What does the Micro-SaaS Pricing Engine calculate?

Derives a price floor (margin + CAC payback), a suggested price (competitor-anchored or target-margin), and a ceiling (willingness-to-pay heuristic) for a micro-SaaS product. Stub entry — see hand-written methodology at `/methodology/micro-saas-pricing-engine/` for the full treatment.

What inputs does the Micro-SaaS Pricing Engine need?

It takes 5 inputs: costPerUser, cac, competitorAverage, targetGrossMargin (default 75), targetPaybackMonths (default 12). Outputs returned: priceFloor, suggestedPrice, priceCeiling.

Can I verify the Micro-SaaS Pricing Engine with a worked example?

Yes. With cost = $2, cac = $80, competitor_avg = $29, target_margin = 80%, target_payback = 12. the tool returns margin_floor = $10, payback_floor ≈ $8.33, price_floor = $10.

Where does the Micro-SaaS Pricing Engine get its benchmark data?

Reference data is sourced from: OpenView SaaS Benchmarks 2024 (as of 2024).

What can the Micro-SaaS Pricing Engine not tell me?

Known limitations: Pricing is context-heavy — positioning, brand, and channel shape willingness-to-pay more than cost structure. Ceiling heuristic is editorial, not survey-backed.

Methodology: Micro-SaaS Pricing Engine

1. Scope

The Micro-SaaS Pricing Engine produces three numbers — a price floor, a suggested price, and a price ceiling — from per-user cost, target gross margin, CAC-payback constraints, and a competitor anchor. It is a cost-and-anchor model. It does not survey willingness to pay, does not run Van Westendorp or Gabor-Granger, and does not estimate demand elasticity from your actual traffic.

2. Inputs and outputs

Inputs: per-user monthly cost (AI inference + hosting + auth + email, typically from the AI Stack Cost Calculator), target gross margin (default 75%), target CAC-payback months (default 12), blended CAC, average competitor price, and a positioning hint (value-priced / mid-market / premium).

Outputs: priceFloor (never charge less), suggestedPrice (based on the positioning mode), and priceCeiling (a premium-anchored bound).

Engine source: src/lib/micro-saas-pricing-engine/engine.ts.

3. Formula / scoring logic

# Price floor — two constraints, take the binding one
margin_floor  = cost_per_user / (1 - target_gross_margin)
payback_floor = cac / (target_payback_months * target_gross_margin)
price_floor   = max(margin_floor, payback_floor)

# Suggested price — competitor-anchored when the competitor data is real;
# otherwise fall back to margin-anchored.
if competitor_avg_price is set:
  suggested_price = competitor_avg_price * positioning_multiplier
  #   value-priced   -> 0.6 to 0.8
  #   mid-market     -> 0.9 to 1.1
  #   premium        -> 1.5 to 2.0
else:
  suggested_price = cost_per_user * (1 / (1 - target_gross_margin)) * 1.5
  # 1.5x margin floor as a default headroom

suggested_price = max(suggested_price, price_floor)
price_ceiling   = max(competitor_avg_price * 2.5, suggested_price * 1.5)

4. Assumptions

Two-constraint floor. Price must clear both the gross-margin constraint (cost coverage) and the CAC-payback constraint (recover acquisition in the target window). Whichever binds more tightly is the floor.
Competitor-anchored preferred over margin-anchored when a real competitor set exists — buyers compare to a reference price before they compare to your cost structure.
Positioning multiplier is editorial. Value (0.6–0.8×), mid-market (0.9–1.1×), premium (1.5–2.0×) reflect the pattern of published micro-SaaS pricing across Indie Hackers, MicroAcquire listings, and the OpenView 2024 sample. They are not statistically calibrated.
Ceiling is a fence, not a forecast. A 2.5× competitor multiple rarely survives contact with buyers; the ceiling exists to flag experiments, not to encourage them.
Per-user pricing model. Tiered or usage-metered products need to be sliced by tier and run through the engine once per tier.

5. Data sources

OpenView SaaS Benchmarks 2024 — CAC-payback percentiles by stage.
Paddle SaaS Benchmarks 2024 — gross-margin percentiles.
SaaS Capital Annual Survey — bootstrapped-SaaS margin and pricing patterns.

6. Known limitations

No willingness-to-pay model. Cost structure tells you the floor; buyer psychology sets the ceiling. Pair the suggested price with 5–10 customer interviews and an A/B pricing test before committing.
Competitor anchor quality depends on the user. A bad competitor set (wrong segment, outdated pricing, anchor-priced loss leaders) produces a bad suggested price.
No psychological pricing. The engine does not round to .95 / .99 thresholds or model anchoring effects.
No multi-seat or per-feature modelling. Enterprise products with negotiated contracts need custom scaffolding on top of the output.

7. Reproducibility

Input
costPerUser = $2, targetMargin = 80%, targetPayback = 12, cac = $80, competitorAvg = $29, positioning = mid-market.

Expected output
margin_floor = $10, payback_floor ≈ $8.33, price_floor = $10. Competitor-anchored mid-market suggestion ≈ $29 × 1.0 = $29. Ceiling ≈ max($29 × 2.5, $29 × 1.5) = $72.50.

8. Change log

2026-04-24methodology page first published. Two-constraint floor and positioning-multiplier logic documented.

Worked example

Run live against the same engine this site ships (/engines/micro-saas-pricing-engine.js). The inputs and outputs below are recomputed on every build and independently re-verified in CI — they are never hand-authored.

Input

tool: micro_saas_pricing_engine
current_users: 100
api_cost_per_user: 0.5
fixed_monthly_costs: 500
competitor_price_low: 10
competitor_price_high: 50
target_gross_margin: 80
value_metric: per_seat

Output

priceFloor: 27.5
suggestedPrice: 27.5
priceCeiling: 60
costPerUser: 5.5
totalMonthlyCost: 550
pricePoints[0].price: 27.5
pricePoints[0].mrr: 2750
pricePoints[0].grossMargin: 80
pricePoints[1].price: 27.5
pricePoints[1].mrr: 2750
pricePoints[1].grossMargin: 80
pricePoints[2].price: 60
pricePoints[2].mrr: 6000
pricePoints[2].grossMargin: 90.8
insight: At 100 users and $27.5/mo, your projected MRR is $2750. The suggested price gives you 80% gross margin with room to grow.
marginWarning: false

Frequently asked questions

What does the Micro-SaaS Pricing Engine calculate?: Derives a price floor (margin + CAC payback), a suggested price (competitor-anchored or target-margin), and a ceiling (willingness-to-pay heuristic) for a micro-SaaS product. Stub entry — see hand-written methodology at `/methodology/micro-saas-pricing-engine/` for the full treatment.
What inputs does the Micro-SaaS Pricing Engine need?: It takes 5 inputs: costPerUser, cac, competitorAverage, targetGrossMargin (default 75), targetPaybackMonths (default 12). Outputs returned: priceFloor, suggestedPrice, priceCeiling.
What formula does the Micro-SaaS Pricing Engine use?: The exact computation is: margin_floor = cost / (1 - target_margin); payback_floor = cac / (target_payback_months * target_margin); price_floor = max(margin_floor, payback_floor)
Can I verify the Micro-SaaS Pricing Engine with a worked example?: Yes. With cost = $2, cac = $80, competitor_avg = $29, target_margin = 80%, target_payback = 12. the tool returns margin_floor = $10, payback_floor ≈ $8.33, price_floor = $10.
Where does the Micro-SaaS Pricing Engine get its benchmark data?: Reference data is sourced from: OpenView SaaS Benchmarks 2024 (as of 2024).
What can the Micro-SaaS Pricing Engine not tell me?: Known limitations: Pricing is context-heavy — positioning, brand, and channel shape willingness-to-pay more than cost structure. Ceiling heuristic is editorial, not survey-backed.