To remove friction and encourage deep usage, Granola avoids credits or pay-per-use models, despite high backend costs. The strategy is to build the best product and capture the market first, treating inference costs as a necessary expense for growth.

The strategy of sacrificing short-term revenue for long-term growth is a repeatable playbook. After success at Appfolio with free support, the guest applied the same model at Ontra. By using AI to lower onboarding costs, they made the service free, reducing friction and dramatically increasing new customer conversion rates.

OpenAI intentionally operates both consumer and enterprise businesses, viewing its free consumer product as a powerful acquisition funnel. This strategy creates a "commitment curve" where users dramatically increase engagement as they upgrade: free users average 7 queries per day, while pro users perform 11 times more, demonstrating a clear path to monetization.

Read AI discovered that the longer a user stays on the free plan, the more likely they are to eventually pay. By allowing users to build a large personal data archive for free, the value of upgrading to access and query that history becomes a powerful, self-created incentive.

Contrary to typical freemium models, business customers are wary of free AI products for critical tasks due to uncertain future pricing. By charging from the start, even if subsidized, a startup establishes a predictable cost, aligns with corporate budgeting processes, and builds the trust necessary for enterprise adoption.

Asana's CPO highlights a disconnect between negative investor sentiment towards SaaS and strong underlying business fundamentals. While investors are "risk off" due to AI hype, companies like Figma and Atlassian post phenomenal quarters because they deliver real, enterprise-grade value that customers continue to rely on.

For high-usage B2C AI apps, free compute credits are a powerful growth lever. Juno accepted OpenAI's $2M offer not just to cover costs, but to strategically expand its free tier. This allows more users to experience the product's value over weeks, maximizing the potential for organic, word-of-mouth referrals.

While AI features are costly to offer for free, it's essential for adoption. Lovable treats these costs as a marketing expense because it's the only way to get products into users' hands and change their habits, leading to double-digit paid conversion rates.

Switching a usage-based AI product to an unlimited SaaS model eliminates budget as a barrier, driving deep adoption. The new bottleneck becomes the client's time to process the AI's output, creating an opportunity to build features that automate this "last mile" of work.