How much does it cost to build an AI-powered app?

Building the app itself costs about the same as a normal MVP — AI integration is standard backend work now. The real difference is operational: cloud AI APIs bill per use, so costs grow with usage. Budget for the API bill at your target scale, not at launch scale.

Should my app run AI on-device or in the cloud?

On-device wins for privacy, offline use and zero marginal cost, but only supports smaller models. Cloud gives full capability but every request costs money and sends user data to a server. Most good apps use a hybrid; privacy-sensitive apps should lean on-device.

Do Apple and Google restrict AI apps?

They enforce privacy disclosure strictly. Apple's privacy labels and Google's data safety section must accurately declare that user content is sent to AI services. Misdeclaring is a rejection or a takedown. Generated content also has to comply with normal content policies.

How do I stop AI API costs from exploding?

Cache identical requests, cap free-tier usage per user, use the smallest model each task allows, and route every AI call through your own backend so you can switch providers without an app update. Never hardcode a provider into the client.

Is adding AI to my app worth it?

Only if the app's core job gets meaningfully better because of it. If removing the AI feature would not change your retention, it is a gimmick that costs API money and privacy-label trust. If AI is the product — as in writing or photo tools — it is increasingly table stakes.

Can React Native apps use AI features?

Yes, fully. Cloud AI APIs are ordinary network calls, and on-device models run through native modules. One of my shipped React Native apps is an AI writing tool that processes on-device for privacy.

AI App Development: What Founders Need To Know Before Building

Building an AI feature into a mobile app is now an integration problem, not a research problem. The decisions that matter are on-device versus cloud inference, per-user API costs at scale, and store privacy rules. The failure mode is bolting on a chatbot nobody asked for.

I shipped one, so this is field notes

One of my four production apps is a privacy-focused AI writing app — built specifically so drafts stay private rather than being shipped to a server by default. That project forced every decision this article covers, so what follows is field notes, not theory.

The one decision that shapes everything: where the model runs

	On-device AI	Cloud AI (API)
Privacy	Data never leaves the phone	Every request reaches a server
Cost at scale	Free per use after shipping	Every user action bills you
Capability	Small models — limited but improving fast	Frontier models, full capability
Offline	Works	Doesn't
Latency	Instant	Network round-trip
App size	Model ships in the binary — bigger download	Small

My writing app leans on-device precisely because privacy was the product. Most apps land on a hybrid: small fast tasks on-device, heavy generation in the cloud. The mistake is not choosing consciously — defaulting to "call the API for everything" and discovering the consequences in your first invoice and your App Store privacy label.

The API bill grows with success — plan for that on day one

Cloud AI pricing looks harmless per request and becomes your biggest line item at scale, because it scales with usage, not with users. A founder budgeting $200/month for AI costs at validation stage needs to know what the same usage pattern costs at 10,000 users — before committing to a free tier.

Practical defenses I build in from the start: cache aggressively (identical requests should never bill twice), cap per-user usage on free tiers, use the smallest model that does each job, and put every AI call behind your own backend so you can switch providers without a store release. That last one has saved projects: model pricing and quality shift every quarter, and being locked to one vendor through hardcoded client calls is a completely avoidable trap.

The stores treat AI apps as privacy-sensitive — because they are

Apple requires every app to declare exactly what data it collects in its privacy label, and text sent to an AI API is user content leaving the device. Google's data safety section works the same way. Declare it wrong and the review team catches it — or worse, catches it later and pulls the app.

If your users will type anything sensitive — health notes, legal drafts, business documents — privacy is not a compliance checkbox, it is a product decision. It is the entire reason the writing app I built processes on-device. "Your data never leaves your phone" is a sentence marketing cannot fake and competitors relying on cloud APIs cannot copy.

When the AI feature is a gimmick

The honest test: does the app's core job get done better because the AI is there? A writing app that improves drafts — yes, that is the product. A booking app with a chatbot bolted on because investors expect AI — no, and users smell it immediately.

Gimmick AI actively costs you: API bills for a feature nobody uses, a privacy label that got scarier, review risk, and a diluted store listing. If the AI feature disappeared tomorrow and your retention would not move, cut it and ship faster. Real AI features, meanwhile, are becoming table stakes in categories like writing, photo editing and language learning — the bar is "meaningfully better at the core job", not "has AI".

What this means for your build

An AI-powered MVP is not automatically more expensive than a normal one — integration against an API is well-trodden work now, and React Native handles it exactly like any backend. The cost risk is operational (the API bill) and the schedule risk is scope, same as every app.

If you are weighing an AI app idea, send me what it should do — I will tell you where the model should run, what the usage economics look like, and whether the AI part is the product or the gimmick. I have made those calls on a shipped product, which is rarer advice than it should be.