fidzu : mobile

If you are an Android developer looking to implement innovative AI features into your app, we recently launched powerful new updates: Hybrid inference, a new API for Firebase AI Logic to leverage both on-device and Cloud inference, and support for new Gemini models including the latest Nano Banana models for image generation.

Let's jump in!

Experiment with hybrid inference

With the new Firebase API for hybrid inference, we implemented a simple rule-based routing approach as an initial solution to let you use both on-device and cloud inference via a unified API. We are planning on providing more sophisticated routing capabilities in the future.

It allows your app to dynamically switch between Gemini Nano running locally on the device and cloud-hosted Gemini models. The on-device execution uses ML Kit's Prompt API. The cloud inference supports all the Gemini models from Firebase AI Logic in both Vertex AI and the Developer API.

To use it, add the firebase-ai-ondevice dependencies to your app along with Firebase AI Logic:

dependencies {
 [...] 
 implementation("com.google.firebase:firebase-ai:17.11.0")
 implementation("com.google.firebase:firebase-ai-ondevice:16.0.0-beta01")
}

During initialization, you create a GenerativeModel instance and configure it with specific inference modes, such as PREFER_ON_DEVICE (falls back to cloud if Gemini Nano is not available on the device) or PREFER_IN_CLOUD (falls back to on-device inference if offline):

val model = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3.1-flash-lite",
        onDeviceConfig = OnDeviceConfig(
           mode = InferenceMode.PREFER_ON_DEVICE
        )
    )

val response = model.generateContent(prompt)

The Firebase API for hybrid inference for Android is still experimental, and we encourage you to try it in your app, especially if you are already using Firebase AI Logic. Currently, on-device models are specialized for single-turn text generation based on text or single Bitmap image inputs. Review the limitations for more details.

We just published a new sample in the AI Sample Catalog leveraging the Firebase API for hybrid; it demonstrates how the Firebase API for hybrid inference can be used to generate a review based on a few selected topics and then translating it into various languages. Check out the code to see it in action!

Try our new models

As part of the new Gemini models, we've released two models particularly helpful to Android developers and easy to integrate in your application via the Firebase AI Logic SDK.

Nano Banana

Last year we released Nano Banana, a state-of-the-art image generation model. And a few weeks ago, we released a couple of new Nano Banana models.

The new Nano Banana models leverage real-world knowledge and deep reasoning capabilities to generate precise and detailed images.

We updated our Magic Selfie sample (use image generation to change the background of your selfie!) to use Nano Banana 2. The background segmentation is now handled directly with the image generation model which makes the implementation easier and lets Nano Banana 2 improved image generation capabilities shine. See it in action here.

You can use it via Firebase AI Logic SDK. Read more about it in the Android documentation.

Gemini 3.1 Flash-Lite

We also released Gemini 3.1 Flash-Lite, a new version of the Gemini Flash-Lite family. The Gemini Flash-Lite models have been particularly favored by Android developers for its good quality/latency ratio and low inference cost. It's been used by Android developers for various use-cases such as in-app messaging translation or generating a recipe from a picture of a dish.

Gemini 3.1 Flash-Lite, currently in preview, will enable more advanced use cases with latency comparable to Gemini 2.5 Flash-Lite. To learn more about this model, review the Firebase documentation.

Conclusion

It's a great time to explore the new Hybrid sample in our catalog to see these capabilities in action and understand the benefits of routing between on-device and cloud inference. We also encourage you to check out our documentation to test the new Gemini models.

Android 17 has reached beta 4, the last scheduled beta of this release cycle, a critical milestone for app compatibility and platform stability. Whether you're fine-tuning your app's user experience, ensuring smooth edge-to-edge rendering, or leveraging the newest APIs, Beta 4 provides the near-final environment you need to be testing with.

Get your apps, libraries, tools, and game engines ready!

If you develop an Android SDK, library, tool, or game engine, it's critical to prepare any necessary updates now to prevent your downstream app and game developers from being blocked by compatibility issues and allow them to target the latest SDK features. Please let your downstream developers know if updates are needed to fully support Android 17.

Testing involves installing your production app or a test app making use of your library or engine using Google Play or other means onto a device or emulator running Android 17 Beta 4. Work through all your app's flows and look for functional or UI issues. Each release of Android contains platform changes that improve privacy, security, and overall user experience; review the app impacting behavior changes for apps running on and targeting Android 17 to focus your testing, including the following:

• Resizability on large screens: Once you target Android 17, you can no longer opt out of maintaining orientation, resizability and aspect ratio constraints on large screens.
• Dynamic code loading: If your app targets Android 17 or higher, the Safer Dynamic Code Loading (DCL) protection introduced in Android 14 for DEX and JAR files now extends to native libraries. All native files loaded using System.load() must be marked as read-only. Otherwise, the system throws UnsatisfiedLinkError.
• Enable CT by default: Certificate transparency (CT) is enabled by default. (On Android 16, CT is available but apps had to opt in.)
• Local network protections: Apps targeting Android 17 or higher have local network access blocked by default. Switch to using privacy preserving pickers if possible, and use the new ACCESS_LOCAL_NETWORK permission for broad, persistent access.
• Background audio hardening: Starting in Android 17, the audio framework enforces restrictions on background audio interactions including audio playback, audio focus requests, and volume change APIs. Based on your feedback, we've made some changes since beta 2, including targetSDK gating while-in-use FGS enforcement and exempting alarm audio. Full details available in updated guidance.

App memory limits

Android is introducing app memory limits based on the device's total RAM to create a more stable and deterministic environment for your applications and Android users. In Android 17, limits are set conservatively to establish system baselines, targeting extreme memory leaks and other outliers before they trigger system-wide instability resulting in UI stuttering, higher battery drain, and apps being killed. While we anticipate minimal impact on the vast majority of app sessions, we recommend the following memory best practices, including establishing a baseline for memory.

In the current implementation, getDescription in ApplicationExitInfo will contain the string "MemoryLimiter" if your app was impacted. You can also use trigger-based profiling with TRIGGER_TYPE_ANOMALY to get heap dumps that are collected when the memory limit is hit.

To help you find memory leaks, Android Studio Panda adds LeakCanary integration directly in the Android Studio Profiler as a dedicated task, contextualized within the IDE and fully integrated with your source code.

A lighter memory footprint translates directly to smoother performance, longer battery life, and a premium experience across all form factors. Let's build a faster, more reliable future for the Android ecosystem together!

Profiling triggers for app anomalies

Android introduces an on-device anomaly detection service that monitors for resource-intensive behaviors and potential compatibility regressions. Integrated with ProfilingManager, this service allows your app to receive profiling artifacts triggered by specific system-detected events.

Use the TRIGGER_TYPE_ANOMALY trigger to detect system performance issues such as excessive binder calls and excessive memory usage. When an app breaches OS-defined memory limits, the anomaly trigger allows developers to receive app-specific heap dumps to help identify and fix memory issues. Additionally, for excessive binder spam, the anomaly trigger provides a stack sampling profile on binder transactions.

This API callback occurs prior to any system imposed enforcements. For example, it can help developers collect debug data before the app is terminated by the system due exceeding memory limits. To understand how to use the trigger check out our documentation on trigger based profiling.

val profilingManager = applicationContext.getSystemService(ProfilingManager::class.java)
val triggers = ArrayList<ProfilingTrigger>()  
triggers.add(ProfilingTrigger.Builder(
             ProfilingTrigger.TRIGGER_TYPE_ANOMALY))
val mainExecutor: Executor = Executors.newSingleThreadExecutor()
val resultCallback = Consumer<ProfilingResult> { profilingResult ->
    if (profilingResult.errorCode != ProfilingResult.ERROR_NONE) {
        // upload profile result to server for further analysis          
        setupProfileUploadWorker(profilingResult.resultFilePath)
    } 
}
profilingManager.registerForAllProfilingResults(mainExecutor, resultCallback)
profilingManager.addProfilingTriggers(triggers)

Post-Quantum Cryptography (PQC) in Android Keystore

Android Keystore added support for the NIST-standardized ML-DSA (Module-Lattice-Based Digital Signature Algorithm). On supported devices, you can generate ML-DSA keys and use them to produce quantum-safe signatures, entirely in the device's secure hardware. Android Keystore exposes the ML-DSA-65 and ML-DSA-87 algorithm variants through the standard Java Cryptographic Architecture APIs: KeyPairGenerator, KeyFactory, and Signature. For further details, see our developer documentation.

KeyPairGenerator generator = KeyPairGenerator.getInstance(
        "ML-DSA-65", "AndroidKeyStore");
generator.initialize(
        new KeyGenParameterSpec.Builder(
                "my-key-alias",
                KeyProperties.PURPOSE_SIGN | KeyProperties.PURPOSE_VERIFY)
        .build());
KeyPair keyPair = generator.generateKeyPair();

Get started with Android 17

You can enroll any supported Pixel device to get this and future Android Beta updates over-the-air. If you don't have a Pixel device, you can use the 64-bit system images with the Android Emulator in Android Studio.

If you are currently in the Android Beta program, you will be offered an over-the-air update to Beta 4. Continue to report issues and submit feature requests on the feedback page. The earlier we get your feedback, the more we can include in our work on the final release.

For the best development experience with Android 17, we recommend that you use the latest preview of Android Studio (Panda). Once you're set up, here are some of the things you should do:

• Compile against the new SDK, test in CI environments, and report any issues in our tracker on the feedback page.
• Test your current app for compatibility, learn whether your app is affected by changes in Android 17, and install your app onto a device or emulator running Android 17 and extensively test it.

We'll update the preview/beta system images and SDK regularly throughout the Android 17 release cycle. Once you've installed a beta build, you'll automatically get future updates over-the-air for all later previews and Betas. For complete information, visit the Android 17 developer site.

Join the conversation

Your feedback remains our most valuable asset. Whether you're an early adopter on the Canary channel or an app developer testing on Beta 4, consider joining our communities and filing feedback. We're listening.