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.

Value-based flat fees should not just reflect the initial time estimate. As a business becomes more efficient and reduces the time required for a task, the flat fee should remain the same. This allows the business, not the client, to reap the financial reward of its accumulated experience.

Atlassian's CEO argues against the death of per-seat pricing. He states that customers dislike the unpredictability of consumption models, and value-based models are too hard to measure accurately. This practical friction ensures simpler, predictable pricing will persist.

Deliver's growth stagnated until they shifted from complex, variable fees to a simple flat rate. This treated pricing not as a billing model but as a product feature that solved the customer's core need for financial predictability, which became their primary growth catalyst.

At scale, a one-size-fits-all pricing model fails. Salesforce CEO Mark Benioff explains that they must offer a mix of seat-based, all-you-can-eat enterprise agreements (ELAs), and consumption-based models. For nearly every significant customer, a custom pricing agreement is crafted to meet their specific needs and circumstances.

Traditional SaaS companies are trapped by their per-seat pricing model. Their own AI agents, if successful, would reduce the number of human seats needed, cannibalizing their core revenue. AI-native startups exploit this by using value-based pricing (e.g., tasks completed), aligning their success with customer automation goals.

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.

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.