Scott Mullin, VP of Sales at Stig, explains why AI companies need consumption-based pricing tied to actual value instead of traditional models. With LLMs like GPT and Claude, every token costs money, making variable pricing essential to protect margins in a market where costs shift constantly.
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You are the VP of sales at Stig, which is a dynamic platform that allows software companies to build and optimize their pricing and packaging with speed and flexibility. How do you think AI is going to impact pricing strategy?
As the VP of Sales at Stig, where we sell monetization infrastructure for the new AI native era, the AI landscape is changing pricing and packaging faster than it's ever changed before. And the companies that are winning in this new era have adopted that as a growth lever. It's obvious that the way that these new AI products are built completely breaks traditional pricing and packaging. Billing technologies, predominantly AI products today, are being adopted in a self serve model where single user comes in and the pricing needs to be able to be consumption based and be tied to the value that they're getting. This needs to be built on top of AI native infrastructure. Right? And LLMs, GPT, Claude, which are all expensive but with variable cost, you know, in usage based considerations where every single token or API call means your margin could change on any single, you know, product offering at any given point. And that causes a ton of complexity because now it's no longer just a finance exercise of how we want to price this. Now you need the real considerations of how do you actually deliver a monetization infrastructure that can allow you to meet those new demands.