Every VP of Engineering has had this meeting. The roadmap review is going fine until someone asks why the simple feature took three sprints instead of one. The engineer says something about legacy code, tight coupling, and a database schema nobody wants to touch. So the CFO nods, moves things along, and the moment passes. Nothing in that answer had a number attached to it, and finance teams do not approve budget for a feeling, however strongly it is felt.
This is the core failure mode of every technical debt conversation that happens in engineering language instead of financial language. Engineers describe debt as a quality problem. Finance teams fund quality problems reluctantly, if at all. But finance teams fund drag on growth, margin erosion, and balance-sheet risk without blinking, because those are the categories a CFO is paid to manage.
So this article gives you the number: a two-layer model for calculating the cost of technical debt in dollars. First, the engineering hours your team loses every sprint. Then four costs finance already understands, which are delayed revenue, lost customers, higher hiring bills, and a lower price at exit. You do not need new tooling to run it, because the inputs come from your sprint retros, your incident log, and your last code review.
What Independent Research Says Technical Debt Costs
Before you model your own number, it helps to know what other people have measured. Three studies are worth quoting in a finance meeting.
Deloitte’s 2026 analysis puts technical debt at 21% to 40% of an organization’s IT spending, using 30% as its working midpoint. On a $10 million annual IT budget, that is $2.1 million to $4 million a year going to debt work rather than to new capability.
McKinsey surveyed 50 CIOs at financial-services and technology companies with revenue above $1 billion. Those CIOs estimated technical debt at 20 to 40 percent of the value of their entire technology estate before depreciation. In plain terms: add up what all their systems are worth, and a fifth to two fifths of that figure is work still owed, meaning the fixes and upgrades that have to happen before those systems are in the shape the business actually needs. The same CIOs reported that 10 to 20 percent of the budget set aside for new products gets diverted into debt work before a single feature ships, and 60 percent said the problem had grown noticeably over the previous three years.
At national scale, the Consortium for Information and Software Quality estimated accumulated technical debt across the US software economy at roughly $1.52 trillion, part of a $2.41 trillion total cost of poor software quality.
Use these figures as a sanity check on your own model, not as a substitute for it. If your number comes out far below 20 percent of engineering spend, your inputs are probably too optimistic, and your CFO will notice that before you do. The metrics that feed those inputs are covered in our guide to measuring technical debt.
The CFO-Ready Cost Model
The model has two layers. The first is a direct cost you can calculate from data you already have. The second is a set of indirect costs that need a few reasonable estimates, which is fine, because CFOs are comfortable with estimated ranges as long as the methodology is transparent. That is how they already treat depreciation, bad-debt reserves, and most of the rest of the balance sheet.
Layer 1: The Direct Cost You Can Calculate Today
Direct cost = hours lost per sprint to debt × loaded engineering cost per hour × sprints per year
- Hours lost per sprint. Pull this from sprint retros, time tracking, or a two-week survey asking what share of story points went to debt-driven rework rather than net-new work. If you have no internal data yet, start at the conservative end of the research range, 20 percent, and replace it with your own figure within a sprint or two. A number from your own retros will always survive scrutiny better than an industry average.
- Loaded engineering cost per hour. Loaded cost is everything one engineer costs the company in a year, not just what lands in their bank account: base salary plus benefits, payroll tax, equipment, software licences, recruitment, and a share of office and admin overhead. Finance already calculates this for every role, so ask for their figure rather than estimating it yourself.
- Sprints per year. Typically 24 to 26 on a two-week cadence.
Here is the calculation step by step, for a team of ten engineers:
- Team capacity per sprint. Ten engineers working a two-week sprint gives you 800 hours in total, which is 10 people × 80 hours each.
- Cost per hour. Say the loaded cost is $145,000 per engineer per year. Divide that by 2,080 working hours, which is the standard full-time year of 40 hours a week × 52 weeks, and you get about $70 an hour.
- Hours lost to debt. At 35 percent, 280 of those 800 hours go to debt work every sprint.
- Cost of one sprint. 280 hours × $70 = $19,600.
- Cost of the year. $19,600 × 26 sprints = about $510,000.
That $510,000 is engineering time spent servicing debt instead of building product, and it has never appeared on a single line item. It usually gets a skeptical CFO leaning forward, because it is a real operating expense nobody has been tracking. To make the hours input harder to argue with before the meeting, tighten how your team defines and tracks code quality.
Layer 2: The Indirect Costs That Make Finance Sit Up
Indirect costs are what technical debt does to the rest of the business: revenue that arrives late, customers who leave after one outage too many, salaries you overpay to staff an ageing system.
Delayed features
Feature value per week × weeks of delay caused by debt
Roadmap projections, delivery history
Churn from incidents
Customers lost per major incident × average customer lifetime value
Support tickets, incident log, churn reports
Hiring premium
Extra time-to-hire and offer decline rate on legacy roles vs modern roles
Recruiting funnel data
Valuation discount
Likely remediation quote from a buyer’s diligence team
Prior diligence findings, audit reports
Delayed features. Take the feature’s projected revenue or cost saving, divide by its planned timeline to get a value per week, then multiply by the delay attributable to debt-driven rework. A feature worth $40,000 a month that ships eight weeks late because of legacy constraints is about $80,000 in deferred value, entirely separate from the engineering hours burned getting there.
Churn from incidents. Use your change failure rate, the share of deployments that trigger an incident, rollback, or hotfix, alongside support and churn data to estimate how many customers you lose per major incident, then multiply by lifetime value. Most teams find this number larger than expected, because incidents rarely get traced back to the debt that caused them.
Hiring premium. If your system needs an old framework version, a bespoke internal tool, or a deprecated language, compare time-to-hire and offer acceptance on those roles against your modern stack. The penalty is real for two reasons: the pool of engineers who still know an outdated technology keeps shrinking every year, and the ones who do know it often turn the job down because taking it narrows their next career move. You pay for that twice, in a longer vacancy and in a higher offer.
Valuation discount. If an exit, acquisition, or fundraise is anywhere on the horizon, model a conservative discount based on the remediation cost a buyer’s technical diligence team would quote. This is the hardest of the four to pin down, but even a rough range moves debt from an engineering complaint to a shareholder-value issue. Our M&A technical due diligence checklist shows what those teams actually look for, which makes the estimate much less speculative.
Add the four indirect estimates to the direct cost and you have one annualized number: the cost of technical debt for your organization, in dollars rather than story points. What you do with it is the next decision, and having the number is what makes that decision possible. Moving routine upkeep such as dependency updates, regression coverage, and security patching onto a software maintenance contract is one common route, because it converts an unpredictable cost into a fixed line item finance can forecast.
Where AI-Assisted Code Changes the Math
If your team shipped meaningfully more code this year with the same headcount, your debt-hours figure from last year is already out of date. AI-assisted development moves the bottleneck from writing code to reviewing it, and code that clears review too easily has a habit of coming back as rework a quarter later. We covered how that debt builds up in our piece on technical debt in AI coding.
Two changes keep your cost model accurate. Re-measure the debt-hours figure from Layer 1 quarterly rather than annually, and track it alongside the share of merged code that was AI-assisted. If those two numbers rise together, you have found your fastest-growing source of debt while it is still cheap to deal with. Teams already using AI review tooling should also check what those tools genuinely catch, which is what we tested in our review of Claude Code for code review.
Where a codebase has already collected a backlog of AI-generated code nobody fully owns, a focused vibe code cleanup engagement is usually cheaper than absorbing that work into normal sprint capacity, because the scope is bounded and can be priced against the number you just calculated.
Why Teams Bring Redwerk Into This Conversation
Redwerk has been building software since 2005, so we have watched a lot of debt accumulate and been hired to clean up a fair amount of it. We build complex products from the ground up, audit systems nobody has documented in years, and refactor legacy projects that other teams walked away from. More recently we have added AI-powered solutions and AI-assisted development and testing to that work. The engineering fundamentals and security practices behind it are the same ones we have applied for clients across North America and Europe, including Fortune 500 companies such as Siemens, J.B. Hunt, and Universal Music Group. They now cover the newest sources of debt as well.
The savings often start smaller and sooner than people expect. When Adoorabelle, an Atlanta real estate marketplace, brought us in, the founder was non-technical, locked in with a previous vendor, and had no documentation to show an investor. Our audit of their real estate app surfaced 80 issues ranging from hardcoded secrets to critical structural flaws, cut $300 a month of unnecessary API subscription costs, which is $3,600 a year recovered from the operating budget on its own, moved their AWS infrastructure closer to their users, and added 24/7 observability with Sentry, Prometheus, and Grafana. They finished with a complete functional specification, full vendor independence, and something to show investors.
That is the pattern worth copying: find out what the debt actually costs, in dollars, before deciding how much to spend on it. If you want that number produced by people who will also tell you plainly which parts are not worth fixing, start with a software development audit.
FAQ
How much does technical debt cost?
There is no single industry figure, because it depends on team size, engineering cost, and how debt-heavy the codebase is. Independent research gives useful anchors: Deloitte puts technical debt at 21% to 40% of IT spending, and McKinsey found CIOs estimating it at 20 to 40 percent of their total technology estate value. Applying your own loaded engineering cost to your team’s debt-hours-per-sprint is the only way to get a company-specific number rather than an industry average.
How do you estimate the long-term cost of technical debt?
Combine a direct cost, which is hours lost per sprint to debt multiplied by loaded engineering cost and sprints per year, with four indirect costs: delayed-feature opportunity cost, churn tied to incident frequency, hiring premiums for legacy-stack talent, and any valuation discount that would surface in technical due diligence. Because debt compounds, model the total annually and revisit the inputs quarterly rather than treating it as a one-time calculation.
How do companies calculate the cost of technical debt?
Most mature engineering organizations track a debt-hours-per-sprint figure from retros or time-tracking data, apply their finance team’s loaded cost-per-engineer number, and layer in delivery metrics such as change failure rate and recovery time to connect debt to incident and churn costs. The output is usually expressed as an annualized dollar figure alongside a percentage of total engineering capacity.
Why do CFOs reject technical debt budget requests?
Most requests are framed in engineering terms, such as code quality, refactoring, and architecture, rather than financial terms like dollars, opportunity cost, and risk exposure. A request framed as “$510,000 in annual engineering time plus $600,000 in projected churn and delayed revenue” competes for budget the same way any other capital allocation decision does. A request framed as “the code needs cleanup” generally does not.
See how Redwerk took over a struggling fitness app from another vendor, cleaned up the inherited technical debt, and helped Pridefit grow subscriptions by 45%
