To ease customer anxiety about runaway costs from its new AI agents, Notion is implementing usage-based pricing but delaying actual billing for several months. This grace period allows users to see their metered usage, understand the value, and adjust, mitigating the fear of unpredictable bills before they have to pay.

AI companies are selling large, seat-based contracts based on hype and experimental budgets, inflating current ARR. Investors are skeptical because, like early SaaS, customers will eventually demand usage-based or outcome-based pricing, challenging the long-term revenue stability of these startups.

Usage-based pricing for AI faces strong customer resistance. Unlike cloud storage where usage is directly controlled, AI credit consumption can be driven by new vendor-pushed features. This lack of control and predictability leads to bill shock, making customers prefer the stability of per-seat models.

While AI pushes software toward consumption-based pricing, SAP employs a hybrid model. The CTO explains that enterprise customers are not ready for pure consumption as they require budget predictability and are not yet fully trusting of AI outcomes, forcing a gradual transition away from seat-based licenses.

Beyond upfront pricing, sophisticated enterprise customers now demand cost certainty for consumption-based AI. They require vendors to provide transparent cost structures and protections for when usage inevitably scales, asking, 'What does the world look like when the flywheel actually spins?'

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.

The shift to usage-based pricing for AI tools isn't just a revenue growth strategy. Enterprise vendors are adopting it to offset their own escalating cloud infrastructure costs, which scale directly with customer usage, thereby protecting their profit margins from their own suppliers.

AI startups often use traditional per-seat pricing to simplify purchasing for enterprise buyers. The CEO of Legora admits this is suboptimal for the vendor, as high LLM costs from power users can destroy margins. The shift to a more logical consumption-based model is currently blocked by the buyer's operational readiness, not the vendor's preference.

Enterprises struggle to adopt AI agents due to unpredictable, consumption-based pricing. The inability to budget for fluctuating token or credit usage makes scalable deployment nearly impossible for finance departments to approve, creating a significant hurdle to widespread adoption.