Free Monte Carlo simulator: your retirement chance of success

What is a Monte Carlo chance-of-success simulation?

Named after the randomness of a casino, a Monte Carlo simulation takes your plan — your spending, contributions, and time horizon — and runs it forward thousands of times, each time against a different randomly-drawn sequence of investment returns. Some of those simulated futures include an early crash, some a long calm bull run, some a rough decade in the middle. At the end, the simulation counts how many of those runs left your money lasting through your retirement horizon.

Divide the runs that held up by the total number of runs and you get your chance of success — often shown as a single headline percentage, like "87% success rate." It compresses thousands of possible futures into one honest number: not a prediction of what will happen, but an estimate of how often a plan like yours, under the assumptions you set, would have held up.

Why a chance of success beats a single static rule

Rules of thumb like the 4% rule are useful starting points, but they collapse an entire retirement into one deterministic number built on a smooth, average return every year. Real markets don't hand out average returns evenly — some years crash, some years surge — and the order those returns arrive in matters as much as their average. Two retirees with identical long-run average returns can end up in completely different places depending on whether the bad years hit early or late. That's sequence-of-returns risk, and a single withdrawal-rate number literally cannot see it.

Monte Carlo is the fix: instead of one assumed path, it stress-tests your plan against thousands of different paths — different orderings, different early shocks, different lucky or unlucky decades — and reports the fraction that worked. Historical research on the 4% rule illustrates the same idea from the deterministic side: the widely-cited Trinity study found a 4% withdrawal over a ~30-year horizon from a 50/50 stock/bond portfolio succeeded in about 96% of historical periods — which is itself a chance-of-success figure, just computed from actual history instead of simulated sequences. Monte Carlo generalizes that idea to your own numbers, your own horizon, and far more sequences than history alone provides.

How to read your chance-of-success number

There's no universal cutoff that makes a percentage "good" or "bad" — a 75% chance means something different to someone with flexible spending than to someone with fixed, non-negotiable expenses. A few things matter more than chasing a specific number:

• Direction, not just level. Does the number rise as you save more, work a bit longer, or trim spending? A plan that improves in response to realistic changes is more trustworthy than one number in isolation.
• Sensitivity to your assumptions. Nudge your expected return, your spending, or your retirement date and watch how much the percentage moves. A number that swings wildly from small assumption changes deserves more scrutiny than one that's stable.
• What "failure" actually means in the model. A simulated run that "fails" usually means the portfolio would have been depleted under those assumptions — not that you'd be left with literally nothing, since a real plan adjusts spending long before that point. The number is a stress test, not a verdict.

Free in FIManager — paywalled by many others

FIManager's free tier runs the Monte Carlo simulation on your plan and shows the headline Success Rate — no credit card, no trial clock. The detailed breakdown underneath that headline number — resilience scoring, downside outcomes, percentile bands, and a year-by-year trial-by-trial view — is part of the paid Basic or Premium plan. Many popular retirement planners take a different approach and put Monte Carlo behind a paywall entirely, with no free access to it at all.

Boldin figures verified against its public pricing page, July 2026 — pricing and feature gating change, so check a provider's current page before relying on this. See our full FIManager vs Boldin comparison.

FAQ

What is a Monte Carlo retirement simulation?

A Monte Carlo retirement simulation runs your financial plan thousands of times against many different possible sequences of market returns, instead of one assumed average. The share of those runs where your money lasts through retirement is your chance of success — a probability, not a single guess.

Is Monte Carlo really free in FIManager?

Yes, with a specific scope. FIManager's free tier runs the Monte Carlo simulation on your plan and shows the headline Success Rate card, no credit card required. The detailed breakdown below that headline number — resilience scoring, downside outcomes, percentile bands, and the year-by-year trial breakdown — requires the Basic or Premium plan.

Do other retirement planners charge for Monte Carlo?

Many popular retirement planners put Monte Carlo behind a paywall or a paid tier. For example, Boldin's free Basic tier does not include Monte Carlo analysis at all — it's a PlannerPlus-only feature, priced at $12/month or $144/year (verified July 2026; see our FIManager vs Boldin comparison). Gating varies by provider, so always check a tool's current pricing page before you rely on this.

Why is a chance of success better than a single 4% rule number?

A static withdrawal-rate number like the 4% rule assumes a smooth, average return every year. Real markets don't behave that way, and the order returns arrive in — sequence-of-returns risk — matters as much as the long-run average. Monte Carlo replaces the single number with a distribution of thousands of possible sequences, so you see a probability instead of one tidy but fragile figure.

Does a high chance of success guarantee my plan will work?

No. A chance-of-success percentage is an estimate built on the assumptions you set — spending, contributions, expected returns, volatility — and the historical or modeled return data behind the simulation. It is not a guarantee or personalized financial, tax, or legal advice, and it can change as your assumptions or the modeled data change.

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