Clay deliberately chose usage-based over seat-based pricing because their ideal customer is a technical builder (GTM Ops, Growth Marketer), not an individual salesperson. This model aligns value with the systems these builders create for the entire team, rather than charging for every end-user who benefits from the output.

Pure value-based pricing (e.g., per seat) fails for AI products due to unpredictable token costs from power users. Vercel's SVP of Product advises a hybrid model: one metric aligned with value (like seats) and another aligned with cost (like token usage) to ensure profitability.

Ledge's pricing scales with a customer's operational complexity (entities, currencies, channels), not user count. This aligns their revenue with the value of their AI automation, which aims to make finance teams leaner. It's a strategic bet that value comes from efficiency gains, not headcount.

Standard SaaS pricing fails for agentic products because high usage becomes a cost center. Avoid the trap of profiting from non-use. Instead, implement a hybrid model with a fixed base and usage-based overages, or, ideally, tie pricing directly to measurable outcomes generated by the AI.

While outcome-based pricing is attractive in theory, customers often prefer the certainty of per-user or consumption-based models. According to Nadella, once a customer achieves a successful outcome, they view sharing that upside as a royalty and quickly ask to revert to predictable pricing structures.

The common agency model of charging a percentage of ad spend creates a conflict of interest. It incentivizes agencies to push for higher budgets rather than focusing on efficiency and driving core business outcomes like pipeline and revenue. A flat fee aligns incentives better.

Uplane serves customers who want a 'done-for-you' solution via a managed service, while offering an enterprise SaaS product for in-house teams. This dual model expands their total addressable market by catering to different buying preferences, allowing them to capture revenue that a pure-play SaaS model would miss.

AI SaaS companies have variable, usage-based costs, but customers demand predictable flat fees for procurement. Product Fruits found charging per usage failed. The solution is to accept the uncertainty, create flat-fee plans, and absorb the risk of variable backend costs to close deals.

High Touch's co-CEO declares seat-based pricing obsolete. Their model charges based on the number of marketing campaigns powered by their AI platform. This aligns incentives perfectly: if a campaign is working, the customer keeps it on and High Touch gets paid; if not, they turn it off, creating a simple, value-driven pricing structure.