Our team is debating whether to use Flutter (AI apps) for our next project which relies heavily on real-time computer vision. Will we lose too much performance by using a cross-platform wrapper for the GPU-intensive AI tasks?
3 answers
The performance gap between Flutter (AI apps) and native development has narrowed significantly. Since the actual AI inference happens in the underlying C++/Metal/Vulkan layer, the "wrapper" overhead is often negligible. The main area where you might notice a difference is in the image stream conversion logic. Native apps have direct access to hardware buffers, while in Flutter, you sometimes have to copy the image data between the native side and the Dart side. However, for 90% of use cases, the speed of cross-platform development outweighs the 1-2ms latency hit you might take on the frame processing loop.
Deborah, in your experience, do specialized AI chips like the Apple Neural Engine work as effectively when called via a Flutter plugin as they do natively?
The biggest advantage of Flutter is that you only have to write your model integration and post-processing logic once for both platforms.
Exactly, Paul. For a startup, the time saved by using Flutter (AI apps) to hit both markets simultaneously is a game changer, and the performance is more than enough for modern mobile AI.
Steven, yes they do! As long as your plugin or library (like TFLite with the CoreML delegate) is correctly configured, it will leverage the ANE or the Android NPU perfectly. When building Flutter (AI apps), the Dart code is just the orchestrator. The heavy lifting is still done by the same silicon-optimized libraries that a native developer would use. I’ve seen nearly identical inference times on both platforms when the delegates are set up properly to offload the math to the dedicated AI hardware.