LLMIntegrationDecisionsNon-TechnicalFoundersShouldUnderstand

Every call to an LLM API costs money, scaling with how much text is sent and received, and billed directly by the provider. This is fundamentally different from most software costs, which are largely fixed once built — LLM costs grow with your product's actual usage, which means they need to be modeled into your pricing and unit economics from the start, not discovered after launch.

Ask your developer to help you understand the expected cost per user interaction, and sanity-check that against what you're charging (or plan to charge) — this is a business decision as much as a technical one.

Language models don't produce perfectly reliable output every time — they can occasionally generate something incorrect, oddly formatted, or irrelevant. This is a real, known characteristic of the technology, not a bug in your specific implementation. The decision you need to weigh in on: how much human review or fallback handling does your product need around AI-generated output, given what happens if it's occasionally wrong.

For a low-stakes feature (a first draft the user will edit anyway), lighter review is reasonable. For a higher-stakes feature (something acted on automatically without human review), more careful fallback handling is worth the extra engineering investment.

Most successful AI features are actually a small AI component embedded in a much larger, conventional software product — the AI handles one specific task, while the surrounding product handles everything else (accounts, data storage, the rest of the user experience) the normal way. Understanding this helps you correctly scope how much of your build is genuinely "the AI part" versus standard product engineering, which is useful for realistic budgeting.