Credits are becoming the new pricing metric for AI products. The logic sounds simple: AI agents consume tokens. Customers pay for credits. But what happens behind the scenes? How do you translate AI usage into credits, calculate the cost, and bill customers in real time? We decided to make this a little more fun. Over the weekend, we developed a playground to build browser extensions through vibe coding, with billing powered by Clocknext. https://test.tryclocknext.com/ Vibe with it. Build a browser extension of your choice. And watch how credits get calculated and billed in real time as you use the platform.
Clocknext is a billing and monetization platform for AI-native apps and agents. It’s tailored for AI founders, revenue, and engineering teams, to track AI usage from your product in real time, turn it into billable metrics with flexible pricing plans, and monitor costs, margins, and profitability at every level.
I ve been talking to a lot of AI founders lately, and one question keeps coming up: "how do you turn raw AI usage into credits your customers actually understand, without losing money on them?"
On paper it sounds simple: 1 credit = X tokens. In practice it gets messy fast:
Input and output tokens cost different amounts, and the gap changes by model
One agent run can fan out into several model calls, tool calls, retrieval and voice
Provider prices change every few months, but your credit price can t keep changing with them
Enterprise customers ask for their own conversion rates
And the question that matters most: are you still making margin on every credit?
