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Estimate Your QA Costs & ROI in Minutes
Understand the financial impact of your QA strategy as you scale with AI. Make informed decisions based on data, not guesswork.
4:1
Average QA ROI ratio
30%
Of dev time spent on QA tasks
$50K
Average cost per critical bug
25%
More bugs with AI coding tools
Engineering Team
AI & Development Practices
Product & Quality Requirements
QA Strategy
Testing Volume & Process
Number of new tests created monthly (scales with team size)
Quality Metrics
Percentage of bugs that escape to production
Estimated Annual QA Cost
$0
Required QA Headcount
0 FTEs
Mix: 0 Manual, 0 Automation/SDET
Estimated ROI
0% - 0%
Expected: 0%
Bug Cost Savings / Year
$0
From earlier defect detection
Time-to-Market Gain
~0 Weeks/Year
Faster release cycles
Calculation Methodology
Results are estimates based on industry benchmarks and may vary based on your specific context, tool choices, and team efficiency.
Team & Salary Costs
Base salaries: $90K (Manual QA), $120K (Automation QA/SDET) annually, adjusted for location (0.45x to 1.5x multiplier). Includes 35% benefits overhead, 18% annual turnover, and $18K recruitment costs per hire.
Product Complexity
Simple products (basic CRUD): 30% less QA needed. Complex (multi-tenant, integrations): 30% more. Critical (healthcare, fintech): 80% more. Also determines bug severity distribution.
AI Coding Assistants
AI increases development velocity but introduces quality issues: 40% more code duplication, 25% more technical debt, 30% more code churn at full adoption. Net result: +18% QA workload.
Test Automation
Non-linear efficiency gains with diminishing returns. At 30% coverage: ~12% headcount reduction. At 90%: ~58% reduction. Automation maturity affects efficiency factor.
Ready to Optimize Your QA Investment?
Desplega.ai uses AI to write stable, deterministic E2E tests that reduce maintenance burden and improve test reliability.
Frequently Asked Questions
How is QA headcount calculated?▼
We use industry-standard ratios adjusted for your specific context. Base ratio is 1 QA per 4-6 developers, modified by product complexity, automation coverage, release frequency, and regulatory requirements. AI adoption increases QA needs due to additional code churn and quality issues.
Why does AI adoption increase QA costs?▼
Research shows AI coding assistants boost velocity but introduce quality issues: 40% more code duplication, 25% more technical debt, and 30% more code churn at high adoption. Junior developers benefit more from AI (35% productivity gain) but introduce more defects, while senior developers see smaller gains (8%) but higher quality code.
What is the ROI range based on?▼
ROI is calculated from bug prevention savings, time-to-market gains, and maintenance efficiency. The range reflects statistical uncertainty: bug costs vary -30% to +50%, time-to-market value ±20%, and catch rates ±15%. This provides realistic high and low bounds.
How does automation coverage affect costs?▼
Automation provides non-linear efficiency gains with diminishing returns. At 30% coverage you see ~12% headcount reduction, while 90% coverage yields ~58% reduction. However, automation maturity matters—low maturity yields 85% efficiency while high maturity (self-healing tests) can exceed 100%.
What makes desplega.ai different?▼
Desplega.ai offers competitive offshore rates (0.6x location factor) plus 25% operational efficiency, 50% shared tool license savings, and 35% infrastructure optimization. The platform uses AI to write stable, deterministic tests that reduce maintenance burden.