When selling to enterprises, founders can feel intimidated asking for large contract values. A powerful yardstick is to frame the price relative to a fully-loaded engineer's salary (e.g., 'is this worth half an engineer to you?'). This contextualizes the cost against a familiar, significant budget item.

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.

AI startups should choose their pricing model based on a 2x2 matrix of autonomy (human-in-the-loop vs. fully automated) and attribution (how clearly its value can be measured). Low levels lead to seat-based pricing, while high levels of both unlock outcome-based models.

Instead of ad-hoc pilots, structure them to quantify value across three pillars: incremental revenue (e.g., reduced churn), tangible cost savings (e.g., FTE reduction), and opportunity costs (e.g., freed-up productivity). This builds a solid, co-created business case for monetization.

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.

Astronomer's customers for their Clickstream product were more fascinated by its Airflow backend than the product's value proposition. This overwhelming interest validated their pivot to a managed Airflow service, revealing a hidden, more urgent market need.

Effective pricing is not just a number; it is a value story. The ultimate test is whether a customer can accurately pitch your product's pricing and value proposition to someone else. This reframes pricing from a simple number to a compelling narrative.

Lacking market comparables, Nexla priced its initial enterprise deals by first understanding the customer's internal cost to solve the same problem. They then proposed a price that was a clear fraction—like one-fifth or one-tenth—of that internal cost, making the ROI immediately obvious and justifiable for the buyer.