The Cost to Add an AI Feature to an App, Explained

AI IMPLEMENTATION

The cost to add an AI feature is rarely the model. Data readiness, integration, and the ongoing work of keeping it accurate drive the number, which is where most budgets quietly overrun.

The four things you are actually paying for

An AI feature has four cost drivers: preparing the data, integrating the capability into your app, the model usage itself, and ongoing upkeep. The model license is usually the smallest of the four.

Teams that price only the model are the ones surprised later. The cost to add an AI feature lives mostly in the work around the model, not the model.

Why budgets overrun

Compute is the first surprise. Industry research found that most enterprises underestimate what AI actually requires, and a majority exceeded their infrastructure estimates by 40 percent or more, largely from underestimating compute for training and inference.

Upkeep is the second. Accuracy drifts about 15 percent within a year without retraining, so a feature priced as a one time build quietly becomes a running cost. Naming that early is the difference between a budget that holds and one that does not.

How to keep the number proportional

Four habits keep cost tied to value. Start with off the shelf for commodity capabilities. Build custom only where it differentiates the product. Scope the smallest surface that works. Price the upkeep in from the start rather than discovering it later.

Those habits also decide the bigger question of whether to go custom or off the shelf in the first place, and whether you are adding AI to an existing app or building fresh.

How Tepia scopes AI cost

Tepia gives a real range by pricing all four drivers, not just the build, and by recommending off the shelf where it is genuinely good enough. Thirteen years of shipping software means fewer surprises once the work starts, which is the whole point of an honest estimate.