Reference Card / Benchmarks|Updated 28 Jun 2026

Technical debt benchmarks 2026: where does your team stand?

All current top-10 benchmark results are 2019-2023. This page compiles the most recent figures from six independent data sources into a single reference table. A 2026-dated compilation beats competitors on freshness.

Sources: CISQ 2022, CAST 2025 (Coding in the Red), DORA 2024, DX 2024, McKinsey 2020-23

Tool 01 / Percentile

Where does your team stand?

Enter your debt drag percentage to see your performance tier.

Your Debt Drag %

28%

Performance Tier: Medium

Typical range. Debt at this level measurably slows feature delivery and raises incident load.

Reference Card 02

Cross-source benchmark table

Six named studies, side by side. Use for directional benchmarking, not precise comparison.

SourceYearMetricEliteAverageStruggling
DORA2024% time on unplanned work<8%20-30%>45%
McKinsey2020-23% of IT budget on debt<10%10-20%~40%
Stripe2018% time on maintenance<15%33% (debt) / 42% (all)>50%
SonarQube SQALERating gridTechnical debt ratio<5% (A)11-20% (C)>50% (E)
DX2024Developer-reported drag<10%25-35%>50%

Note: metrics are not identical across sources. Use the table for directional benchmarking. See /case-studies for full methodology notes per study.

Adjustment Layer 01

Company-size adjustments

Startup (1-10 eng)

10-25% drag

High debt tolerance is appropriate early. Speed matters more than cleanliness. Watch for debt becoming structural as you scale.

Scale-up (10-50 eng)

15-35% drag

The most dangerous zone. Teams grow faster than code quality norms spread. Deliberate debt culture investment needed.

Enterprise (50+ eng)

20-40% drag

Larger codebases accumulate more legacy debt. Formal debt management programmes become necessary.

Adjustment Layer 02

Industry adjustments

IndustryTypical drag rangeKey driver
Fintech / Banking10-20%Regulatory pressure forces quality investment. Test debt is high but code debt is low.
SaaS / B2B Software20-35%Velocity-driven culture. Feature speed prioritised over debt paydown.
E-commerce15-30%Frontend debt accumulates fastest. Backend payment systems are heavily tested.
Gaming25-45%Shipped-then-forgotten pattern. Post-launch patches create high debt. Live service games are exceptions.
Embedded / IoT5-15%Long product lifecycles enforce quality. High cost of field failure creates quality investment.
Healthcare / Life Sciences8-18%FDA / HIPAA compliance forces documentation and test discipline.

Directional drag ranges synthesised from the cross-source table above and engineering-org field observation, not a single published per-industry dataset. Use for orientation, not precise comparison.

Field Note

Self-reporting caveat

Every benchmark in this table comes from self-reporting (developer surveys) or proxy measurement (DORA metrics). Real medians are probably worse than reported for two reasons: (1) teams with the worst debt are least likely to participate in benchmarking surveys, and (2) engineers tend to underestimate the debt drag they have normalised to.

CAST Software's code analysis is the exception. Its Coding in the Red 2025 study analyses actual production code rather than asking engineers to report. That methodology is among the most objective available, though it too has sample bias (organisations that run CAST's analysis tools).

Continue Reading

Five Measurement Methods->Six-Lever Reduction Playbook->Canonical Studies with Methodology Notes->TechnicalDebtCost.com: Deep Industry Statistics->

Sources

  1. Google DORA. State of DevOps Report 2024. 36,000+ respondents.
  2. McKinsey Digital. Tech Debt: Reclaiming Tech Equity. 2020 (reaffirmed 2023).
  3. Stripe. The Developer Coefficient. 2018. 1,000 developers, 5 countries.
  4. CAST Software. Coding in the Red: The State of Global Technical Debt, 2025. 47,000 applications across 3,000 companies, 17 countries.
  5. DX. Developer Experience Index 2024. getdx.com.
  6. CISQ. Cost of Poor Software Quality in the US: A 2022 Report.