The original title is: "Why Per-Seat Pricing Breaks AI Agent SaaS (And What Works Instead)"

Short summary

Traditional per-seat pricing fails for AI agent SaaS because cost scales with usage complexity, not user count—one customer with one seat can consume more margin than ten seats. The author presents six pricing models (token-based, per-action, per-task, per-outcome, tiered with overages, and feature tiers) with pros/cons and a decision framework. Key insight: align pricing to actual economics and size free tiers to convert users in 7-14 days.

• •Per-seat pricing breaks for AI agents because LLM costs are unpredictable and decoupled from headcount
• •Six viable pricing models tested: token-based, per-action, per-task, per-outcome, tiered, and feature-locked
• •Author chose per-tool-call; includes free tier sizing math and unit economics for decision-making

Generated with AI, which can make mistakes.