Authors - Aanchal Khandalkar, Reena S. Satpute Abstract - Mobile app developments exploded lately, and it’s not hard to see why. Things like 5G, AI, machine learning, and Mobile Edge Computing aren’t just making headlines they’re actually changing how apps work. Now, apps are smarter, more personalized, and packed with features that can show up overnight. Sounds amazing, but the flip side is tough: people expect everything instantly. They want quick responses, apps that basically read their minds, and zero downtime, no matter where they are. Honestly, building apps these days is anything but simple. Hardware is a real pain for developers. Phones just don’t have the power of regular computers. You get less memory, slower processors, and, of course, batteries that bail on you before you even realize. So, developers have to work magic keep the app running smoothly without draining the battery, or else people just uninstall. And with so many apps depending on outside libraries and analytics tools, there’s a whole new pile of problems. Sure, these tools help, but they open the door to security risks. Not every team has someone who lives and breathes security, so it’s easy for privacy issues or sketchy code to sneak in. All of that puts some cracks in the process and makes building solid apps a lot trickier. Such fragmentation exists on platform that developing AI/ML application for it, on the Android platform in particular, turns out to be extremely painful to integrate on. Then it becomes a question of tradeoffs from developers’ perspective-how the usage of cloud services versus local device processing fits, on each having its share of difficulties regarding scale, speed, power and security. In this paper, we go through the workflow and delve deeper into a comparative study on native application development.
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