BudgetingforYourFirstAIFeature:WhatItCoststoAddLLMCapabilities

API usage cost from an LLM provider scales with how much text you send and receive, and it's billed by the provider directly — this is usage-based and grows with your product's traffic, which means it needs to be modeled into your unit economics, not just your initial build budget.

Integration engineering cost is separate: designing prompts that reliably produce useful output, handling failures gracefully when the model returns something unexpected, managing rate limits, and building the surrounding product experience around the AI feature. This is usually the larger and more variable of the two costs, especially for a first implementation.

A simple, well-scoped AI feature — summarizing a document, classifying incoming text into categories, drafting a first version of some content — is genuinely inexpensive to build well. A classification model built for detecting fake news, for example, reached 94% accuracy as a scoped, well-defined ML task — that kind of focused, well-bounded problem is exactly where AI integration cost stays reasonable.

Cost climbs when the feature needs to feel deeply integrated — real-time streaming responses, context that persists across a conversation, or output that has to be reliably structured for your app to act on programmatically. None of that is unreasonable to build, but it's meaningfully more engineering than a single API call.

Fixed-price integration work for a scoped AI feature starts from USD 500 and scales with how deeply the feature needs to be woven into your product. Hourly billing runs USD 25–45/hr with a 10-hour minimum, which suits AI features where the right prompt design and failure handling take some iteration to get right. Budget API usage cost separately, and model it against expected traffic before committing to a pricing plan for your own product.