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Integrating ai into existing software is mostly an architecture problem rather than a model problem. You are adding a component that is slow, occasionally wrong and priced per use into a system built on the assumption that functions return quickly, correctly and for free. Where you place it decides how much of that assumption you break. Start Where Being Wrong Is Cheap The first integration should be somewhere a mistake is visible and reversible. Suggesting a category a user confirms. Drafting text somebody edits. Summarising a document that remains available. Ranking results a person still chooses from. What all of

Most ai api integration best practices lists are really general API advice with the word AI added. Retries, timeouts and authentication matter, and they are not what makes these integrations different. What makes them different is that the response is non-deterministic, priced per token, and capable of returning something well-formed and wrong. Everything below follows from those three. Treat The Output As Untrusted Input This is the single most important habit. A model returns text that looks like what you asked for. It has not been checked against anything. Validate the shape before using it. If you asked for JSON,

Self-hosted n8n is usually pitched on one number: no per-task billing. That number is real and it is the least interesting part of the decision, because what you are actually trading is a subscription for a set of operational obligations. We run it and we deploy it for clients. Here is what the trade actually contains. Where The Economics Genuinely Win The saving is largest for loop-heavy work. Most managed automation tools bill per task or per operation, so a workflow iterating over a thousand rows consumes a thousand units. n8n bills a workflow execution as one execution regardless of