Before adding an AI feature to a Flutter app, run through this quick checklist.
Use cloud AI when:
- The model is large (LLM chat, complex recommendations).
- You need to update model behaviour without shipping a new app version.
- Latency of a few hundred milliseconds is acceptable.
- Users are usually online.
Use on-device ML when:
- You need real-time camera, audio or AR processing.
- Privacy matters and data should not leave the phone.
- The app must work offline.
- You want to avoid per-request API costs at scale.
Either way:
- Log inputs and outputs (with consent) so you can improve the feature.
- Add a graceful fallback when the model fails or is unsure.
- Test on low-end Android devices, not just flagship phones.
- Budget for retraining or prompt tuning after launch.
Most production apps end up hybrid: lightweight on-device models for instant feedback, cloud models for heavier reasoning.
We documented how we make these trade-offs on client projects here: [Before adding an AI feature to a Flutter app, run through this quick checklist.
Use cloud AI when:
- The model is large (LLM chat, complex recommendations).
- You need to update model behaviour without shipping a new app version.
- Latency of a few hundred milliseconds is acceptable.
- Users are usually online.
Use on-device ML when:
- You need real-time camera, audio or AR processing.
- Privacy matters and data should not leave the phone.
- The app must work offline.
- You want to avoid per-request API costs at scale.
Either way:
- Log inputs and outputs (with consent) so you can improve the feature.
- Add a graceful fallback when the model fails or is unsure.
- Test on low-end Android devices, not just flagship phones.
- Budget for retraining or prompt tuning after launch.
Most production apps end up hybrid: lightweight on-device models for instant feedback, cloud models for heavier reasoning.
We documented how we make these trade-offs on client projects here: (https://www.pyramidions.com/ai-powered-app-development-company-in-chennai/)
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