AI pricing model picker

Use when the user needs to pick or defend a pricing model for an AI agent product. Produces a recommended model, tier structure, and pricing anchors grounded in the budget the agent actually displaces.

Profile the agent first

Before recommending anything, get four answers — ask the user or infer from their product description, then confirm:

1. Breadth of responsibility. Does the agent cover a comprehensive job function, or discrete tasks?
2. Workload predictability. Consistent volume, or wildly variable?
3. Attribution. Can a completed outcome be cleanly credited to the agent? Is autonomy high (agent finishes work alone) or low (copilot suggesting actions)?
4. Budget displaced. Headcount, BPO/outsourcing spend, tools budget, or a performance line item? Headcount budgets run roughly 10x larger than tool budgets — this answer moves price more than any other.

Map to the model

Model	Fits when	Watch out
Per agent (FTE replacement, fixed monthly fee)	Comprehensive function, consistent workload, headcount budget	Low differentiation; vulnerable to cheaper competitors
Per action (consumption)	Varied discrete tasks, unpredictable frequency, BPO budget	Highest commoditization risk — pricing pressure only moves down
Per workflow (completed multi-step sequences)	Multi-step processes with standardized intermediate deliverables	Standard workflows invite price compression; complex ones risk negative margin
Per outcome (pay for results)	High autonomy + high attribution, success metric the customer already tracks	Requires consistent performance; attribution disputes; bespoke outcomes proliferate contracts

Rule of thumb: low autonomy + loose attribution → seat or subscription. High autonomy + high attribution → outcome. In between → workflow, or a hybrid (subscription floor + consumption layer — the most common shape among successful transitions).

Set the anchors

Never anchor on compute cost. Anchor on what the work costs done another way:

• an SDR fully loaded runs $70–90k/year — a $2,000/month agent replacing 80% of that job is an easy story
• BPO spend averages ~$877 per employee per year across the market the agent eats into
• outcome pricing anchors on the unit the buyer already values — Intercom's Fin charges $0.99 per resolved ticket; no resolution, no charge

Express the price as a fraction of the displaced cost, then sanity-check margin against the agent's worst-case execution cost.

Structure the tiers

• keep an entry tier that lands inside the champion's discretionary budget
• if usage varies, add credits as the bridge: subscription with an included credit allowance sized so ~80% of customers stay in bounds, then let real usage data tell you where to go next
• for outcome pricing, agree the metric and the measurement methodology before the contract is signed, not after

What good looks like

A great recommendation names the budget line the agent raids, prices against it, and picks the model the buyer can defend internally. Mediocre output picks a model from vibes and prices from competitor screenshots. The failure modes: pricing on tokens (races to zero as LLM costs fall), outcome pricing without attribution infrastructure (renewal-time disputes), and seat pricing an agent that reduces seats (your own success shrinks the deal).

MUST state which of the four models is recommended and why the other three lose. NEVER anchor primarily on model/compute costs. NEVER recommend outcome pricing without naming the metric and how it gets verified.