Build intelligent Android apps: Cloud and hybrid inference

Posted by Thomas Ezan, Jolanda Verhoef, Caren Chang, Senior Developer Relations Engineers, Android Developer Relations Welcome back to the blog post series " Build intelligent Android apps " where we take a basic Android app and transform it into a personalized , intelligent , and agentic experience. In our previous post we explored how to build intelligent on-device features using Gemini Nano through ML Kit's Prompt API. In this post, we will look at how you can leverage Firebase AI Logic to build cloud-hosted and hybrid AI features:  Grounding answers in real-world context Routing requests dynamically between cloud and local execution using hybrid inference Translating content with custom routing systems Sometimes a use case requires AI models with greater world knowledge, a much larger context window, or the ability to handle complex queries.

  Other times, you want the best of both worlds: using hybrid inference to run on-device when available to lower costs, while falling back to the cloud to ensure compatibility for all devices. Cloud and hybrid features in Jetpacker: Museum assistant with web grounding, hybrid restaurant review drafting, and  support chat featuring custom-routed live translation. Let’s look at how we implemented three cloud and hybrid features in Jetpacker : a museum assistant with web grounding hybrid restaurant review drafting hotel support chat featuring custom-routed live translation.

Use LLM grounding for up-to-date informationMuseum assistant chatbot with LLM grounding The Museum assistant is an interactive chatbot designed to help users plan their museum visits. It provides visitors with up-to-date details regarding specific exhibits, current opening hours, ticket pricing, and more. ’ When building AI features, getting the model to answer with fresh, accurate, and specific real-world information is a common challenge.

  Grounding data is added to the context window to enable the model  to answer questions correctly and accurately. To bridge this gap, we can use grounding techniques to add extra context to the model’s context window. g.

current ticket prices or museum rules). Google Search grounding : Letting the model query the real-time Google search index for up-to-date details. Maps grounding : Using Google Maps location data.

generativeModel( modelName = "gemini-3-flash", systemInstruction = content { text("You are a helpful museum assistant answering questions about a museum. sendMessage(prompt) Hybrid inference: On-device review generation with Maps deep link Not every AI task requires a cloud-based model, and not every device is online. To help developers balance latency, cost, and offline availability, we recently introduced the Firebase API for Hybrid Inference .

In Jetpacker, the restaurant review feature lets users review select topics and automatically drafts a review. PREFER_ON_DEVICE ) ) The Hybrid Inference API supports four distinct routing modes: PREFER_ON_DEVICE: Prioritizes local execution and falls back to cloud if Gemini Nano is unavailable. PREFER_IN_CLOUD: Prioritizes cloud execution and falls back to on-device if the device goes offline.

ONLY_ON_DEVICE: Restricts execution strictly to the device. ONLY_IN_CLOUD: Restricts execution strictly to the cloud. startActivity(intent) } Custom hybrid routing: Hotel support chat translation with simulated personas The hotel support chat was built to let users finalize logistics and check on hotel details.

This feature uses system instructions to configure a localized receptionist assistant. By passing specific information—such as the preferred language and hotel information—in the instructions, we can set up a conversational persona representing a specific hotel. generativeModel( systemInstruction = content { text(""" You are a helpful hotel receptionist at $hotelName only speaking $language.

Answer politely in $language. The bar closes at 10pm and breakfast is from 7am to 10am. There's someone at the desk 24/7.

You can retrieve your luggage from the storage room at the back of the lobby at any time.   Hotel support chat messages are automatically translated to the user’s preferred language  While hybrid models can configure simple routing preferences, complex scenarios require custom routing logic. In Jetpacker, we implement a custom routing stack that takes into account: Language identification: Using the on-device ML Kit Language Identification API , we can detect the incoming message language.

On-device translation (Gemini Nano): ML Kit’s Prompt API lets us translate common language pairs directly on the device, saving bandwidth and cloud cost. Cloud translation (Gemini 3 Flash): For more complex languages, we use Gemini Flash 3 to get a higher quality translation. launch { // 1.

text)) } catch (e: Exception) { "Undefined" } // 2. = "kr" val prompt = "Translate the following text to $selectedLanguage. id to "$routePrefix: $translatedText") } } } } In this example, the custom routing logic only takes into consideration the translation’s source and target language.

However, based on your app’s use case, you can expand the routing logic to include other factors such as the on-device model version, network connectivity, battery status, and more. Securing the AI Pipelines: Firebase App Check Lastly, using AI in the cloud opens up possibilities of API key abuse or unauthorized billing. To secure API calls, we integrated Firebase App Check using both Play Integrity (production) and the local Debug Provider (for local development or emulators).

signInAnonymously() } When building locally on an emulator, App Check prints a local token secret to logcat: Enter this debug secret into the allow list in the Firebase Console: a8c2dd4c-xxxx-xxxx-xxxx-ef6c114ba27e Once registered in the Firebase console, local requests are fully verified and authenticated by App Check, protecting our backend while letting us test the app locally. Conclusion By combining cloud model capabilities (grounding, system instructions) with on-device capabilities (hybrid routing, translation, security app checks), we created a travel app that is smart, secure, and available offline. Check out the full source code for Jetpacker on GitHub , and explore the Firebase documentation to get started: Firebase AI Logic Documentation Firebase Hybrid Inference API Learn more Check out the other parts of this blog post series: Part 1 : Introduction of the app and a high-level overview.

Part 2 : On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing. ): Hybrid and cloud reasoning.

Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context. Part 4: System integration.   Part 5 (coming soon): In-app agentic workflows.

Extend the app with an end-to-end booking assistant powered by A2UI and ADK. Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn !

All code snippets in this blog post follow the following copyright notice: Copyright 2026 Google LLC.