Blog / AI · Engineering
Adding AI features to a mobile app: cost, latency, privacy and safety
What it takes to ship generative and intelligent features in a mobile app that users trust and that pay for their own inference.
· 2 min read · Vasoo
AI features have moved from demos to product. The hard part is no longer getting a model to produce something; it is shipping a feature that is fast enough, cheap enough, safe enough and clear enough to keep users. Here is what we plan for on our own AI apps and on client work.
Cost
Every generation has a price. Model the cost per active user per month before building, tie usage limits to the subscription tier, cache aggressively and choose the smallest model that meets the quality bar for each task. Usage analytics per feature are not optional.
Latency
Users tolerate waiting when they can see progress and when the result is worth it. Stream text, show staged previews for images, and do work in the background where possible. On-device models with Core ML or ML Kit remove network latency for vision, speech and small language tasks.
Privacy
Say what is sent, where, and why. App Store privacy labels and Google Play data safety must match the reality of the pipeline. Prefer on-device processing for sensitive data, and never train on user content without explicit consent.
Safety and moderation
Moderate inputs and outputs, add guardrails to prompts, and build a path for users to report results. Both stores have policies on AI-generated content, and review will test them. Design the rules before the feature, not after a rejection.
Evaluation
Keep a test set of real inputs and expected qualities. Run it on every prompt or model change. Without it, quality drifts silently and the first sign is a wave of one-star reviews.
See our AI app development service, or start a project to discuss a feature.