The real cost of an AI feature
The model's bill is rarely the biggest line. Where the money and time actually go when you add AI to a product.
When teams estimate an AI feature, they usually price the model calls and stop there. That number is real, but it is rarely the one that surprises them later.
Four costs that hide
- Evaluation: building and maintaining a set of test questions and answers, and running them on every change.
- Monitoring: logging what the feature said, spotting bad answers and reviewing a sample regularly.
- Prompt and tool maintenance: models change, your data changes, and the feature needs tuning to keep its quality.
- Support: every new capability produces new questions, and some of them land with your team.
Costs that grow with success
Traditional software costs roughly the same whether ten people use a feature or ten thousand. AI features don't: every use costs something. That is fine when usage is valuable, and painful when one heavy user or a loop gone wrong runs up the bill. Set per-user and per-day limits before launch, and alerts before the limits.
“Price the feature you'll be running in a year, not the demo you're building this month.”
Plan for version two
The first version of an AI feature is a research instrument. Real users ask things nobody predicted, and the logs show exactly what. The most useful line in the budget is the one reserved for acting on what that first month teaches you.
Written by the team at Bytelights, a product and AI studio in India.