PM Calculators

Compare the total cost of car ownership across buy-new, buy-used, lease, and keep-your-current-car — depreciation, insurance, maintenance ramp, and fuel over a real horizon, not just the monthly payment. Use when asked should I lease or buy a car, is it cheaper to keep my old car, what does this car really cost per month, or new vs used total cost. Produces the ranked scenario totals, per-month true cost, the assumption ledger, and the not-modeled list.

Compare an EV against a comparable gas car on total cost — upfront gap after incentives, energy vs fuel per year, maintenance delta, and the crossover year when the EV pulls ahead (or doesn't). Use when asked is an EV worth it, EV vs gas total cost, when does an EV pay for itself, or should my next car be electric. Produces the year-by-year cumulative comparison from the script, the crossover year, the per-mile energy math, and the honest not-modeled list.

Divide things that cannot be divided — an inherited house, a dissolved partnership, the furniture after a breakup — using sealed-bid fair division (Knaster), computed by the bundled script so everyone ends provably above their own fair share. Use when asked to split an estate fairly, divide assets between siblings or ex-partners, who gets the house and what do they owe the others, or handle unequal shares. Produces the sealed-bid procedure, the computed allocation with exact cash transfers, the per-person fairness proof, and the presentation script. Math for the family meeting; not legal advice.

Build a debt payoff plan — avalanche vs snowball simulated month by month on your actual debts, the real payoff dates, and the psychology-vs-arithmetic tradeoff priced in dollars. Use when asked how do I pay off my debts, avalanche or snowball, make me a debt payoff plan, tackle my credit cards or loans, or when will I be debt-free. Produces the month-by-month comparison from the script, the payoff order with dates, the interest cost of choosing morale over math, and the plan-survival rules. Educational, not regulated financial advice.

Compute a financial-independence (FIRE) target and years-to-reach with every assumption labeled as an assumption — plus a sensitivity table instead of a single false-precision answer. Use when asked what's my FIRE number, when can I retire early, how much do I need to be financially independent, or model my savings trajectory. Produces the FIRE number, years-to-target at stated assumptions, a return × withdrawal-rate sensitivity grid, and the honest list of what the model ignores.

Model rent-vs-buy honestly — year-by-year net position for both paths including the assumption everyone drops (the renter invests the difference), with a breakeven horizon instead of a verdict. Use when asked should I rent or buy, does buying beat renting in my city, when does buying break even, or run the rent-vs-buy numbers. Produces the year-by-year comparison table, the breakeven year, the assumption list with defaults labeled, and the not-modeled list.

Compute what a degree will actually cost — sticker minus real aid, inflated per year, split into cash and loans, with the loan's decade-long monthly tail made visible before enrollment instead of after. Use when asked what will college really cost, compare these two offers' real prices, how much loan payment after graduation, or is this school affordable. Produces the all-in number from the script, the offer-letter decode (grants vs loans untangled), the monthly-tail reality check, and the two-school comparison.

Compute the core SaaS metrics — MRR/ARR, growth, NRR/GRR, churn, quick ratio, magic number — from your numbers. Use when asked to calculate SaaS metrics, MRR/ARR, net revenue retention, the quick ratio, or to build a SaaS metrics snapshot for a board/investor update. Produces a computed metrics dashboard with each value, its benchmark, and a one-line read on what it means.

Model staying for annual raises vs job-hopping for bigger bumps — cumulative earnings trajectories, the crossover year, and the costs the salary math hides (vesting resets, promotion paths, search risk). Use when asked should I switch jobs for more money, is job hopping worth it, model my salary if I stay vs leave, or raise versus new offer. Produces the year-by-year salary and cumulative-earnings table, the crossover year, and the not-in-the-model checklist that usually decides it.

Estimate the ROI, payback, and NPV of an investment, project, or purchase. Use when asked to calculate ROI, build a business case, justify a purchase/initiative, work out payback period, or compare options by return. Produces a computed ROI summary (net benefit, ROI %, payback, simple NPV) with the assumptions made explicit and a sensitivity note, so a business case is defensible.

Size an emergency fund from essential spend and real risk factors — not a one-size 'six months' — with the funding timeline and where the money should sit. Use when asked how big should my emergency fund be, do I have enough saved, emergency fund or invest, or how many months of expenses do I need. Produces the risk-adjusted target from the script, the essential-spend worksheet, the funding plan, and the what-counts-as-an-emergency rules.

Compute who gets what at each exit price from a cap table — liquidation preferences, conversion points, and where the founders' share collapses. Use when asked to model an exit waterfall, what do I get if we sell for X, explain liquidation preferences on my cap table, or compare payouts across exit prices. Produces a per-stakeholder payout table across exit values with conversion decisions shown, plus the plain-English reading of what the structure means for each party.

Derive a freelance day/hourly rate backwards from target income, honest billable utilization, overhead, and the self-employment tax premium — the arithmetic that proves a rate is not salary÷2000. Use when asked what should I charge as a freelancer, how do I set my consulting rate, why is my freelance rate so high, or convert my salary to a contract rate. Produces the required-revenue breakdown, billable-hours math, the hourly and day rate, and the multiplier vs the naive salary÷2000 number.

Model the unit economics of a business — CAC, LTV, payback, contribution margin — from real inputs. Use when asked to calculate unit economics, work out LTV:CAC, find the payback period, or check whether a business model is viable per customer. Produces a computed unit-economics summary (LTV, CAC, ratio, payback, contribution margin) with a verdict and the levers that move it most.

Build a wedding budget that survives to the wedding — allocation by real shares, the per-guest lever made explicit, the routinely-forgotten line items priced in from day one, and a contingency that isn't decorative. Use when asked make a wedding budget, how do people split X across a wedding, we have N dollars and M guests, or why is our wedding over budget. Produces the allocation table from the script, the guest-count math, the forgotten-items audit, and the track-against-actuals discipline.

Model whether solar panels pay for themselves for your roof — net cost after incentives, bill offset with degradation, electricity inflation, the inverter replacement, and the breakeven year, plus the policy risk no calculator controls. Use when asked are solar panels worth it, when does solar break even, check this solar quote's payback claim, or model solar for my bill. Produces the year-by-year table from the script, the breakeven year, the quote-vs-model comparison, and the not-modeled list led by net-metering risk.

Compare two or more job offers as total-comp curves over four years — vesting cliffs, bonuses, 401(k) match, and the crossover year computed, not vibed. Use when asked to compare job offers, which offer pays more over time, model my equity vesting, or is the startup offer actually worth it. Produces a year-by-year and cumulative comp table per offer, the crossover analysis, and negotiation levers ranked by dollar impact.

Calculate cash runway, burn, and the zero-cash date — and whether you're default alive or dead. Use when asked to work out runway, monthly burn, when the money runs out, or how much to raise/cut to reach a target. Produces a computed runway summary (net burn, months of runway, zero-cash date, default alive/dead) plus what it takes to extend it.

Fit a retention curve to observed cohort data and project LTV — computed, not estimated. Use when someone has real cohort retention numbers (month 0, 1, 2…) and asks what lifetime value, lifetime periods, or long-run retention they imply, or whether retention is flattening or leaking. Produces a fitted power curve (parameters, R², retention floor), a 24-36 period projection, and a real .xlsx with live formulas where editing ARPU recalculates LTV — via the bundled zero-dependency script.

Model pricing scenarios — tiers, margins, break-even, and the revenue impact of a price change. Use when asked to calculate pricing, model a price increase, find break-even volume, set tier prices to a margin target, or estimate the revenue effect of a pricing change. Produces a computed pricing model (per-tier margin, break-even units, price-change revenue impact with an elasticity assumption) and a recommendation.

Cash runway as a distribution, not a number — Monte Carlo simulated. Use when someone asks how long their cash lasts, when to start fundraising, or how burn/revenue volatility changes their runway; especially when the naive cash÷burn answer is driving a decision. Produces P10/P50/P90 runway, month-by-month death probabilities, and a real .xlsx with editable assumptions and a live naive-runway formula — via the bundled zero-dependency simulator.

Find and rank the recurring-payment leak — every subscription annualized, sorted by real yearly cost, with the keep/cancel/downgrade pass and the where-they-hide checklist. Use when asked audit my subscriptions, how much am I spending on subscriptions, help me cancel stuff, or what recurring charges am I forgetting. Produces the annualized ranking from the script, the hidden-subscription hunt list, the keep/cancel/downgrade decisions with the cancellation friction notes, and the re-audit cadence.

Compute the month a refinance actually starts saving money — payment delta, breakeven month, and total interest on both paths including the term-reset trap. Use when asked should I refinance, when does a refi break even, compare my loan to a refi offer, or is this refinance worth the closing costs. Produces the breakeven analysis with both interest totals, the if-you-sell-before-month-N warning, and the cases where the breakeven math lies.

Which assumption actually moves the answer — one-at-a-time sensitivity, ranked into a tornado. Use when a model's output is being argued about (LTV, ROI, forecast) and the room is debating drivers that don't matter, or before spending diligence effort: swing every driver low→high and see which one owns the outcome. Produces the ranked tornado table, share-of-swing per driver, and a real .xlsx — via the bundled zero-dependency script with a safely restricted formula evaluator.

Run the real math on a parent leaving work versus paying for childcare — the second income net of daycare, marginal taxes, and work costs, AND the career-trajectory cost of years out, over horizons instead of one brutal year. Use when asked does it make sense for me to keep working, daycare costs my whole salary, stay-home vs daycare math, or what does leaving work for 5 years really cost. Produces both sides of the ledger from the script, the horizon comparison, and the decision sheet that lets the non-financials vote.

Turn a fork-in-the-road decision into a computed expected-value tree — settle or sue, launch or wait, fix or replace — rolled back by the bundled script, with the break-even probability where the answer flips. Use when asked should we settle or go to trial, build a decision tree, what probability makes this worth it, or compare options under uncertainty. Produces the structured tree, the rollback with the best choice at every fork, the break-even probabilities, and the honest list of what the numbers leave out. Decision support, not advice — the probabilities are yours.

Project completion as a distribution, not a date — Monte Carlo over the task graph. Use when a plan's finish date came from summing 'likely' estimates (it's wrong, mathematically), when leadership needs a commit date, or when you need to know which tasks actually control the timeline. Produces P10/P50/P90 completion, per-task criticality (how often each task sits on the critical path), and a real .xlsx — via the bundled zero-dependency simulator, deterministic with a seed.

Decide what the extra money does about student loans — attack them, invest alongside them, or ride a forgiveness track — with the three paths simulated on your actual loans and the guaranteed-vs-assumed framing kept honest. Use when asked should I pay off my student loans faster, pay loans or invest, is my forgiveness track worth it, or model my student debt. Produces the three-path comparison from the script, the guaranteed-return framing, the forgiveness-track math with its warnings, and the decision sheet.

How many support agents does the queue actually need — Erlang C, computed, not 'tickets per agent' folklore. Use when staffing a support/CS team, defending headcount, or checking whether an SLA is mathematically possible with the current roster. Produces agent counts across load scenarios (with shrinkage), occupancy and average-wait numbers, and a real .xlsx — via the bundled zero-dependency script.

Van Westendorp price sensitivity, computed from real survey answers — crossings found by interpolation, not read off a chart by eye. Use when someone has (or plans) the four-question pricing survey (too cheap / cheap / expensive / too expensive) and needs the optimal price point, the acceptable range, and a defensible readout. Produces OPP/IPP and the PMC–PME range, the four cumulative curves as data, and a real .xlsx with a live revenue what-if — via the bundled zero-dependency script.