Vertex AI Gemini API

If you're new to the Gemini API, the Gemini Developer API is the recommended API provider for Android Developers. But if you have specific data location requirements or you are already embedded in the Vertex AI or Google Cloud environment, you can use the Vertex AI Gemini API.

Getting started

Before you interact with the Vertex AI Gemini API directly from your app, you can experiment with prompts in Vertex AI Studio.

Set up a Firebase project and connect your app to Firebase

Once you're ready to call the API from your app, follow the instructions in "Step 1" of the Firebase AI Logic getting started guide to set up Firebase and enable required APIs and services.

Add the Gradle dependencies

Add the following Gradle dependencies to your app module:

dependencies {
  // ... other androidx dependencies

  // Import the BoM for the Firebase platform
  implementation(platform("com.google.firebase:firebase-bom:34.15.0"))

  // Add the dependencies for the Firebase AI Logic and App Check libraries
  // When using the BoM, you don't specify versions in Firebase library dependencies
  implementation("com.google.firebase:firebase-ai")
  implementation("com.google.firebase:firebase-appcheck-debug")
}

dependencies {
  // Import the BoM for the Firebase platform
  implementation(platform("com.google.firebase:34.15.0"))

  // Add the dependencies for the Firebase AI Logic and App Check libraries
  // When using the BoM, you don't specify versions in Firebase library dependencies
  implementation("com.google.firebase:firebase-ai")
  implementation("com.google.firebase:firebase-appcheck-debug")

  // Required for one-shot operations (to use `ListenableFuture` from Guava Android)
  implementation("com.google.guava:guava:31.0.1-android")

  // Required for streaming operations (to use `Publisher` from Reactive Streams)
  implementation("org.reactivestreams:reactive-streams:1.0.4")
}

Configure the App Check debug provider for local development

Starting early July 2026, as part of the guided setup workflow for AI Logic in the Firebase console, Firebase App Check is automatically enforced to protect the Gemini API. For local development, you need to configure the App Check debug provider to bypass attestation while still maintaining the enforcement of App Check.

1. In your debug build, configure App Check to use the debug provider factory:

   Kotlin

   Firebase.initialize(context = this)
   Firebase.appCheck.installAppCheckProviderFactory(
       DebugAppCheckProviderFactory.getInstance(),
   )
   

   Java

   FirebaseApp.initializeApp(/*context=*/ this);
   FirebaseAppCheck firebaseAppCheck = FirebaseAppCheck.getInstance();
   firebaseAppCheck.installAppCheckProviderFactory(
           DebugAppCheckProviderFactory.getInstance());
   

2. Obtain your debug token:

   1. Run your app in the emulator or on your test device.

   2. Look for the App Check debug token in your logs. For example:

      D DebugAppCheckProvider: Enter this debug secret into the allow list
      in the Firebase Console for your project: 123a4567-b89c-12d3-e456-789012345678
      

   3. Copy the token (for example, 123a4567-b89c-12d3-e456-789012345678).

3. Register your debug token with App Check:

   1. In the Firebase console, go to the Security > App Check > Apps tab.

   2. Find your app, click the overflow menu (more_vert), and then select Manage debug tokens.

   3. Follow the on-screen instructions to register your debug token.

For details about the debug provider (including how to get a new debug token), check out the official App Check docs.

Initialize the generative model

Start by instantiating a GenerativeModel and specifying the model name:

val model = Firebase.ai(backend = GenerativeBackend.vertexAI())
    .generativeModel("gemini-2.5-flash")
VertexAiGeminiApi.kt

GenerativeModel firebaseAI = FirebaseAI.getInstance(GenerativeBackend.vertexAI())
        .generativeModel("gemini-2.5-flash");

GenerativeModelFutures model = GenerativeModelFutures.from(firebaseAI);
VertexAiGeminiApiJava.java

In the Firebase documentation, you can learn more about the available Gemini models. You can also learn about configuring model parameters.

Generate text

To generate a text response, call generateContent() with your prompt.

suspend fun generateText(model: GenerativeModel) {
    // Note: generateContent() is a suspend function, which integrates well
    // with existing Kotlin code.
    val response = model.generateContent("Write a story about a magic backpack.")
    // ...
}
VertexAiGeminiApi.kt

Content prompt = new Content.Builder()
        .addText("Write a story about a magic backpack.")
        .build();

ListenableFuture<GenerateContentResponse> response = model.generateContent(prompt);
Futures.addCallback(response, new FutureCallback<GenerateContentResponse>() {
    @Override
    public void onSuccess(GenerateContentResponse result) {
        String resultText = result.getText();
        // ...
    }

    @Override
    public void onFailure(Throwable t) {
        t.printStackTrace();
    }
}, executor);
VertexAiGeminiApiJava.java

Similar to the Gemini Developer API, you can also pass images, audio, video, and files with your text prompt. For details, see Interact with the Gemini Developer API from your app.

To learn more about Firebase AI Logic SDK, read the Firebase documentation.