A bug in production on a critical path comes at a triple cost: lost revenue, teams mobilized on an emergency basis, and damaged trust. And the manual tests meant to prevent it also come at a cost—one that is incurred with every deployment to production.
This calculator provides you with an initial estimate based on your own figures.
Before discussing the cost of an automated testing solution, take a look at what the status quo is already costing you.
Hours of work are spent over and over again with every production deployment, by teams that have better things to do. This cost is incurred with every release.
In a critical purchase funnel or customer journey, every hour of downtime costs the company revenue and erodes trust.
Due to a lack of time, coverage is falling behind as the platform evolves. The risk is growing silently.
"Finding just one bug is enough to make the solution pay for itself for an entire year!"
Anthony Cornevin, E-commerce Platform Manager, VertbaudetAdjust the sliders. All calculations are performed in your browser; no data is sent.
This is an approximate estimate. It does not take into account the impact on brand image, permanently lost shopping carts, or the time spent making urgent corrections.
Calculation assumption: hourly downtime cost = monthly revenue ÷ 730 hours. Typical incident = 24 hours in the sales cycle.
These figures are a preliminary estimate. Please provide your contact information, and an expert— Mr Suricate to you with a more detailed analysis tailored to your specific situation.
Two factors: the cost of manual testing (hours per year × hourly rate) and a typical 24-hour incident in the sales funnel (monthly revenue ÷ 730 hours × 24). This estimate is intentionally conservative: it assumes only one incident per year, without factoring in the impact on brand reputation or permanently lost shopping carts.
This is an initial assessment based on your own data—not a quote. The customized analysis refines the calculation based on your actual scope: critical workflows, web and mobile platforms, and deployment frequency.
It depends on the scope of the testing and the level of support required—ranging from self-service tools to full-service management of your tests. As a point of reference: at Vertbaudet, detecting just one bug was enough to make the solution pay for itself over an entire year.