The Real Cost of Maintenance

Stripe’s 2018 Developer Coefficient survey remains the most widely cited benchmark for maintenance burden: teams reported losing 42% of capacity to toil, bad code, and ongoing upkeep. This calculator uses that signal as the global average and contrasts it with the 33% level observed among the strongest performers. By entering headcount and fully-loaded salary, engineering and finance leaders can align on the real monthly and annual cost of carrying that maintenance load.

The output is not a guilt-inducing vanity metric. It quantifies the opportunity cost of under-investing in the foundations. Use the delta between 42% and 33% to show how many roadmap slots you can recover and what that unlocks for committed work.

Reclaiming nine percentage points of engineering time is roughly the same as adding one extra teammate for every eleven engineers you already have. When you show stakeholders that reclaimed capacity in a roadmap scenario, the debt conversation becomes a planning exercise—not a plea for permission.

1. Export your calculator results so finance sees the budget impact.
2. Drop the savings into the ScopeCone capacity planner to baseline discovery vs. delivery crews.
3. Share committed/target/stretch scenarios that prove how a steady maintenance cadence keeps roadmap promises realistic.

Start with the capacity-led roadmapping guide to calibrate your first model. It takes ten minutes with the demo data.

Load the sample data to see a realistic scenario for a late-stage SaaS team. With 18 engineers at an average fully-loaded salary of $145k, the model estimates a $912k annual maintenance budget at the global 42% benchmark. Best-in-class teams would trim that to roughly $717k, unlocking nearly $200k per year that could be redeployed into roadmap work.

Toggle the SQALE view to layer in static-analysis violations and an hourly remediation rate. That extension translates code-quality hotspots into a backlog of hours and dollars so you can discuss remediation plans with finance in their own language.

SQALE provides the formula for estimating remediation effort, but the default hour multipliers ship from SonarQube, not academia. Treat them as starting points: calibrate the coefficients with your historical fix data, adjust for language and architecture differences, and socialise the assumptions with the teams doing the work.

The calculator calls this out explicitly so stakeholders understand that remediation numbers are directional, not gospel. Use them to compare “what if” scenarios and to frame the trade-off between maintenance budgets and new product capacity.