8 Best Ways to Reduce Android App Size

5 minutes, 29 seconds read

Published on Aug 14, 2020

With the increase in mobile storage spaces that have gone up to 256 GB, app size is also growing. App size is sure to grow as developers are adding new features, trying to meet customer needs, and also trying to support their apps on various screen sizes. Around 74% of the world uses Android, and 70% of users consider app size before installing them. Moreover, as humans are getting accustomed to instant gratification, they ponder on ways to download apps as they take up storage spaces. Despite the cloud support for photos, videos, and files, android users face issues, such as mobile hanging due to app size. As customer expectations are increasing, android app developers are considering other ways to reduce app size while still incorporating significant additional features and keeping in mind the customer experience.

Below are the 8 best ways to reduce android app size:

1. Use Android App Bundle to Reduce App Size

When generating the release version of your app, you can choose between APK and Android App Bundle. The second option will make Google play to generate the APK with only those features a specific user need.

App Bundle Vs APK

Android App Bundle

It is a publishing format that includes compiled code and resources of your app, and delays APK generation and signing to Google Play.
With Android App Bundles, the compressed download size restriction is 150 MB. The app bundle cannot be used with APK expansion files.

How to build android app bundles?

To build app bundles, follow these steps:

Download Android Studio 3.2 or higher—it’s the easiest way to add feature modules and build app bundles.
Add support for Play Feature Delivery by including a base module, organizing code and resources for configuration APKs, and, optionally, adding feature modules.
Build an Android App Bundle using Android Studio. You can also deploy your app to a connected device from an app bundle by modifying your run/debug configuration and selecting the option to deploy APK from app bundle. Keep in mind, using this option results in longer build times when compared to building and deploying only an APK.
If you’re not using the IDE, you can instead build an app bundle from the command line.
Test your Android App Bundle by using it to generate APKs that you deploy to a device.
Enroll into app Play App Signing. Otherwise, you can’t upload your app bundle to the Play Console.
Publish your app bundle to Google Play.

Please note: Android Package Kit – As per developer console, by the mid of 2021, developers won’t be able to upload apk on play store)

Android operating system uses APK which is the package file format for distribution and installation of mobile apps, games and middleware. APK is similar to other software packages such as APPX in Microsoft Windows or a Debian package in Debian -based operating systems.
Google Play requires that the compressed APK downloaded by the users should not exceed 100 MB.
The expansion files for your app are hosted by Google Play which serves them to the device at no cost to you. The expansion files are saved to the device’s shared storage location (the SD card or USB-mountable partition).

2. Use Proguard

Proguard is probably one of the most useful tools to reduce your APK size. It reduces the source code files to a minimum and can reduce the APK file size upto 90%.

Use it in all variants whenever using “Proguard”
Helps to avoid conflict at the of generate apk or bundle if will use in all the variants.
We cannot let ProGuard rename or remove any fields on these data classes, as they have to match the serialized format. It’s a safe bet to add a @Keep annotation on the whole class or a wildcard rule on all your models.

3. Use Android Size Analyzer Plugin

This Android Studio plugin will provide you recommendations to reduce the size of your application.

With the APK Analyzer, you can accomplish the following:

View the absolute and relative size of files in the APK, such as the DEX and Android resource files.
Understand the composition of DEX files.
Quickly view the final versions of files in the APK, such as the AndroidManifest.xml file.
Perform a side-by-side comparison of two APKs.

There are three ways to access the APK Analyzer when a project is open:

Drag an APK into the Editor window of Android Studio.
Switch to the Project perspective in the Project window and then double-click the APK in the default build/output/apks/ directory.
Select Build > Analyze APK in the menu bar and then select your APK.

More details at: Jetbrains

4. Optimize Your App’s Resources

Whether used or not, every resource takes up memory. It is therefore necessary to have only those resources that you need, and to use those in a memory efficient way. In other words, you should consider the resolution of the image before finalizing on it.

5. Optimize Libraries

As large libraries consume huge amounts of space, it is advisable to remove parts of it in case you do not need them and if it is permitted by the license of the library. Proguard can aid you in this process but it is not always able to remove large internal dependencies.

6. Use Vector Graphics Wherever Possible

They are sharp and do not consume much space as they rely on mathematical calculations and not on pixels that need to be saved. However, they cannot be used for photography.

7. Compress Your Images

By using tools such as pngcrush, you can reduce the file size of PNG images. It is advisable to do this images as they still look the same.

8. Only Support Specific Densities

If only a small portion of users use a specific density, it might be better to let Android scale your other densities for them as it would reduce your APK size.

As mobile storage space is growing, people are installing a large number of apps to meet a wide range of needs. But as app size is increasing, people are continuing to struggle with storage issues. With provisions such as Proguard, one can compress the APK file size and optimize libraries easily. Compressing images and using vector graphs are also useful in reducing app size.

About the author: Anand Singh is Tech Lead at Mantra Labs. He is integral to the company’s Android-based projects and enterprise application development.

Further Reading:

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

Can AI recognize its own mistakes?
What would “self-awareness” look like in AI without consciousness?
Are there techniques to make AI second-guess itself?
Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

8 Best Ways to Reduce Android App Size