Building Computer Vision apps for Android in 2026: Has the bottleneck shifted from AI models to mobile engineering?

I've been reading case studies and following discussions around Android apps that use Computer Vision for OCR, object detection, image segmentation, quality inspection, and video analytics.

A few years ago, most conversations focused on model accuracy and training. But now it seems like the bigger challenges are:

Running models efficiently on-device with TensorFlow Lite or MediaPipe.
Balancing latency, battery usage, and model size.
Handling fragmented Android hardware and varying NPUs.
Building reliable pipelines for image/video processing in production.
Deciding when to use on-device inference vs cloud inference.

Interestingly, many successful products seem to rely as much on strong mobile engineering and UX as on the AI model itself.

I'm curious if other Android developers feel the same way.

For people who have built or worked on Computer Vision apps:

What has been the hardest problem for you?
Has model development become easier while deployment remains the real challenge?
Which frameworks or approaches have worked best in production?

Would love to hear real-world experiences and lessons learned.

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