On-device AI vs cloud AI: how to choose for your app

When founders plan an AI feature, they usually ask which model to use. The more important question comes first: where should the AI run? On the user's phone, or on a server in the cloud?

The answer shapes your running costs, your privacy promises, what your free tier can include and whether the product works at all in certain situations. We learned this building HAVEN, an offline survival app that ships 22 curated AI models that run entirely on the device.

The case for on-device AI

It costs nothing per question. Once the model is on the phone, every answer is free to you. With cloud AI, every question has a cost that grows with your users. On-device AI is what made HAVEN's generous free tier possible.

It works offline. For HAVEN, this was the whole point. A survival app that needs a signal fails at the exact moment people need it most. If your product's core promise involves remote locations, travel, unreliable connections or emergencies, on-device AI may be the only honest option.

Data never leaves the device. That's a simpler privacy story, fewer compliance questions and an easier sell to cautious users, especially in health, finance and children's products.

The trade-offs are real. Local models are smaller and less capable than the best cloud models. They take storage space (often hundreds of megabytes or more), use battery, and run slower on older phones. Updating them means shipping a new download.

The case for cloud AI

It's more capable. The strongest models only run in the cloud. For complex reasoning, long documents or high-quality image understanding, cloud models are usually far better.

It's easier to update. You can switch to a better or cheaper model overnight without users downloading anything.

It works on every device. A cheap phone gets the same quality as a flagship.

But every request costs money, needs a connection, and sends data to a third party, which you have to disclose in your privacy policy.

A simple way to decide

Ask these questions in order:

1. Must it work without internet? If yes, the core loop needs on-device AI.
2. Is the data sensitive? Health, finance, children or private documents push you toward on-device, or toward a carefully chosen cloud provider with clear data terms.
3. How often will users call it? Frequent, everyday use on a free tier gets expensive in the cloud fast.
4. How hard is the task? Short answers, classification and simple extraction often work well locally. Complex reasoning usually needs the cloud.

Most products end up hybrid

The best answer is often both. Run the frequent, simple, private tasks on the device, and send the occasional heavy task to the cloud with clear limits.

Finvex takes a different approach to the cost problem: cloud AI for receipt scanning and the chat assistant, with daily limits on the free tier and an option for power users to bring their own API key. That keeps the AI features generous without letting heavy usage eat the margin.

The mistake to avoid

The most expensive mistake is choosing cloud AI by default, launching a free tier, and discovering months later that your AI bill grows faster than your revenue. Decide where your AI runs before you set your pricing, not after.

Building something with AI at its core? Tell us about it. Choosing the right architecture early is one of the most valuable things a technical partner can do.