SaaS companies scale revenue not by adjusting price points, but by creating distinct packages for different segments. The same core software can be sold for vastly different amounts to enterprise versus mid-market clients by packaging features, services, and support to match their perceived value and needs.

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.

A one-size-fits-all sales role fails in consumption models. Success requires segmenting the team into specialized roles—new business acquisition, customer onboarding, and account management—each with distinct incentives aligned to their specific function, from initial sign-up to value realization and expansion.

Initially, Astronomer priced against the cost of hiring an engineer for analytics tasks. As customers adopted Airflow for critical operational workloads (e.g., regulatory reporting), the pricing conversation shifted. The value is no longer saving a salary, but preventing catastrophic revenue or compliance failures.

A blanket price increase is a mistake. Instead, segment your customers. For those deriving high value, use the increase as a trigger for an upsell conversation to a better product. For price-sensitive customers, consider deferring the hike while you work to better demonstrate your value.

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.

The dominant per-user-per-month SaaS business model is becoming obsolete for AI-native companies. The new standard is consumption or outcome-based pricing. Customers will pay for the specific task an AI completes or the value it generates, not for a seat license, fundamentally changing how software is sold.

Unlike perpetual or even subscription models, consumption-based compensation holds sales reps directly responsible for the customer's ongoing product usage. Reps are on the hook to ensure credits are "burned down," effectively merging the roles of sales and customer success and forcing a continuous selling motion.

For tools requiring a new workflow, like Factory's AI agents, seat-based pricing creates friction. A usage-based model lowers the initial adoption barrier, allowing developers to try it once. This 'first try' is critical, as data shows an 85% retention rate after just one use.