Pricing is the highest-leverage decision most teams make once and forget. These 15 prompts force you to write the hypothesis behind each price — who pays, what they compare against, what they downgrade if you raise it. If you want the full workflow around these templates — what to feed the model, what to validate by hand, and a 1-week test plan — see our walkthrough on generating pricing hypotheses with AI before launch. Coverage: free vs freemium vs trial, 2-tier vs 3-tier vs custom, usage-based vs seat-based, anchor and decoy patterns, packaging vs pricing, and the most-skipped exercise — writing why a tier exists at all.
Who this is for
Founders pricing a v1, PMs leading a pricing rework, growth leads testing packaging changes, and operators preparing to raise prices on existing customers.
When not to use these prompts
Skip these for one-time consumer purchases (single SKU e-commerce) — those need different decoy / anchor frameworks. Skip too if you have less than 50 paying customers — talk to them directly first.
Prompt anatomy / structure formula
A pricing-hypothesis prompt should always carry six elements:
- Role: who the AI plays (senior PM / solo founder / product designer / indie dev / growth lead).
- Context: stage (idea / MVP / growth / scale), team size, traffic or ARR, platform (web / iOS / Android), audience, constraints.
- Goal: one concrete deliverable — one PRD section, one user-story set, one experiment design, one launch post.
- Constraints: timeline (this sprint / this quarter), scope cuts, must-not-break (existing flows, billing, compliance).
- Output format: table, checklist, ticket-ready JSON, or labeled blocks you can paste straight into Linear / Notion / Jira.
- Examples / signal: 1-2 reference docs or competitors you like, plus 1 anti-example you want to avoid.
Best for
- Initial pricing for a new product
- Pricing page rewrite
- Free vs freemium vs trial decision
- Tier restructure (2 → 3 → custom)
- Pre-mortem before a price increase
15 copy-ready prompt templates
1. Free vs freemium vs trial decider
The first decision. Get this wrong and the rest of pricing fights uphill.
You are a SaaS pricing strategist. For {product}, recommend free / freemium / 14-day trial / 30-day trial / reverse trial / paid only. Output: recommendation, the 3 reasons it fits, the 1 risk, and what data would change the answer.
Context: {product, segment, sales motion, willingness-to-pay signal}Variables to swap: product, segment, sales motion, WTP signal
Optimization: If the recommendation is too generic, add: “Justify by naming a comparable product (named) and what they tried — what worked, what failed.”
2. Tier structure builder (3-tier)
Design a 3-tier pricing structure for {product}: Starter, Pro, Business. For each: target persona, price point, top 5 features, what is excluded, the upgrade trigger from tier below. End with one observation about which tier you expect to over-sell and which to under-sell.
Context: {paste}3. 2-tier minimalist alternative
Argue the case for cutting our pricing to 2 tiers instead of {current N}. What would each tier include, what would be killed, what would move to add-ons. Predict the revenue impact in 1 quarter. End with the strongest objection to going simpler.4. Usage-based vs seat-based decision
For {product}, recommend usage-based vs seat-based vs hybrid pricing. For each option: ideal customer profile, expected ACV impact, churn-risk profile, billing complexity. Pick one and explain when to revisit.5. Anchor + decoy structure
Design a 3-tier pricing page with a deliberate anchor (high-price tier) and decoy (mid-tier that makes the next tier look obvious). For each tier: price, features, intended psychological role (anchor / decoy / target). Show how a buyer scans the page in 8 seconds.6. Willingness-to-pay interview script
Generate a 30-minute customer interview script to surface willingness-to-pay for {product}. Use Van Westendorp 4-question framing plus 4 open-ended follow-ups about anchors. End with a checklist of red flags (interviewer-leading questions) to avoid.7. Pre-mortem of a price increase
We plan to raise prices by {X%} on {date}. Run a pre-mortem: 5 ways this could fail (mass churn, NPS drop, negative press, sales-team revolt, competitor weaponization), and the smallest mitigation for each. End with a kill-switch trigger ("if X happens, roll back").8. Grandfathering policy
Design a grandfathering policy for an upcoming price increase. Options: full grandfather, 12-month grace, partial discount, no grandfather. For each: revenue impact, churn risk, brand-trust impact. Recommend one with reasoning.9. Tier explainer rewrite
Below is our current pricing page. For each tier, write a 1-line "this tier exists for X" statement. If you cannot, that tier is redundant. Output: tier, current copy, proposed 1-liner, kill / keep / merge.
{paste pricing page}10. Packaging vs pricing audit
Audit our pricing problem: is it packaging (wrong bundle) or pricing (wrong number)? Score 1-5 on each of 6 dimensions: feature-tier fit, upgrade path obviousness, decoy effectiveness, price-feature ratio, competitor parity, willingness-to-pay alignment. Recommend the smallest fix.11. Per-seat vs flat-rate trade-off
For a {team-size} customer, calculate effective cost under per-seat vs flat-rate vs hybrid. Show the breakeven seat count and where each model creates buyer-friction. Recommend which to lead with.12. Add-on vs core feature decision
Below are 8 features we are considering. For each, decide: include in core, make a paid add-on, or push to a higher tier. Decision criteria: usage frequency, dev cost, perceived value, willingness-to-pay signal.
Features: {paste}13. Competitor pricing reverse-engineering
For each competitor in {list}, infer the pricing logic from their public page: who they expect to buy each tier, what they signal with anchor pricing, where they hide cost. End with one move we could make that none of them are doing.14. Price-test design
Design an A/B price test for {product}: variants ({control vs +20% vs +50%}), sample size, success metric, guardrails (CAC, churn, support load), duration, kill criteria. Mark which decisions cannot be A/B-tested ethically and must be sequential instead.15. Pricing-page copy from structure
Given this finalized tier structure, write the pricing-page copy. For each tier: 1-line value statement (less than 12 words), 4 feature bullets, 1 social proof line. Then write the FAQ block (5 Qs covering: refunds, billing cadence, upgrade, downgrade, custom plans).
Structure: {paste}Common mistakes
- Pricing by gut without writing the hypothesis — you cannot test what you did not articulate.
- Copying a competitor’s price without copying their cost structure or sales motion.
- 3 tiers when 2 would do — or 4 when 3 is the ceiling for most buyers.
- Free tier with no upgrade trigger — it just costs you money and confuses positioning.
- Raising prices without grandfathering policy — predictable churn spike.
- Pricing page in pure feature lists — buyers scan for value, not feature counts.
- Skipping the willingness-to-pay interviews and relying only on AI guesses.
How to push results further
- Write 1-line “this tier exists for X” for every tier before publishing — if you cannot, the tier is dead weight.
- Always pair a price hypothesis with the metric that would falsify it (e.g., churn over 8% on the higher tier).
- Anchor pricing only works when the anchor tier is plausible — fake anchors get caught.
- Test price by cohort and date, not blanket A/B — pricing tests are tricky and ethical.
- Talk to 10 customers about pricing before any change; AI cannot replace this.
- When in doubt, raise prices to acquire fewer better customers; rarely the other way.
- Refresh the pricing model once a year; product changes faster than the pricing page does.
FAQ
- Should I always start with freemium?: No. Freemium fits high-frequency, network-effect, or content products. For high-touch B2B, a 14-day trial usually beats it.
- How many tiers is right?: Most SaaS land at 3 + custom enterprise. Going below 3 forces fewer decisions; going above 3 confuses buyers.
- When can I raise prices?: When new-customer NPS is healthy, churn is under 3% monthly, and 3+ recent customers said “we would have paid more.”
- Should I show prices publicly?: For self-serve, yes. For enterprise, hide custom tier but always show starter prices — hidden full pricing damages trust.
- How do I avoid grandfather-rage on a price increase?: Notify 60 days ahead, offer a 12-month lock-in at the old price, and personally email top accounts before the public announce.
Related
- Startup idea evaluation prompts
- Feature prioritization prompts
- Growth experiment prompts
- Churn analysis prompts
- Product, App & Startup Prompts hub
Tags: #Prompt #Product startup #Pricing