The article compares cost-plus and value-based pricing for AI inference resellers, showing how cost-plus margins shrink as inference commoditizes while value-based charges per outcome retain durable margins. It also covers cost-optimization tactics—model routing, caching, distillation—and explains why bring-your-own-key customers break cost-plus but still fit value-based and optimization models.

• Cost-plus pricing on inference collapses as models commoditize since customers can switch to cheaper raw API calls once they spot the markup
• Value-based pricing (Sierra charging per resolved ticket, Devin's Agent Compute Units) decouples revenue from inference costs by charging for outcomes instead
• Distillation—training a sub-8B "student" model from a "teacher" model—can cut per-call costs (e.g., $1.00 to $0.70) while creating a proprietary edge that's harder to copy than caching or routing
• Bring-your-own-key customers break cost-plus pricing entirely but still work under value-based or platform-fee/optimization models