FinToolSuite

Technical Debt Calculator

Updated April 17, 2026 · Financial Health · Educational use only ·

Cost of technical debt.

Calculate annual cost of technical debt from team size, salaries, debt time %, and velocity drag. Enter engineering team size and see the result instantly.

What this tool does

This tool calculates annual tech debt cost from engineering team size, salaries, debt time, and velocity drag.


Enter Values

Formula Used
Team
Salary
Debt %
Drag %

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Disclaimer

Results are estimates for educational purposes only. They do not constitute financial advice. Consult a qualified professional before making financial decisions.

Technical debt costs engineering teams in two ways: direct time spent maintaining/fixing legacy code (typically 15-30% of engineering time), and velocity drag from working around technical limitations (10-25% slower feature development). Combined, tech debt can consume 25-50% of engineering capacity in mature codebases.

10 engineers × 80k avg salary = 800k total eng cost. 20% direct debt time = 160k. 15% velocity drag = 120k. Total tech debt cost: 280k/year or 35% of engineering budget. That's the amount you'd recover by eliminating all tech debt (impossible in practice - but 50% reduction is achievable with focused effort).

Tech debt is like financial debt: manageable when small, crushing when large. The interest rate on tech debt is the velocity drag - every sprint, existing debt makes new features take longer. Best organisations budget 15-20% of engineering time for deliberate debt reduction, targeting highest-drag areas first.

Run it with sensible defaults

Using engineering team size of 10, avg engineer salary of 80,000, tech debt time of 20%, velocity drag of 15%, the calculation works out to 280,000.00. Nudge the inputs toward your own situation and the output recalculates instantly. The defaults are meant as a starting point, not a recommendation.

The levers in this calculation

The inputs — Engineering Team Size, Avg Engineer Salary, Tech Debt Time %, and Velocity Drag % — do not pull with equal force. Not every input has equal weight. Flip one at a time toward extreme values to feel which ones move the needle most for your situation.

How the math works

Direct cost = team × salary × debt time %. Velocity cost = team × salary × drag %. Total = sum. The working is transparent — you can verify every step yourself in the formula section below. No black box, no opaque "proprietary model".

What the score tells you

Headline financial numbers — income, savings, debt — each tell part of the story. This calculation stitches several together into a single read you can track over time. The value is in the direction, not the absolute number.

What this doesn't capture

The score is a composite of the inputs you provide. Life context — job security, family obligations, health, housing — doesn't appear in the math but shapes the real picture. Use the number as a prompt, not a verdict.

Example Scenario

10 × £80,000 £ × (20% + 15%) = $280,000.00.

Inputs

Engineering Team Size:10
Avg Engineer Salary:80,000 £
Tech Debt Time %:20
Velocity Drag %:15
Expected Result$280,000.00

This example uses typical values for illustration. Adjust the inputs above to match a specific situation and see how the result changes.

Sources & Methodology

Methodology

Direct cost = team × salary × debt time %. Velocity cost = team × salary × drag %. Total = sum.

Frequently Asked Questions

How to measure tech debt time?
Track sprint allocation: % of points/hours on debt vs features. Most teams undercount by 30-50% because small 'just fix this quickly' tasks add up. Better: tag all Jira tickets as feature/debt/bug and measure quarterly.
Is 20% debt time normal?
Yes, for mature codebases. New startups: 5-10%. 3-5 year old products: 15-25%. Legacy systems: 30-50%. Above 30% typically signals need for major refactoring or rewrite. Industry average across all software: 23% (Stripe research).
How to reduce velocity drag?
Prioritize highest-drag areas (usually: test infrastructure, deployment pipeline, data models, core libraries). Focus 2-3 sprints on one area for visible improvement rather than spreading thin. Measure: feature velocity before and after to prove impact.
Should I rewrite?
Usually no. Rewrites take 2-3x longer than estimated and often recreate similar debt in new code. Better: strangler fig pattern (replace piece-by-piece), targeted refactoring of highest-pain areas, invest in automated testing to enable safer changes.

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