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 7 inputs: current_users, api_cost_per_user, fixed_monthly_costs, competitor_price_low, competitor_price_high, target_gross_margin (default 75), value_metric. Outputs returned: priceFloor, suggestedPrice, priceCeiling, costPerUser / totalMonthlyCost / pricePoints.

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 (competitor high × 1.2) 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 your cost structure (fixed monthly cost, per-user API cost, and user count), a target gross margin, and a competitor low/high price band. It is a cost-and-anchor model. It does not take a CAC or payback input, 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: current_users, api_cost_per_user (monthly), fixed_monthly_costs, competitor_price_low, competitor_price_high, target_gross_margin, and a value_metric (per seat / per project / per API call / flat). There is no CAC, payback-months, or positioning-hint input.

Outputs: priceFloor (never charge less), suggestedPrice (the higher of the floor and 90% of the competitor midpoint), priceCeiling, plus costPerUser, totalMonthlyCost, three pricePoints (price / MRR / gross margin), an insight string, and a marginWarning flag.

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

3. Formula / scoring logic

cost_per_user   = fixed_monthly_costs / users + api_cost_per_user
price_floor     = cost_per_user / (1 - target_gross_margin)

# Suggested price: the higher of the floor and 90% of the competitor midpoint
competitor_mid  = (competitor_price_low + competitor_price_high) / 2
suggested_price = max(price_floor, competitor_mid * 0.9)

# Ceiling: 20% above the competitor high (a willingness-to-pay estimate)
price_ceiling   = max(suggested_price, competitor_price_high * 1.2)

4. Assumptions

Single margin floor. The floor is the price that hits your target gross margin on per-user cost. There is no CAC-payback constraint — the engine takes neither CAC nor a payback window.
Competitor-anchored suggestion. The suggested price is the higher of the floor and 90% of the competitor midpoint (the average of your low and high anchors) — buyers compare to a reference band before they compare to your cost structure.
Ceiling is a fence, not a forecast. The ceiling is 20% above the competitor high (or the suggested price, whichever is larger); it exists to flag pricing 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
current_users = 100, api_cost_per_user = $1, fixed_monthly_costs = $200, competitor_price_low = $25, competitor_price_high = $45, target_gross_margin = 75%.

Expected output
cost_per_user = $3 (= $200/100 + $1). price_floor = $12 (= $3 / 0.25). Competitor midpoint $35, so suggested_price = $31.50 (= max($12, $35 × 0.9)). price_ceiling = $54 (= max($31.50, $45 × 1.2)).

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 7 inputs: current_users, api_cost_per_user, fixed_monthly_costs, competitor_price_low, competitor_price_high, target_gross_margin (default 75), value_metric. Outputs returned: priceFloor, suggestedPrice, priceCeiling, costPerUser / totalMonthlyCost / pricePoints.
What formula does the Micro-SaaS Pricing Engine use?: The exact computation is: cost_per_user = fixed_monthly_costs / users + api_cost_per_user; price_floor = cost_per_user / (1 - target_margin); competitor_mid = (competitor_price_low + competitor_price_high) / 2; suggested_price = max(price_floor, competitor_mid * 0.9); price_ceiling = max(suggested_price, competitor_price_high * 1.2)
Can I verify the Micro-SaaS Pricing Engine with a worked example?: Yes. With current_users = 100, api_cost_per_user = $1, fixed_monthly_costs = $200, competitor_price_low = $25, competitor_price_high = $45, target_gross_margin = 75. the tool returns costPerUser = $3, priceFloor = $12, suggestedPrice = $31.50, priceCeiling = $54.
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 (competitor high × 1.2) is editorial, not survey-backed.