Usage Pricing Breaks at Renewal
If You Had to Change What You Charge For, Could You?
Every AI contract you sign assumes a pricing unit the market hasn’t settled on. The vendors coping best stopped betting on one.
TL;DR: Salesforce has priced one AI product four ways since May 2025, and the market splits between charging for effort (tokens), output, and outcomes (all-you-can-eat wasn’t sustainable after all). The vendors coping best sell a currency instead: credits whose exchange rate they control, so the next change happens in a rate table. Fund usage data and the currency layer; defer billing, contracts, and comp tied to one unit. Your customer’s budget, built on your unit, is what you can never migrate. Output or outcome pricing leaves rising inference costs on your P&L, and not in a good way. Freeze the exchange rate for each contract term so customers can budget. Bill this year’s usage through two candidate units, on paper, and record what breaks.
Continue reading “If You Had to Change What You Charge For, Could You?”The SaaS Bill Just Split Into Two Meters
One meter counts how many people log in. The other counts everything the agents they authorized won’t stop doing, and the surviving architecture stacks both.
Your next software renewal will likely carry two prices, not one. One line still counts the people who log in. The new line counts everything the agents they turned loose did while nobody was watching a screen. Salesforce already reports it publicly: Agentforce revenue up 205% year over year, with the seat line holding right beside it. This is a stacking model, not a swap. It changes three things at once: what you sell, what you buy, and how fast a software bill you thought was fixed can run away from you.
Continue reading “The SaaS Bill Just Split Into Two Meters”The most durable thing in your software isn’t in the code.
AI can clone a SaaS product in a week and walk an agent straight through a shallow integration. What survives are three things that compound into one another, plus the human judgment beneath them that no competitor can replicate.
A trader at Jefferies, not an engineer, named the “SaaSpocalypse” panic, which tells you what kind of event this is. Software shed close to $2 trillion from its October peak on the theory that anything can now be cloned in a week. The theory is right about features and wrong about moats. The error is picturing a moat as a wall, one thing you build once and stand behind. The durable defense is a loop, and underneath it sits the part no competitor can vibe-code, because it was never in the code: judgment.
Continue reading “The most durable thing in your software isn’t in the code.”AI SaaS Pricing: How to Profit When Every Prompt Has a Real Cost
In SaaS, variable costs are familiar. AWS and Azure bills rise and fall with traffic, storage, and bandwidth, but you can usually forecast them and smooth them with commitments.
AI flips the model because cost is triggered by a mixture of behavior and model choice, not just scale. Each generation can add metered COGS, and multimodal makes the spikes sharper: images, audio transcription, voice output, and video generation can cost orders of magnitude more than a short text reply. Retries, longer outputs, bigger context windows, and tool calls amplify this fast.
Then comes the perception problem. Buyers are trained by ChatGPT and Gemini that AI feels cheap or “free” at the point of use, which anchors expectations. The executive challenge becomes defending value and margin while keeping usage predictable.
Continue reading “AI SaaS Pricing: How to Profit When Every Prompt Has a Real Cost”