What does manual prompt QA cost your team?
Four inputs, every assumption editable. Annual QA cost, risk-weighted regression cost, recommended plan and payback period.
Re-running prompts, eyeballing outputs, regression spreadsheets.
- Annual manual QA cost
- $432,000
- 2,880 engineer hours a year
- Risk-weighted cost of escaped regressions
- $186,480
- 7.2 escapes x $25,900
- Hours saved per sprint
- 72
- 2-week sprint, 60% of QA automated
- Payback on each month's plan
- 1 day
- Net saving $397,104 a year
AssumptionsEvery number the model uses. Edit any of them to match your incident history.
Cost per escape is the debugging time (6 h x $150) plus the downstream cost you enter for the vertical: remediation, member or borrower notices, re-review of affected cases. Defaults are starting points, not industry statistics; regulatory penalties are excluded. Monthly runs assume a fast-path gate of 20 cases per update per workflow plus 8 full-suite runs of 400 cases per workflow a month. Overage is billed per started block of 1,000 runs and extra seats at $49.
Email this breakdown
A PDF with your inputs, the assumptions above and the 12-month comparison, ready for a budget review.
A worked example you can check by hand.
A healthcare team of 15 engineers spending 20 hours each per month on manual prompt QA burns 300 hours. At $150 an hour that is $45,000 a month, before a single regression escapes.
Compare plans- Annual manual QA cost
- engineers x QA hours per week x working weeks x hourly rate
- Escaped regressions per year
- updates per week x working weeks x escape rate
- Cost per escape
- debug hours x hourly rate + vertical downstream cost
- Annual savings
- QA cost x automation share + risk cost x catch rate
- Hours saved per sprint
- engineers x QA hours per week x automation share x sprint weeks
- Payback days
- plan monthly price / (annual savings / 365), rounded up
How the calculator works
Where do the default assumptions come from?
The $150 engineer hour and 6 hours to debug one production prompt regression are the worked-example figures we use across the site. Escape rate, downstream cost per escaped regression, automation share and catch rate are conservative starting points, not published statistics. Replace them with numbers from your own incident tracker and the output updates as you type.
Why is regulatory exposure not in the risk-weighted cost?
Penalties depend on facts no calculator can know: jurisdiction, the number of affected people, whether the regulator treats the event as a pattern. Folding a possible EU AI Act or CFPB penalty into an expected value would dwarf everything else and hide the engineering argument. The cost per escape field is where you add remediation and notice costs you can actually defend.
How is the recommended plan chosen?
The model estimates monthly eval runs from your update cadence, workflow count, fast-path gate size and full-suite schedule, then prices Developer and Scale with overage and extra seats and picks the cheaper. Teams above 50 engineers, or whose Scale bill would pass the typical Enterprise floor of $35k a year, are pointed to Enterprise.
What does the payback period mean here?
The number of days of estimated savings (manual QA hours replaced plus risk-weighted regressions caught) needed to cover one month of the recommended plan. If your inputs produce no savings, the calculator says so instead of printing a figure.
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