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Not All Features Are Created Equal (Economically)
AI products bundle features with very different execution paths under a single pricing model. The table below shows how small feature changes activate different cost multipliers that surface at scale.
Feature Economics by Execution Path
| Row Labels | Feature A: Chat | Feature B: Doc Summary | Feature C: Workspace Agent |
|---|---|---|---|
| Input / Output Context for read/write | 1.0x Small | 5-20x Medium | 20-100x Unbounded |
| Execution Steps Internal actions for one attempt | 1.0x 1-Step Process Direct Inference | 2-3x Sequential Steps Read-Summarize-Format | 5-10x Recursive Loop Plan-Retrieve-Reason-Repeat |
| Iteration Loops (Failure Path) How often must be re-run to get a result | 1.0x User Moves on | 1-2x Re-run for Quality | 2-5x Re-planning, non-convergence |
| Total Multiplier Typical Cost per Use | 1.0x $0.001 | 10-120x $0.02 - $0.10 | 200-5,000x $0.50 - $5.00 |
| Viable Pricing Model | Flat Rate | Tiered / Capped | Usage / Metered |
- Features look identical in the UI, but behave differently economically
Unit economics diverge by orders of magnitude due to context size, internal fan-out and retry behavior - Failure, not success, is what drives cost
Iteration loops (failure path) dominate unit economics. The system re-runs the full execution path, one single bad loop can cost more than dozens of good runs - Pricing model is based on feature shape
Traditional SaaS flat pricing only works with linear features while agents force usage-based pricing
This is why scale breaks in AI businesses. Cost is not driven by how often features succeed, but by how they fail. Feature economics is key for pricing and architecture decisions.