce before moving to the buy side. He structured and priced the kind of large agreements most buyers only see once or twice in a career, which taught him where the leverage sits and how far a vendor will actually move. He started ISVCOSELL to hand that knowledge to every sourcing team.
More posts by Fredrik Connect on LinkedIn →
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