Consumption-Based Pricing in AI Companies

The traditional SaaS per-seat pricing model is under pressure from AI companies offering outcomes-based or consumption-based pricing. Examples: Decagon charges per resolved support ticket (not per seat). Ramp charges a flat fee (vs. per-card per-month competitors). AI model APIs charge per token. The discourse: as AI commoditizes features, per-seat pricing becomes harder to defend. Companies that price on outcomes align incentives with buyers and win more enterprise deals faster.

For CROs and Head of GTM: pricing strategy is the lever that most affects deal velocity and ACV. Outcomes-based pricing can unlock deals that per-seat pricing loses because the ROI calculation is immediate. But outcomes pricing creates revenue volatility — you earn more when customers succeed, less when they don't. The 2026 enterprise AI consensus: 'price on outcomes where you can prove the outcome.'

• "Per-seat pricing is dying in AI. Here's the 2026 pricing playbook"
• "Outcomes-based pricing: why Decagon charges per resolution instead of per seat"
• "From per-seat to consumption: the ACV implications of pricing model changes"