Credit-based pricing for AI products requires three core infrastructure pieces: a credit ledger, precise metering, and strict enforcement. It works best when you have high inference costs to control, customers expecting usage billing, and want predictable charges that drive expansion revenue through top-ups and overages.
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Look, if you're building an AI product right now, like there's a very high chance that you've been thinking about whether or not credits make sense for your business. I think people get it like seats and flat fees assume that every customer costs you about the same and gets about the same value out of it. And that was true for a long time. That is not true anymore. As we all know with AI, what it costs you to serve someone and what they get out of it both move around basically constantly. So, you know, credits sit in between, right? Like you buy a balance upfront. It's predictable on both sides. You top up when you run low, which if you're the one selling is expansion revenue that feels really natural. If you get to price each action and what it actually costs you and what it's worth to the customer, you're kind of in a better value equation. So credits make a lot of sense for a lot of companies, but the reality is going to credit-based monetization. Is a major infrastructure decision. And the thing that you need to be prepared for is that underneath credits, there are really 3 jobs your billing infrastructure has to do. You have to have a credit ledger. You have to make sure that you have excellent metering and you have to make sure that you have excellent enforcement. You can build all of that yourself. You can buy a couple of those and build the rest, or you can buy the whole thing off the shelf. You know, when somebody wants to go to credit-based pricing, this is a question for you. Like, what do they actually need?
I think a couple things are going to be true for credits to make a lot of sense. One is you want to move to usage-based and you have a customer base that's accustomed to that and even expecting it. And probably alongside that, you have an AI product that has pretty significant internal costs and you need to control that, especially as we've seen inference costs just go up. Um, and then the sec— and maybe the third thing here is you are hoping to provide a level of predictability to your customers. Um, and both in like the self-service context and in the sales-led context where for self-service, they don't want to be surprised by a bill. They also have familiarity with tools like Claude and ChatGPT and et cetera, where They have a lot of control. They're used to the concept of credits. And then on the sales side, you can sell large amounts of credits on an annual basis. You can set up overage plans and top-ups, and it's a powerful way to sell, you know, with these key concepts present of significant internal costs, predictability, and a leverage to expand customers to.
