Move the sliders on the left and watch your estimated annual cost update instantly on the right — across 8 cost dimensions. No wizard, no signup required to see your number. Part of a 10-tool assessment suite — see tabs above.
Modeled at an illustrative 18%/year compounding rate. See the full math on the Compound Interest Visualizer →
Get a copy of this breakdown sent to your inbox — useful for sharing internally. No blur, no gate — this is just so you don't lose it.
How your "time lost to debt" slider compares to published bands across company stages. Full methodology and more cuts of this data live on the Benchmarks page →
The model behind this calculator combines your inputs with published industry research rather than inventing coefficients from scratch. Three data points anchor most of the math:
Each category on the left (velocity, architecture, licensing, FinOps, staffing, security, operations) uses its own formula — e.g. velocity tax = team size × loaded salary × share of time on maintenance × a test-coverage risk multiplier. The 5-year outlook compounds the current-year total at 18%/year, a rate consistent with CAST Software's research on unaddressed debt growth (see the Compound Interest Visualizer for the full breakdown by growth driver). Every number here is directional — read the disclaimer below before using it externally.
This is an educated estimate, not an audit. Figures are directional (typically ±30–40%) and derived from self-reported inputs combined with published industry benchmarks (e.g., Stripe Developer Coefficient, McKinsey, Flexera State of the Cloud, DORA/Accelerate, CISQ cost-of-poor-quality research). We have no access to your codebase, infrastructure, or vendor contracts — nothing here constitutes a technical, security, or financial audit.
Use this to frame an internal conversation about prioritization and budget, not as a precise forecast or a substitute for formal technical due diligence. Actual costs vary significantly by codebase, team composition, and business context.
This calculator is one of ten free, vendor-neutral tools for quantifying and acting on technical debt.
Rate your codebase across 10 dimensions and get benchmarked against industry standards.
Take the Assessment →Quantify the dollar cost of declining throughput and see how debt taxes your sprint output.
Open Velocity Calculator →Model the payback period and 3-year return on investment for a debt-reduction initiative.
Open ROI Calculator →Which metrics to track, how to collect them, and what good looks like across DORA and code quality.
Read the Guide →See how your debt ratio, DORA metrics, and cost profile compare by company stage and industry.
View Benchmarks →See how debt cost grows under different scenarios — from doing nothing to aggressive paydown.
Open Visualizer →Martin Fowler's quadrant — deliberate vs. inadvertent, reckless vs. prudent — and what each demands.
Explore the Framework →Four proven approaches — debt sprints, the 20% rule, Strangler Fig, and the boy scout rule — compared.
Compare Strategies →Step-by-step guide and template for presenting tech debt to non-technical stakeholders.
Build the Case →Survey the engineering team on how much of a typical week goes to maintenance, workarounds, and legacy-code friction rather than new feature work. Stripe's Developer Coefficient study puts the industry average around 33%; most teams land in the 20–35% range. If you'd rather not guess, the Assessment Scorecard walks through a structured 10-dimension evaluation instead.
60–80% is a healthy range for most codebases. Below 40% is a genuine risk signal — every change carries more hidden regression risk. Above ~90% often has diminishing returns unless you're in a safety-critical domain.
We apply an 18%/year compound growth rate to your current-year total — a rate consistent with published research (CAST Software puts unaddressed debt growth at roughly 15–25% annually). The formula is simple: each year's cost = prior year's cost × 1.18, summed across 5 years. See the Compound Interest Visualizer for a breakdown of the five factors that actually drive that growth.
Base salary plus benefits, payroll taxes, office/equipment overhead, and management burden. A common rule of thumb is 1.3–1.5× base — a $120,000 base salary becomes roughly $156,000–$180,000 fully loaded.
This calculator is built specifically for software engineering teams. It generalizes reasonably well to infrastructure, data engineering, and DevOps teams, but the underlying cost drivers (test coverage, deploy coupling, dependency freshness) don't translate cleanly to non-technical functions.
They're estimates grounded in industry research (Stripe, CISQ, McKinsey, DORA), not a measurement of your actual systems. Treat the output as a directional baseline for an internal conversation, and validate it against your own metrics wherever you can — especially the 5-year projection, which should be read as directional rather than precise.
It's folded into the architecture and security sliders (system age and known unpatched/EOL findings) rather than broken out separately, since in practice the two move together. If you want a dedicated view of dependency and coupling debt, the Metrics Guide covers how to track it directly.