Most founders asking this question have already built a mental picture of what AI in a mobile app looks like: a chatbot, a magic search bar, maybe a camera that recognizes things. What they do not know is whether it is technically possible in their app, what it costs, and whether it will make their app sluggish or drain the user's battery. All fair concerns. Here is a clear-eyed answer to each.
What types of AI features work well on mobile?
The short answer is: more than you would expect, and they are getting easier to add every year.
The most common AI features in mobile apps fall into a few categories. Intelligent search lets users describe what they want in plain language instead of typing exact keywords. A furniture app where you type "something warm for a small bedroom" and get relevant results is a good example. Content generation lets users draft messages, summarize long text, or fill in forms with a tap. Personalization surfaces the right content, product, or recommendation at the right time based on how a user actually behaves in the app. Image and voice features let users take a photo of something or speak a query and get a useful result back.
All four of these are practical additions to an existing mobile app. They do not require rebuilding your product from scratch. And in 2025, the APIs that power them cost a fraction of what they did in 2022. OpenAI's GPT-4o API, for example, costs roughly $2.50 per million input tokens. At a realistic usage level for a consumer app, that translates to well under $0.01 per user interaction. The cost that used to scare founders off has mostly vanished.
A 2024 survey by Apptopia found that apps with at least one AI-powered feature had 23% higher 30-day retention than comparable apps without AI. Retention is revenue, and that number is why every product team is asking this question right now.
How does on-device AI differ from cloud-based AI?
When your app sends a user's text or photo to a server to get an AI result, that is cloud-based AI. When the AI model runs directly on the user's phone without any network connection, that is on-device AI. The distinction matters for cost, speed, and what features are even possible.
Cloud-based AI is easier to build, more powerful, and more flexible. You call an API, pass the user's input, and get a result back. The AI models available through cloud APIs are much larger and more capable than anything that fits on a phone today. The tradeoff is latency: a round trip to a server and back adds 200–800 milliseconds depending on connection quality. For most features, users do not notice. For a feature where every millisecond counts, like real-time voice translation mid-conversation, it can feel slow.
On-device AI runs without an internet connection and responds instantly because nothing leaves the phone. Apple's Core ML and Google's ML Kit are the two main tools for this approach. They ship pre-trained models for common tasks like face detection, text recognition, and language identification. These models are smaller and narrower than cloud APIs, but they are fast and private. No user data ever leaves the device.
For most mobile apps, the right answer is a mix of both. Use on-device AI for features that need to feel instant or that handle sensitive data. Use cloud AI for features that need intelligence and flexibility. A fitness app might use on-device AI to detect movement through the phone's camera and cloud AI to generate a personalized workout summary.
Apple reported in 2024 that over 60% of its App Store top-grossing apps now use at least one Core ML model. On-device AI is no longer a specialty technique; it is a standard part of how good mobile apps are built.
Do AI features drain battery or slow the app down?
This is the question founders almost never ask and should. The honest answer: yes, if implemented carelessly. No, if done correctly.
Cloud-based AI features use the phone's network and a small amount of processor power to make an API call. The server does the heavy lifting. The phone's battery cost is roughly equivalent to loading a webpage. For most use cases, this is negligible.
On-device AI is the one to watch. Running an image recognition model locally means the phone's chip is doing real computation. On older or budget Android devices, a poorly optimized on-device model can cause the UI to stutter while it runs. Battery drain is also measurable if the model runs continuously.
The fix is straightforward: run AI tasks on a background thread so the app stays responsive, and trigger AI processing only when the user actually needs a result rather than running it constantly. Apple's Neural Engine and Google's Tensor chip are specifically designed to handle AI workloads efficiently, so on modern devices the battery impact is minimal. A well-built on-device feature on a recent iPhone or Pixel uses roughly the same power as playing a 30-second video clip.
A developer who has shipped AI features before knows to profile these workloads before launch. An agency that has not shipped AI-native apps before often skips this step and ships something that degrades on any device older than two years. Ask the team building your feature whether they will test on a range of devices, not just the latest hardware.
Should I use platform-native AI SDKs or third-party APIs?
Platform-native SDKs (Apple's Core ML, Google's ML Kit) and third-party APIs (OpenAI, Anthropic, Google Gemini) are not competitors. They solve different problems.
Native SDKs are the right choice when your feature needs to run offline, when the task is narrow and well-defined (detecting faces, reading QR codes, identifying objects in a photo), or when the data is too sensitive to send to an external server. They are also free: you pay no per-call fees, because all the computation happens on the user's device.
Third-party APIs are the right choice when your feature needs real intelligence, when the task involves language understanding or generation, or when you want to iterate quickly without training your own models. The cost is low and predictable. A typical consumer app with 10,000 monthly active users, each triggering an AI feature three times per session, would spend roughly $150–$300 per month on API calls at current pricing. Western agencies often quote AI integration as a separate expensive line item. In reality, the API cost is almost never the expensive part. The development time to integrate the feature correctly is.
| Feature Type | Best Approach | Typical Add-on Cost (vs. No AI) | Per-User API Cost |
|---|---|---|---|
| Smart search / semantic filters | Third-party API (e.g. OpenAI embeddings) | +$3,000–$6,000 to build | ~$0.002 per search |
| AI content generation (drafts, summaries) | Third-party API (e.g. GPT-4o) | +$4,000–$8,000 to build | ~$0.005 per generation |
| Image recognition (objects, text, faces) | On-device (Core ML / ML Kit) | +$2,000–$5,000 to build | $0 (runs on device) |
| Real-time voice features | Hybrid (on-device trigger, cloud processing) | +$8,000–$15,000 to build | ~$0.01 per minute |
| Personalized recommendations | Third-party API or lightweight on-device model | +$5,000–$10,000 to build | ~$0.001 per session |
Western agencies routinely add 30–50% to a project budget when a client asks for AI features, citing integration complexity. An AI-native team builds these integrations as a standard part of the workflow, not an exotic addition. The premium at a traditional agency is not because AI is hard. It is because their process was not designed around it.
At Timespade, adding a smart search feature to an existing mobile app costs $3,000–$6,000 and ships in about two weeks. The same feature at a Western agency typically runs $12,000–$20,000 and takes six to eight weeks. The underlying API is identical. The difference is the process.
If your product roadmap includes any AI features, the most expensive decision you can make is adding them as an afterthought with a team that has never shipped them. AI features that are woven into the app from the start are faster to build, cheaper to run, and more reliable than features bolted on later.
