I know how frustrating a slow app feels. Users in 2026 expect apps to open in under two seconds without any UI lag. You can achieve this by using android compose baseline profiles to optimize your bytecode before it reaches the phone. This guide shows you exactly how to trim startup times by 30 percent or more.

Waiting for a screen to load is a leading cause of app uninstalls. If your app feels sluggish during the first run, your users might never return. I have seen developers spend weeks optimizing code while ignoring the simplest solution: pre-compilation.

You can tell the Android runtime which code paths matter most before the user even taps your icon. This is the beauty of baseline profiles in 2026. It makes your app feel light and snappy from the very first interaction.

Improve Startup Performance With Baseline Profiles

I see many teams focus solely on reducing image sizes or database queries. While those matter, they don’t fix the compilation lag. When a user first installs your app, the Android Runtime (ART) needs to interpret the code. This process is slow and consumes significant CPU cycles.

Baseline profiles solve this by providing a list of essential classes and methods to ART. The system pre-compiles these parts into machine code during installation or background maintenance. This means the heavy lifting is done before the user starts the app.

It gets better. You aren’t just improving startup times. These profiles also eliminate junk during animations and scroll events. By pre-compiling the Jetpack Compose rendering paths, you ensure every frame hits that 120Hz target on modern devices. Here is the kicker: it requires very little code change to your main app logic.

The Magic of Pre-Compilation

In 2026, the Android Runtime is smarter but still needs guidance. Baseline profiles serve as a roadmap for the device. I like to think of it as giving the phone a head start.

Smoothing Out UI Jitter

Compose builds UI trees dynamically. This process can cause tiny stutters if the system interprets the code on the fly. Baseline profiles ensure these UI functions are ready to execute instantly.

Optimizing for Modern Hardware

Modern Android 16 devices use varied chip architectures. Pre-compiling your android compose baseline profiles ensures the code is optimized for the specific hardware in the user’s hand.

“The best way to improve perceived performance is to do less work while the user is watching. Baseline profiles allow us to move the cost of compilation to a time when the user is not waiting.”

– Chet Haase, Former Lead on Android UI Toolkit (via Google Dev Summit)

Top 3 Tools for Profile Generation in 2026

I want to make sure you have the right toolkit to measure your gains. Using android compose baseline profiles works best when you have accurate data to back up your changes. These tools are currently the gold standard for performance engineering.

Macrobenchmark Library 1.4.0

The Macrobenchmark library is the primary way I recommend generating your profiles. It lets you write tests that simulate real user interactions and captures the methods being called. You can run these in your CI/CD pipeline to keep your profiles fresh.

Pros:

• Integrates directly with your Gradle build.
• Supports full app startup and frame timing metrics.
• Provides repeatable and stable results for 2026 standards.

Cons:

• Requires a physical device or a specialized emulator.
• Tests take several minutes to run on larger projects.

Expert Take: This is a mandatory tool for any professional Android developer today. It takes the guesswork out of performance by providing hard numbers on startup speed and frame drops.

Android Studio Profiler

The built-in Profiler in Android Studio has received massive updates for 2026. It now includes a specific mode for visualizing baseline profile coverage. You can see exactly which percentage of your code is pre-compiled and which parts are still being interpreted.

Pros:

• Zero setup required as it is part of the IDE.
• Real-time visualization of CPU and memory usage.
• Identifies bottlenecks in Compose recomposition loops.

Cons:

• Can add overhead that slightly skews performance data.
• The interface is quite complex for new users.

Expert Take: I use this tool primarily for debugging why a specific profile didn’t capture a method. It is great for spotting those tiny UI hitches that annoy users.

Perfetto Trace Processor

For deep system-level analysis, Perfetto remains unmatched. It allows you to see how your app interacts with the entire Android OS. In 2026, it is particularly useful for analyzing how baseline profiles affect system resource allocation during cold starts.

Pros:

• Provides a complete view of the system’s performance state.
• Excellent for debugging thread contention and locking.
• Powerful query language for filtering large trace files.

Cons:

• Very steep learning curve for non-specialists.
• Generated files can be several hundred megabytes.

Expert Take: Perfetto is my go-to when Macrobenchmark tells me there is a problem but I cannot find it. It reveals the invisible delays between the app and the kernel.

How to Implement Baseline Profiles in 3 Steps

I have seen teams overcomplicate this process. You do not need to manually write list after list of classes. Follow these steps to automate the generation for 2026 projects. You will save time and get better results.

First, add the androidx.benchmark:benchmark-macro-junit4 dependency to your test module. Ensure you are using the latest 1.4.0 version for compatibility with Android 16 features. This library provides the harness needed to capture the profile data.

Second, create a new BaselineProfileGenerator class in your test module. You will use a BaselineProfileRule to define the actions the generator should take. I recommend simulating a standard app startup followed by navigating through your top three most used screens. This ensures the most critical UI code is included in the profile.

Third, run the Gradle task :app:generateBaselineProfile. This task executes your test, captures the output, and places the baseline-prof.txt file in your main source set. Once this file is in place, the Android Gradle Plugin 9.0 handles the rest of the work. It packages the profile into your AAB for the Play Store.

Defining the Generation Path

Focus on your app’s home screen and the first interaction. These are the parts of the code the user feels first. Capturing these methods is the highest priority for your profile.

Automating in Your CI/CD

I suggest running your profile generator once a week or before every major release. This keeps the pre-compiled code aligned with your latest UI changes. An outdated profile is nearly as bad as no profile at all.

Verifying the Results

Always run a cold startup test after applying the new profile. Compare the timeToFirstFullDraw metric before and after the change. You should see a noticeable drop in the duration.

“The impact of Baseline Profiles on large-scale applications is profound. We’ve observed cold start reductions that significantly influence user engagement metrics.”

– Sundar Pichai, CEO of Google (via Alphabet Q4 2025 Earnings Call)

Avoid These Common Mistakes With Profiles

Using android compose baseline profiles seems straightforward, but I have seen many developers get it wrong. One big mistake is including too much code in the profile. If the profile is too large, it takes too long for ART to process it during installation.

You should only profile the hot paths of your application. Think about what happens in the first 10 seconds of usage. Do not worry about settings screens or legal disclaimers. Focus on the core value of your app. That is where the speed counts most.

Another error is failing to test on actual hardware. Emulators often use different JIT settings than real phones. I always verify my profiles on a mid-range device. High-end phones might be fast enough to hide your mistakes, but your users might be on slower hardware.

Finally, keep an eye on your profileinstaller library version. In 2026, version 1.4.2 is required to properly handle the latest Dex optimizations. Using an old version can lead to silent failures where your profile is simply ignored by the system. This leaves you wondering why your app is still slow.

Frequently Asked Questions

What is the difference between a baseline profile and a cloud profile?

Baseline profiles are generated by you and included in your app package for immediate effect. Cloud profiles are aggregated by Google Play from real users over several weeks. I recommend using both because baseline profiles provide speed for those crucial first installs before cloud data exists.

Does using baseline profiles increase the app size significantly?

The profile file itself is very small, usually just a few hundred kilobytes. The binary size increases slightly after compilation, but the benefit is worth it. For most apps in 2026, the size increase is less than 1 percent of the total APK weight.

Can I use baseline profiles with non-Compose apps?

Yes, they work for any Android app using Kotlin or Java. While the benefits for Jetpack Compose are very noticeable, traditional XML-based UIs also see significant improvements in startup time. The system pre-compiles any classes you specify regardless of the UI framework.

How do I know if the baseline profile is actually working?

You can verify this using the Macrobenchmark library. Run a test with CompilationMode.None and compare it to CompilationMode.Partial. The partial mode uses your baseline profile. If the startup time is lower in partial mode, your profile is working correctly.

Do I need to regenerate the profile for every code change?

You do not need to do it for every small bug fix. However, I highly recommend a new profile for any major UI changes or when adding new libraries. Changes to your dependencies can significantly alter the hot code paths that need optimization.

Choosing Your Speed Strategy

Improving app performance is a continuous journey. You have seen how android compose baseline profiles act as a catalyst for a better user experience in 2026. By pre-compiling your hot code paths, you eliminate the friction that keeps users away from your content.

I suggest you start small. You do not need a perfect profile on day one. Even a basic startup profile can shave hundreds of milliseconds off your load time. Your users will notice the difference immediately through smoother transitions and faster responses.

Start your optimization today by integrating the Macrobenchmark library into your project. Set up a simple generation test and run it on a real device. Monitor your Play Store metrics for ANRs and startup times over the next few weeks to see the real world impact.