How to integrate ChatGPT into an iOS app

Short answer

You integrate ChatGPT into an iOS app by calling OpenAI’s API, and the key rule is to route those calls through your own backend server rather than from the app, so your API key is never exposed. The app sends the user’s text, your server calls OpenAI, and the reply comes back to the screen. The things that make or break it are controlling cost, since the API charges per use, handling the wait and errors gracefully, and moderating AI content to satisfy Apple’s rules.

What integrating ChatGPT means

Before the how, it helps to be clear on what integrating ChatGPT actually involves, because the name can mislead. You are not putting the ChatGPT app inside your app; you are connecting your app to OpenAI’s API, the same underlying service that powers ChatGPT. Your app sends some text, called a prompt, to that service, and receives back generated text in reply. Everything you build, whether a chat assistant, a summarizer, or a writing helper, is a variation on that one exchange: send text, get text.

That simplicity is real, but it hides a few decisions that determine whether the feature is safe, affordable, and pleasant to use. Where the API call happens, how you pay for it, how you handle the wait and the occasional failure, and how you keep the content appropriate are the parts that separate a solid integration from a fragile one. The rest of this piece walks through those decisions in order, starting with what you can actually build, then the single rule that matters most, and then the practical concerns of cost, speed, and Apple’s rules.

What you can build with it

ChatGPT integration opens up a familiar set of features, and it helps to pick which one you actually need rather than adding AI for its own sake. The table shows the common uses.

Use case	What it does
Chat assistant	Answers users in natural language
Summaries	Condenses long text into short points
Writing help	Drafts, rewrites, or improves text
Questions and answers	Answers questions about your content

Each of these is the same send-text, get-text exchange dressed up for a purpose, which is why the integration work is similar across them. The important choice is which one adds genuine value to your app, because a well-aimed AI feature that solves a real user need is worth far more than a generic chatbot bolted on because AI is popular. Decide the job first, then the rest of the integration follows. A focused feature is also cheaper and easier to control than an open-ended one, which matters for the cost and safety concerns below.

The one rule: keep your API key on a server

If there is a single rule to take from this, it is that your OpenAI API key must never live inside the app. The key is like a password that lets anyone use OpenAI and charge it to you, and anything shipped inside an app can be pulled out by a determined person. A key placed in the app can therefore be stolen and used to run up your bill or abuse the service, which is a serious and common mistake. The key belongs only on your own server.

The safe pattern is straightforward: the app talks to your backend, and your backend talks to OpenAI. When a user does something that needs the AI, the app sends the request to your server, your server adds the secret key privately and calls OpenAI, and the reply travels back to the app. The key never leaves your server, so it cannot be extracted from the app. This is why a ChatGPT feature needs a backend, even a small one, and our guide to the cost of servers for an app covers what running that backend involves. Skipping the server to save time is how apps leak keys and lose control of their spending.

Cost, latency, and errors

Three practical concerns shape how a ChatGPT feature feels and what it costs. The first is cost: OpenAI charges per use, based on how much text goes back and forth, so your bill scales with usage rather than being fixed. You keep it predictable by setting sensible limits, keeping prompts efficient, and caching answers to repeated questions where it makes sense. A feature designed with cost in mind stays affordable as it grows; one that sends huge prompts on every tap can get expensive fast.

The second is latency. AI responses take a moment to generate, longer than users expect from a normal tap, so the app must handle the wait well, by showing a loading state or streaming the reply as it arrives rather than freezing. The third is errors: the network can fail, the service can be busy, and requests can hit rate limits, so the app needs to fail gracefully with a clear message instead of breaking. Handling these three well is what makes an AI feature feel reliable rather than flaky, and it is where a careful build clearly beats a rushed one.

A concrete example ties them together. Say your app summarizes a long article the user pastes in. A careful build caps how much text it sends, so a huge paste does not become a huge bill, shows a brief animated state while the summary generates so the wait feels intentional, and if OpenAI is briefly unavailable, tells the user to try again instead of showing a blank screen. The same feature built carelessly would send the whole text every time, freeze during the wait, and break on the first hiccup. The AI behind them is identical; the experience and the cost are worlds apart, which is exactly why this part is where the real work lives. Our overview of what it costs to build an app puts this effort in the wider picture.

Since iOS 26 there is also a way to make some of that cost disappear entirely. Apple’s Foundation Models framework gives your app direct access to the on-device model behind Apple Intelligence, with no API key, no per-request bill, and no network dependency, and it works offline. It is not a ChatGPT replacement for heavy reasoning, but for focused tasks like summarizing, tagging, or extracting structure from text, routing those requests on-device and saving the OpenAI call for the hard cases cuts the running cost of an AI feature substantially.

Apple’s rules on AI content

Apple allows AI features, but it expects the content to be handled responsibly, and ignoring this can cause a rejection. The core expectation, set out in Apple’s review guidelines, is that AI-generated and user-submitted content be moderated. If users can type anything and the AI responds, or if the AI produces content that other people see, you are responsible for filtering harmful or inappropriate output and, for user-generated content, providing a way to report it and a means to block abuse.

In practice this means building moderation into the feature rather than treating it as an afterthought. OpenAI and other tools offer ways to screen content, and combined with your own limits they keep the feature within Apple’s expectations. The honest point is that adding AI does not remove your responsibility for what the app shows; it adds to it, because now your app can generate text you did not write word for word. An app that adds a thoughtful, well-moderated AI feature passes review comfortably, while one that ships an unfiltered pipe to a language model invites both rejection and real-world problems. Plan the safeguards in from the start.

The integration checklist

Bringing the pieces together, a clean ChatGPT integration follows the checklist below, in roughly this order.

Step	What to do
Use a backend	Route all OpenAI calls through your own server
Protect the key	Keep the API key off the device, server only
Control cost	Set limits, keep prompts lean, cache where sensible
Handle the wait	Show a loading state or stream the reply
Moderate content	Filter harmful output and follow Apple’s rules

The order reflects dependencies: the backend comes first because everything else, key safety, cost control, and moderation, runs through it. With the server in place, protecting the key and managing cost are configuration rather than afterthoughts, and good handling of latency and moderation turns a working feature into a trustworthy one. The honest limitation is that a real ChatGPT integration is genuine development work with a backend behind it, not a snippet you paste in, and it must respect both cost and Apple’s content rules. If you want a team to add a secure, well-designed AI feature to your app, published under your own Apple developer account, book a free call.