Price Pricing Models Comparison

---
name: price-pricing-models-comparison
description: Lays out a Pricing Models Comparison per service, with hourly, day rate, fixed fee, retainer, subscription, value, outcome and hybrid models side by side on five considerations, who carries the risk in each, and the model you pick. Use for "run price-pricing-models-comparison", "how do I bill when AI does the work", "hourly or fixed fee", "should I move off hourly billing", "compare pricing models", "fixed fee or value pricing", "which fee model for this service", part of the Pricing Under AI Pressure Pack by Polar Bear.
---

# Pricing Models Comparison

## When To Use
You are asking "how do I bill when AI does the work: hours saved, fixed fee or value?" because faster work now means a smaller invoice. Use it to choose a fee model per service, with the risks in view.

## When Not To Use
If the question is one existing retainer client, use Retainer Redesign. If a client wants to pay for results and you must design that deal, use Outcome-Based Pricing Plan. This skill chooses the model; it does not set the number.

## Inputs
- Your services and how each is billed today
- The AI Savings Decision and the Full-Price Work List
- What you know of each service's scope stability and whether its outcome can be measured
If you have none of this, I start from your list of services and how you bill them today, and mark the output as a first draft.

## Approach
The IPA Pricing Playbook (March 2026, as reported in press coverage) groups fee models into input, output and outcome types, plus hybrids, and weighs them on five considerations. Value pricing is a way of setting the number and can sit on an output or outcome model. The judgment: a taxonomy does not decide for you. The failure this prevents: staying on hours because it feels safe, while every gain in speed lowers your revenue.

## Workflow
1. Ask at most three questions: which services, the scale you want for rating (for example low, medium, high, or your own), and which models you would never offer and why.
2. Group the models: input (hourly, day rate, hours retainer), output (fixed fee per deliverable, subscription), outcome (fee tied to a business result), and hybrids (a base fee plus an outcome part, a fixed fee plus a capped variable). Note that value pricing sets the number on any output or outcome model.
3. Per service, you rate the five considerations on your scale: your appetite for risk; flexibility you need; stability of scope; commercial fluency, yours and the client's; confidence in and measurability of the outcome. I ask; I do not fill in your ratings.
4. Show who carries the risk in each model: overrun, under-delivery, outcome not reached, and AI speed (in an input model, faster work lowers your revenue; in an output model, it raises your margin).
5. Read the ratings against the models: low scope stability rules out a pure fixed fee without a change rule; low outcome measurability rules out a pure outcome fee. Name what each rating rules in and out, nothing more.
6. You pick one model per service and write one line of reason. I do not pick, and I do not offer what others charge.

## Output Format
```markdown
# Pricing Models Comparison
Service: [name] · Billed today: [model] · Rating scale: [scale]
## Five considerations
| Consideration | Your rating | What it rules in or out |
|---|---|---|
| Appetite for risk | [rating] | [models] |
| Flexibility | [rating] | [models] |
| Stability of scope | [rating] | [models] |
| Commercial fluency | [rating] | [models] |
| Outcome measurability | [rating] | [models] |
## Models side by side
| Model | Type | Who carries overrun | Who carries outcome risk | Effect of AI speed | Fits your ratings? |
|---|---|---|---|---|---|
| [hourly / day rate / fixed fee / retainer / subscription / outcome / hybrid] | [input / output / outcome] | [you / client / shared] | [you / client / shared] | [plain line] | [yes / no / with conditions] |
## Decision
[Owner] picks one model per service and writes the reason by [date].
```

## Done When
- Every service has all five considerations rated by you
- Risk is shown for overrun, outcome and AI speed in each model
- Each "with conditions" names the condition
- Nothing in the comparison is a market rate or a peer's price

## Quality Bar
- Plain risk lines, no jargon a client could not follow
- Value pricing is shown as a way of setting the number, not a separate model
- Ratings come from you; any I could not get stay [bracketed]
- Contract wording for any new model: check with a qualified adviser
- Claude lays the models side by side; you choose

## Next
Run price-productized-service (Productized Service Sheet) to package the service on its chosen model.

## About the makers

This pack is made by Polar Bear, a consultancy built by ex-McKinsey founders with a dream to make AI work for People, not instead of them. We help our clients build people systems and AI-first ways of working, and we run our own company on Claude. If your team has outgrown the self-serve version, message Pauline (linkedin.com/in/paulinebertry).