Unlike sticky workflow software, data products are 'ingredients' that can sit unused. If a new customer doesn't integrate your data into a model, decision engine, or other tangible outcome within the first 12 weeks, the likelihood of renewal drops dramatically.

While incumbents sell roadmaps, startups can collapse enterprise sales cycles by demonstrating a fully functional product that is provably better *today*. Showing a live, superior solution turns a year-long procurement process into a 60-day sprint for motivated buyers.

The company initially used a one-time payment plan, resulting in low customer lifetime value. Switching to a recurring subscription model, even for a product with natural churn, massively increased revenue and LTV by capturing more value over time from each customer.

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.

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.

To combat renewal fatigue, DaaS vendors must guide customers to a single, measurable business win within the first 60 days. This aggressive timeline forces prioritization of the most tangible use case, creating an "anchor point" of proven value that makes future renewal conversations significantly easier.

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.

In the age of AI, software is shifting from a tool that assists humans to an agent that completes tasks. The pricing model should reflect this. Instead of a subscription for access (a license), charge for the value created when the AI successfully achieves a business outcome.