The Hidden Costs of Bolting AI Onto Your Product.

Adding an AI feature looks cheap in the demo — a few lines of code, a slick result. But AI has a cost structure unlike normal software, and the bills that matter arrive after launch. Here is what to budget for before you commit.

Usage costs that scale with success

Most AI runs on per-request pricing. Every time a user triggers your feature, you pay. That is fine at ten users and painful at ten thousand — the more successful the feature, the bigger the bill. A good build controls this with caching, smaller models for simple tasks, and sane limits, but it has to be designed in from the start.

The accuracy tax

A demo that works 80% of the time feels magical. A product that works 80% of the time feels broken. Closing that last gap — prompt tuning, testing, guardrails, fallbacks for when the AI is unsure — is where the real engineering time goes, and it rarely shows up in early estimates.

Maintenance and drift

AI models change. Providers release new versions, deprecate old ones, and shift behavior. What worked perfectly in spring can subtly break by fall. AI features need occasional tending in a way a static form never does.

Trust: the cost you cannot see

The most expensive AI mistake is a confident wrong answer in front of a customer. One bad output can cost more trust than the feature ever earns. That is why the human-in-the-loop question matters so much.

None of this means do not build AI. It means build it with eyes open — that is the honest estimate I give clients at AbbaCodabra, including what comes after launch.