AI Coding Tool ROI Calculator
Estimate the financial return on investment of adopting an AI coding assistant (e.g., GitHub Copilot, Cursor, Tabnine) for your software development team.
Formulas Used
Hourly Rate = Annual Salary ÷ Billable Hours per Year
Productivity Value per Developer = Hourly Rate × Billable Hours × Productivity Gain %
Total Productivity Value = Productivity Value per Developer × Number of Developers
Annual Tool Cost = Tool Cost per Developer per Month × 12 × Number of Developers
Onboarding Cost = Hourly Rate × Onboarding Hours × Number of Developers
Total First-Year Cost = Annual Tool Cost + Onboarding Cost
Net Benefit (Year 1) = Total Productivity Value − Total First-Year Cost
ROI % = (Net Benefit ÷ Total First-Year Cost) × 100
Payback Period = Onboarding Cost ÷ Monthly Net Benefit
3-Year Net Benefit = (Total Productivity Value × 3) − (Annual Tool Cost × 3) − Onboarding Cost
Assumptions & References
- Productivity gain range (20–55%): GitHub's 2022 study found developers completed tasks 55% faster with Copilot. McKinsey (2023) reports 20–45% productivity improvements for software tasks. A conservative default of 25% is used.
- Billable hours: Assumes ~1,800 productive hours/year (standard for a 40-hr week minus holidays, PTO, and meetings).
- Productivity value methodology: Gains are valued at the developer's fully-loaded hourly rate — representing the cost equivalent of additional output, not necessarily direct revenue.
- Onboarding cost: Modeled as a one-time salary cost for training time; does not include lost productivity during ramp-up beyond the specified hours.
- Tool cost examples: GitHub Copilot Business ~$19/dev/month; Cursor Pro ~$20/dev/month; Tabnine Enterprise ~$15/dev/month (2024 pricing).
- Not included: Infrastructure costs, reduced bug rates, faster onboarding of new hires, or morale/retention benefits — all of which may further improve actual ROI.
- References: GitHub (2022) "Research: quantifying GitHub Copilot's impact"; McKinsey & Company (2023) "The economic potential of generative AI"; NBER Working Paper No. 31161 (Brynjolfsson et al., 2023).