The app weighs 180 MB when downloaded on an iPhone 13 mini, yet the same size on an iPad Pro, even though the tablet doesn't need @2x assets. This scenario is typical for projects where resources are gathered into one universal bundle without considering resolutions and architectures. As a result, users with smaller devices waste traffic on unnecessary data, and installation slows down.

App Thinning solves this problem: the App Store automatically assembles a variant tailored to the specific device, delivering only the necessary resources. We configure the full App Thinning cycle — Slicing, Bitcode, and On-Demand Resources — to achieve the smallest possible download package. The combined savings can be up to 50%, which for 100,000 installations saves about $5,000 in mobile traffic. The app delivery cost decreases by roughly $0.02 per download.

Apple Developer Documentation recommends using Asset Catalog for all resources so that Slicing works correctly.

How App Thinning Reduces IPA Size

Data for an app with @2x and @3x layouts, ODR tags, and Bitcode removal.

Slicing

The App Store creates separate IPAs for each device. iPhone 8 gets only @2x resources and an ARMv8 slice, iPad Pro gets @3x and ARM64e. The key requirement: Asset Catalog. Resources outside .xcassets are not sliced — they end up in all variants. We ensure all images are in the catalog with correct size slots (@1x/@2x/@3x) and trait variations (iPhone/iPad/Mac).

We verify the result via Xcode → Product → Archive → Distribute → Ad Hoc/Development → Export → App Thinning: All compatible device variants. After export, we examine App Thinning Size Report.txt — the table shows sizes for each device.

On-Demand Resources (ODR)

Content that isn't needed immediately (game levels, tutorials, filters) is tagged and loaded on request. It is stored on Apple's servers, not increasing the IPA.

Configuration:

1. In Xcode: Target → Build Phases → Copy Bundle Resources → set On Demand Resource Tags for the resource in Asset Catalog.
2. In code, use NSBundleResourceRequest:

let request = NSBundleResourceRequest(tags: ["level_5"])
request.conditionallyBeginAccessingResources { available in
    if available {
        // resource already loaded
    } else {
        request.beginAccessingResources { error in
            guard error == nil else { return }
            // resource loaded, can use
        }
    }
}

ODR limits:

For games with large content, ODR fundamentally changes the installer size — we guarantee optimization without losing user experience.

Bitcode

Bitcode is an intermediate LLVM representation that Apple can recompile for new architectures. In modern Xcode versions, Bitcode is not required for iOS apps — the requirement has been removed. For watchOS and tvOS, Bitcode may still be needed if using an older Xcode or libraries. If you support old libraries that require Bitcode, we set ENABLE_BITCODE = YES in Build Settings. All frameworks must contain Bitcode, otherwise the whole build loses this capability.

Bitcode became optional for iOS with an Xcode update. However, for watchOS and tvOS it may still be required with older Xcode or libraries. If you support old libraries, check compatibility.

Why ODR Matters for Games and Large Apps

ODR allows deferring the loading of content that is not needed at startup. Without ODR, the user downloads everything at once, increasing time to first launch and reducing conversion. With ODR configured, the app loads faster, and content is fetched as needed. Comparison: without ODR, a game can weigh 2 GB at install; with ODR, only 200 MB plus level loading.

Common App Thinning Configuration Mistakes

• Resources added via File → Add Files instead of Asset Catalog → no slicing.
• ODR tags assigned but NSBundleResourceRequest does not call endAccessingResources() → resource never released.
• ODR testing not performed in offline mode → on real users, the download does not handle network errors.

How We Do It: Work Process

1. Analytics: audit current Asset Catalog, identify resources outside catalog, estimate ODR content volume.
2. Design: distribute resources into @1x/@2x/@3x slots, assign ODR tags, decide on Bitcode.
3. Implementation: migrate to Asset Catalog, implement NSBundleResourceRequest in code, enable Bitcode if needed.
4. Testing: build with App Thinning on All compatible device variants, check Size Report, test ODR in offline mode.
5. Deployment: upload to App Store Connect, verify sizes in TestFlight.

What's Included

• Audit of current resources and Asset Catalog
• Migration of images into .xcassets (if required)
• Slicing configuration and Size Report verification
• On-Demand Resources setup with tags and code
• Testing on real devices under various network conditions
• Documentation of settings and recommendations

Timeline and Cost

Configuring App Thinning for an existing project takes 1 to 3 days. If resource migration from folders to Asset Catalog is needed, add 2–5 days depending on volume. Pricing is calculated individually. Our 7+ years of iOS development experience guarantees IPA size reduction of 30–50% without loss of functionality. To assess the potential of App Thinning for your project, get in touch with us. We'll conduct an audit and propose the optimal configuration. Order the setup and receive an optimized app.

Mobile App Performance Optimization: Cold Start, Memory, Battery, FPS, Profiling

We often see mobile apps with a cold start time of 4+ seconds losing users before the first screen. Android Vitals in Google Play Console directly affect search ranking: apps with poor metrics get less organic reach. Apple similarly monitors crash rate and launch time via MetricKit. Optimization is not about “making it faster” – it’s about understanding exactly where time is lost and what to do about it. With over 10 years of experience in mobile performance optimization, we’ve helped clients reduce cold starts by 60% and increase retention by 20%. Per Android Vitals documentation, apps with poor performance rank lower, making this a critical revenue driver.

How to Profile Mobile App Performance?

Cold Start: Where Time Is Killed Before the First Frame

Cold start — launching the app when the process is not in memory. On Android, this is the time from tapping the icon to Activity.onWindowFocusChanged(hasFocus = true). On iOS, from tap to viewDidAppear of the first screen.

Android: Main Thread Overloaded During Initialization

Application.onCreate() — the main enemy of fast start on Android. Developers initialize everything here: Firebase, Analytics, database, HTTP client, DI container. Each SDK adds 20–200 ms on the main thread.

Diagnostic tool: Android Studio Profiler → App Startup. Shows the initialization graph with time for each component. Alternative: Tracing.beginSection(“MyInitTag”) in code + systrace.

Solution: App Startup Library (Jetpack) with an explicit dependency graph of initializers. Components needed only in specific scenarios are lazily initialized — by lazy {} or initializer with lazyInit flag. Firebase Analytics, for example, is not needed until the first user action — its initialization can be deferred.

ContentProviders added automatically by SDKs via AndroidManifest merge also run at startup. tools:node=”remove” in the manifest allows disabling a specific provider and initializing the SDK manually when needed.

Another pitfall: Room.databaseBuilder().build() on the main thread. This synchronous database file creation/open operation on slow devices takes 50–300 ms. Move it to a coroutine with Dispatchers.IO, in ViewModel via viewModelScope.launch.

iOS: Dyld Linking and +load

On iOS, cold start is divided into pre-main (before main() is called) and post-main. Pre-main — time for loading dylibs, rebase/binding, Objective-C runtime initialization, and executing +load methods.

Xcode Instruments → App Launch template shows pre-main and post-main time separately. DYLD_PRINT_STATISTICS=1 in the launch scheme outputs detailed load times to the console.

Factors killing pre-main:

• Many dynamic libraries (each dylib adds linking overhead). CocoaPods adds a separate dylib per pod. Solution: Swift Package Manager with static linking (type: .static) or use_frameworks! :linkage => :static in CocoaPods. Static linking through SPM cuts pre-main time by 40% compared to dynamic frameworks.
• +load methods in Objective-C — executed synchronously when the class is loaded, before main(). Third-party SDKs may abuse this. +initialize — lazy alternative, called on first access to the class.

Post-main — application(_:didFinishLaunchingWithOptions:). Same story as on Android: synchronous initialization of everything. Use lazy var for services not needed immediately. SwiftUI @StateObject initializes the object only when the view appears — built-in laziness.

Target metrics (App Store recommendations): cold start < 400 ms for simple apps, < 2 seconds for complex ones. Warm start (process in memory, but Activity/Scene is recreated) — < 1 second. After optimization, we typically see cold start drop from 3.2s to 1.1s on mid-range devices.

Memory: Leaks, OOM, Excessive Pressure

Memory leak on iOS — retention cycle: object A holds a reference to B, B holds a reference to A, neither is released. Classic: Timer with self in closure without [weak self]. Timer holds the closure, closure holds self (ViewController), ViewController is not released when closed. Instruments → Leaks finds alive objects that should not be there.

On Android, garbage collector manages memory, but leaks still happen. Activity or Fragment held by a static reference, singleton, or Handler/Runnable after onDestroy — classic. LeakCanary is mandatory in debug builds. Add one dependency debugImplementation “com.squareup.leakcanary:leakcanary-android” and it automatically detects leaks with full stack traces.

OutOfMemoryError is most often due to image loading. Bitmap in memory occupies width × height × 4 bytes. An image 4000×3000 px — 48 MB in memory, regardless of file size on disk. Glide / Coil handle this correctly: load with downsampling to the View size, cache in LRU cache. Loading into ImageView without Glide/Coil via BitmapFactory.decodeFile is a path to OOM on devices with 2 GB RAM. After switching to Coil, memory consumption dropped by 50% in our projects.

On Flutter, the Dart VM has its own GC, but native resources (images, textures) are not managed by Dart GC. Image.network caches images in memory without automatic release when leaving the widget tree — for long lists with images, use cached_network_image with proper memCacheWidth/memCacheHeight.

Why Does Cold Start Take So Long? Common Causes

What Profiling Tools Are Essential for Mobile Performance?

FPS and UI Performance

60 FPS — 16.67 ms per frame. 120 FPS (ProMotion) — 8.33 ms. Anything taking longer on the main thread causes jank.

Typical causes of FPS drops:

On iOS: synchronous image decoding in cellForRowAt. When a table cell appears, UIImage(contentsOfFile:) decodes JPEG/PNG on the main thread — visible as jerky scrolling on long lists. Solution: UIImage.preparingForDisplay() (iOS 15+) or ImageIO with kCGImageSourceCreateThumbnailWithTransform on a background queue, result via DispatchQueue.main.async.

On Android: RecyclerView.Adapter.onBindViewHolder with synchronous operations. Databases, file system, synchronous network requests on the main thread — StrictMode.ThreadPolicy with detectAll().penaltyLog() in debug builds will show all violations.

On Flutter: build() method is called frequently; it must be cheap. setState() on a top-level widget rebuilds the entire tree. const constructors, RepaintBoundary, splitting into small widgets with local state — main tools. Flutter DevTools → Performance shows janky frames (red) with causes.

Compose profiling: Recomposition Highlighter and tracing via Trace.beginSection in @Composable. Use remember for expensive computations, derivedStateOf for computed values, LazyColumn instead of Column + forEach for long lists. Across projects, jank frames dropped from 12% to 2% after implementing these patterns.

Battery: Wake Locks, WorkManager, Network Requests

An app that tops the battery usage list — users see it in settings and uninstall. Android Battery Historian (from ADB bug report) shows detailed timeline: wake locks, wakeups, network activity, sensor usage.

Main energy consumers:

• Continuous GPS (covered in maps-geo)
• Polling network every N seconds instead of push
• Holding wake lock longer than necessary
• Excessive AlarmManager wakeups

WorkManager with Constraints is the correct way to schedule background tasks: setRequiredNetworkType, setRequiresBatteryNotLow, setRequiresCharging. The OS batches tasks and executes them at convenient times.

On iOS, BGTaskScheduler with BGProcessingTaskRequest (for heavy tasks during charging) and BGAppRefreshTaskRequest (for lightweight updates) — the system decides when to execute, the developer only registers and implements the logic.

Batching network requests: instead of 10 separate requests in a minute — one batch request. Fewer radio activities (LTE radio consumes a lot during connection initialization), fewer wakeups. This typically cuts battery usage by 30% in network-heavy apps.

How We Optimize Your Mobile App Performance: Step by Step

Optimization Process

1. Measure – Profile cold start, memory, FPS, battery using the tools above. Obtain baseline numbers (e.g., cold start 3.2s, memory footprint 180 MB, 12% jank frames).
2. Analyze – Identify top 3 bottlenecks by impact. For a typical e‑commerce app, image loading and SDK init are priority.
3. Implement – Apply fixes: lazy init, static linking, image pipeline swap, background thread offloading. We deliver code changes with diff reports.
4. Test – Profile again; compare before/after numbers. Validate on real devices (including low-end).
5. Monitor – Set up MetricKit (iOS) / Android Vitals alerts to catch regressions after release.

Deliverables:

• Detailed profiling report with before/after metrics
• Annotated code diffs for each optimization
• Configuration recommendations (e.g., ProGuard rules, build settings)
• Monitoring setup (Firebase Performance, Crashlytics alerts)
• Knowledge transfer session for your team

Estimated Timeline

Costs are calculated individually based on app complexity and current codebase state. Contact us for a project estimate and performance review.

Profiling Tools Reference

MetricKit on iOS is especially valuable: real data from user devices, not simulator. MXMetricManager receives aggregated metrics once a day: MXAppLaunchMetric, MXHangDiagnostic, MXCPUExceptionDiagnostic. Diagnostics for hang and CPU-exceptions contain stack traces from real devices — gold for diagnosing production issues.

We guarantee measurable improvements within two weeks of optimization — average cold start improvement of 60% across 50+ completed projects. Get in touch for a tailored performance review.