Key points
- The model mix is the largest lever in most AI bills: if right-sizing the mix and pulling the caching and batching levers cuts the baseline 30 percent before you sign, a commit that would have been $1M becomes $700K, and the discount you negotiated stays a discount instead of funding capacity you forfeit.
Size the commit from your usage, not their forecast
The central move in any AI purchase is refusing to size the commit from the vendor's growth story. A consumption commit is a bet, and the vendor writes the odds in their own favor by projecting your usage upward and pricing the discount to a number you may never reach. Overshoot the commit and you have prepaid for capacity you will forfeit. The honest input is not their forecast. It is your own usage, measured.
That is what the optimizer tools do first. Feed in what you actually consume, tokens by model, seats by real adoption, and the commit gets sized against your measured demand and a set of realistic bands rather than an aspirational curve. The discount for committing is real and worth having. It is only worth having on a number you will actually use, and the only way to know that number is to start from your own meter.
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The commit sized from your measured usage against realistic bands, not the vendor's upward forecast. The overbuy is the trap.
THE SAME JOB, TWICE
TODAY, BY HAND
The vendor presents a growth slide and asks for a consumption commit sized to where your usage is supposedly going.
The buying team has no per-seat list price to anchor on, so the offered rate is accepted as the market.
Nobody models the model mix, caching, or batching first, so the commit is sized to unoptimized usage that could have been smaller for free.
The deal overshoots into forfeited prepaid capacity, or growth tips past the commit into on-demand penalty pricing.
Days of guessing, priced against the vendor's own forecast
WITH ISVCOSELL
Feed your measured usage into the optimizer: tokens by model, seats by real adoption, your meter rather than their forecast.
Model the free levers first, routing easy work to a cheaper tier, caching repeated context, batching non-urgent work.
Size the commit against realistic bands, high enough to earn the rate, low enough that realistic growth never tips off the consumption cliff.
Benchmark the rate against comparable enterprise AI agreements, because new units or not, the market still has a distribution.
About an hour from your own meter to a sized commit
What changes: the commit is sized to reality instead of the pitch. The model mix is the largest lever in most AI bills: if right-sizing the mix and pulling the caching and batching levers cuts the baseline 30 percent before you sign, a commit that would have been $1M becomes $700K, and the discount you negotiated stays a discount instead of funding capacity you forfeit.
PART TWO
The levers that move the bill before you negotiate
AI pricing hides a set of levers that change the bill without touching the rate, and pulling them before you commit is often worth more than the discount you negotiate. The first is the model mix. Not every task needs the most capable, most expensive model, and a workload routed to a right sized tier for the easy work and reserved for the hard work can cost a fraction of running everything at the top. Sizing the mix to the job is the single largest lever in most AI bills.
The others are structural. Caching repeated context and batching non urgent work both cut the effective price of the same output, sometimes sharply, and they are levers you control rather than concessions you have to win. The discipline is to model these before you size the commit, because a commit sized to your unoptimized usage is a commit sized to a number you could have made smaller for free.
"A consumption commit sized to the vendor's growth forecast is last decade's unlimited license bundle in a new outfit. Size it to your meter instead."
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The rate benchmarked against comparable enterprise AI agreements. New units, but the market still has a distribution.
PART THREE
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The consumption cliff, and reading the meter
The trap unique to consumption pricing is the cliff. Usage that grows past the commit does not just cost more, it often costs more per unit, at on demand rates that erase the discount the commit was supposed to buy. So the commit has to be sized in a band, high enough to earn the rate, low enough that realistic growth does not tip you off the edge into penalty pricing. Both errors cost money, and the vendor's forecast reliably points you toward the expensive one.
This is why measurement is not optional with AI. A per seat tool at least has a countable number of seats. Consumption has a meter that only your own telemetry can read honestly, and a commitment set without reading it is a guess the vendor is happy to price. The teams that buy AI well are the ones that treat their own usage data as the negotiation's most important document.
THE DISCIPLINE
Buying AI without the overbuy
1 Start from your meter. Size every commit to measured usage, not the vendor's growth forecast. The forecast is priced to make you overshoot.
2 Right size the mix. Route easy work to a cheaper tier and reserve the top model for the hard work. The model mix is the largest lever in most AI bills.
3 Pull the free levers. Cache repeated context and batch non urgent work before you commit. These cut the bill without a single concession from the vendor.
4 Mind the cliff. Set the commit in a band that earns the rate but survives realistic growth, so usage never tips into on demand penalty pricing.
THE HONEST LIMIT
New units, old judgment
The optimizers model your usage and the levers, and the benchmarks place the rate against comparable deals, but AI pricing moves fast and the model that is expensive today may be the bargain next quarter. A commit is still a forecast, and no tool removes the judgment about how fast your own adoption will really grow.
What does not move is the discipline. Size to reality, price to the market, and pull the free levers before you pay for anything. AI software dresses that up in tokens and consumption curves and a compelling story about the future, but underneath it is the same negotiation buyers have always run. The buyers who do well are the ones who recognise an old trap in new units, and read their own meter before they read the vendor's forecast.
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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