FAQ and troubleshooting

Why did the name change from "Vertex AI in Firebase" to "Firebase AI Logic"?

Back in 2024, we launched a set of Firebase client SDKs that could use the Vertex AI Gemini API as well as a Firebase proxy gateway to protect that API from abuse and to enable integrations with other Firebase products. We called our product "Vertex AI in Firebase", and this product name accurately described our product's available use cases at that time.

Since then, though, we've expanded the capabilities of our product. For example, as of May 2025, we now offer support for the Gemini Developer API, including the ability to protect the Gemini Developer API from abuse using our integration with Firebase App Check.

As a result, the name "Vertex AI in Firebase" no longer accurately represents the expanded scope of our product. Thus, a new name — Firebase AI Logic — better reflects our evolving feature set and allows us to continue to expand our offerings in the future!

Check out the migration guide to make sure you get all the latest features from Firebase AI Logic (and optionally start using the Gemini Developer API).

What are the differences between using the Gemini Developer API and the Vertex AI Gemini API?

The following table lists important differences between the two "Gemini API" providers in general regardless of how you access them:

¹ The two API providers have different pay-as-you-go pricing (learn more in their respective documentation).

² The Files API for the Gemini Developer API is not supported through the Firebase AI Logic SDKs.

The following table lists the availability of commonly asked about features for the two "Gemini API" providers. This table applies specifically when using the Firebase AI Logic client SDKs.

Can I use both the Gemini Developer API and the Vertex AI Gemini API?

Yes, you can have both "Gemini API" providers enabled in your Firebase project, and you can use both APIs in your app itself.

To switch between API providers in your code, just make sure that you've set the backend service appropriately in your code.

What are the required APIs? And how do I enable them?

To use the Firebase AI Logic SDKs with the Gemini Developer API, your project must have the following two APIs enabled:

• Gemini Developer API (generativelanguage.googleapis.com)
• Firebase AI Logic API (firebasevertexai.googleapis.com)

You should enable these two APIs using the Firebase console:

1. In the Firebase console, go to AI Services > AI Logic.

2. Click Get started.

3. Select to get started with the Gemini Developer API.

   This launches a guided workflow that enables the two APIs for you. The console will also generate a Gemini API key, as well as add the Firebase AI Logic API to the allowlist for your Firebase API key.

How do I disable APIs in my Firebase project?

For Firebase AI Logic, we try to make setup of your Firebase project to use your chosen Gemini API provider as easy as possible. This includes enabling the required APIs in your Firebase project during specific journeys, like the guided workflow in the Firebase console.

However, if you decide to not use Firebase AI Logic or one of the Gemini API providers, you can disable the associated APIs in your Firebase project.

Which models can be used with the Firebase AI Logic SDKs?

See lists of supported models. We frequently add new capabilities to the SDKs, so check back on this FAQ for updates (as well as in release notes, blogs, and social posts).

• Gemini Developer API

  • Both Gemini and Imagen foundation models.

    Note that the Gemini Developer API (regardless of how it's accessed) only supports specific stable Imagen models.

• Vertex AI Gemini API

  • Both Gemini and Imagen foundation models.

• Regardless of your chosen Gemini API provider

  • Firebase AI Logic does not support the following:

    • Non-foundation Gemini models (like PaLM models, tuned models, or Gemma-based models).

    • Older Imagen models or imagen-3.0-capability-001.

What to do when models are shut down (retired)?

When we release a stable model version, we strive to ensure that it's available for at minimum one year before retiring the model.

Where to find the shutdown date of a model?

Here are some ways you can find the shutdown date of a model:

• At the time of release: We list each model's expected shutdown date several places in the Gemini API provider documentation as well as in the Firebase documentation (see the supported models page).

• As the shutdown date approaches: Google sends emails to appropriate project members, and we post reminders in the release notes and other channels about any upcoming shutdowns (for example, reminder of shutdown dates for the Gemini 1.5 and 1.0 stable models).

What to do if the model you're using is about to be shut down?

1. Find a suitable currently supported model and its model name.

2. Update the model name used by your app before the shutdown date; otherwise, any requests to that model will fail with a 404 error.

   • You set the model name during initialization when you create a GenerativeModel, LiveModel, or ImagenModel instance. Make sure to review the critical recommendation below about using Firebase Remote Config.

   • When using Firebase AI Logic, you usually don't need to modify any of the code which actually calls the model.

3. Test your app to ensure that responses are still as expected.

Imagen stable models - shutdown dates & replacements

• All Imagen models will shut down on June 24, 2026.

Replacement: Migrate your apps to use Gemini Image models (the "Nano Banana" models).

Gemini 2.0 stable models - shutdown dates & replacements

• Gemini 2.0 Flash and Gemini 2.0 Flash‑Lite:

  gemini-2.0-flash-001 (and its auto-updated alias gemini-2.0-flash)
  gemini-2.0-flash-lite-001 (and its auto-updated aliasgemini-2.0-flash-lite)

  • February 6, 2026: These models will no longer be available for use in projects that had no prior usage of the model.

  • March 31, 2026: These models will shut down.

  Note that stable Gemini Live API 2.0 models are not impacted.

Replacement: Migrate to use a Gemini 2.5 model (like gemini-2.5-flash-lite) or try one of the new Gemini 3 models (like gemini-3.1-flash-lite-preview).

Gemini 1.5 and 1.0 stable models - shutdown dates & replacements

• Gemini 1.5 Pro models:

  • gemini-1.5-pro-002 (and gemini-1.5-pro): September 24, 2025
  • gemini-1.5-pro-001: May 24, 2025

• Gemini 1.5 Flash models:

  • gemini-1.5-flash-002 (and gemini-1.5-flash): September 24, 2025
  • gemini-1.5-flash-001: May 24, 2025

• Gemini 1.0 Pro Vision models: April 21, 2025 (previously scheduled for April 09, 2025)

• Gemini 1.0 Pro models: April 21, 2025 (previously scheduled for April 09, 2025)

Replacement: Migrate to use a Gemini 2.5 model (like gemini-2.5-flash-lite) or try one of the new Gemini 3 models (like gemini-3.1-flash-lite-preview).

How do I set a per-user rate limit?

By default, Firebase AI Logic sets the request limit per user at 100 requests per minute (RPM).

If you want to adjust your per-user rate limit, you need to adjust the quota settings for the Firebase AI Logic API.

Learn more about the Firebase AI Logic API quota. On that page, you can also learn how to view and edit your quota.

Which permissions may be needed to use the Firebase AI Logic SDKs?

Does Firebase AI Logic use my data to train models?

See Data governance & Responsible AI.

Is MIME type required in my multimodal requests? (like for images, PDFs, video, and audio input)

Yes, in each multimodal request, you must always provide the following:

• The file's mimeType. See an exception below.

• The file. You can either provide the file as inline data or provide the file using its URL.

Learn about supported input file types, how to specify MIME type, and the two options for providing the file in Supported input files and requirements.

Exception to including MIME type in your request

An exception to providing the MIME type is inline image inputs for requests from native Android and Apple platform apps.

The Firebase AI Logic SDKs for Android and Apple platforms provide a simplified and platform-friendly way to handle images in requests — all images (no matter their format) are converted client-side to JPEG at 80% quality before being sent to the server. This means that when you provide images as inline data using the Android and Apple platforms SDKs, you don't need to specify the MIME type in the request.

This simplified handling is shown in the Firebase AI Logic documentation in the examples for sending base64-encoded images in requests.

Here's some additional platform-specific information about this feature:

• For Android:

  • You can take advantage of the simplified way to handle platform-native image types (Bitmap) in multimodal prompts that contain images as inline data (see example).

  • For more control over image formats and conversions, you may provide the images as an InlineDataPart and supply the specific MIME type. For example:

    content { inlineData(/* PNG as byte array */, "image/png") }

• For Apple platforms:

  • You can take advantage of the simplified way to handle platform-native image types (UIImage, NSImage, CIImage, and CGImage) in multimodal prompts that contain images as inline data (see example).

  • For more control over image formats and conversions, you may provide the images as an InlineDataPart and supply the specific MIME type. For example:

    InlineDataPart(data: Data(/* PNG Data */), mimeType: "image/png")

Are these features available when using Firebase AI Logic: grounding with Google Image Search, grounding with Google Maps, fine tuning a model, embeddings generation, and semantic retrieval?

The following features are supported by various models and the API providers, but they are not available when using Firebase AI Logic:

• Grounding with Google Image Search
• Grounding with Google Maps
• Fine tuning a model
• Embeddings generation
• Semantic retrieval

If you would like to add these as feature requests or vote on an existing feature request, visit Firebase UserVoice.

For Swift apps on Apple platforms - What to do about the module name change from FirebaseAI to FirebaseAILogic?

For Apple platform apps, starting with Firebase SDK v12.5.0, Firebase AI Logic is now distributed under the FirebaseAILogic module. We've made this change non-breaking and backwards-compatible.

Why did we make this change?

We formerly distributed this service under the FirebaseAI module. However, we needed to rename it to FirebaseAILogic for the following reasons:

• Avoid a name collision between module and class that causes issues in binary distributions.

• Enable us to use Swift macros for future feature development.

What to do if you're upgrading to v12.5.0+?

The module name change to FirebaseAILogic is non-breaking and backwards-compatible. However, eventually, we may remove the old module alongside a future major Firebase SDK breaking change release (time frame currently undetermined).

There are no required changes for this module name change, but we recommend that you do the following:

1. When choosing Swift PM dependencies, choose FirebaseAILogic (instead of FirebaseAI).

2. Change import statements to FirebaseAILogic (instead of FirebaseAI).

What's a Gemini API key?

The Gemini Developer API uses a "Gemini API key" to authorize the caller. So, if you're using the Gemini Developer API through the Firebase AI Logic SDKs, then you need a valid Gemini API key in your Firebase project to make calls to that API.

A "Gemini API key" just means an API key that has the Gemini Developer API in its API allowlist.

When you go through the Firebase AI Logic setup workflow in the Firebase console, we create a Gemini API key that's restricted to only the Gemini Developer API, and we set up the Firebase AI Logic proxy service to use this API key. This Firebase-generated Gemini API key is named Gemini Developer API key (auto created by Firebase) in the credentials page of the Google Cloud console.

Learn more about API restrictions for API keys.

You do not add your Gemini API key to your app's codebase when using the Firebase AI Logic SDKs. Learn more about how to keep your Gemini API key secure.

Should I add my Gemini API key into my mobile or web app's codebase?

When using the Firebase AI Logic SDKs, do not add your Gemini API key into your app's codebase.

In fact, while developing with the Firebase AI Logic SDKs, you don't directly interact with your Gemini API key. Instead, our Firebase AI Logic proxy service will internally include the Gemini API key in each request to the Gemini Developer API — completely in the backend.

How can I change the Gemini API key used to call the Gemini Developer API?

When using the Firebase AI Logic SDKs, it's unlikely that you'll need to change your Gemini API key. However, here are two cases where you might need to:

• If you accidentally leaked the key and want to replace it with a new secure key.

• If you accidentally deleted the key. Note that you can undelete the key within 30 days of deletion.

Here's how you change the Gemini API key that's used by the Firebase AI Logic SDKs:

1. If your Firebase-generated Gemini API key still exists, delete it.

   You can delete this API key in the APIs & Services > Credentials panel of the Google Cloud console. It's named:
   Gemini Developer API key (auto created by Firebase).

2. In that same page of the Google Cloud console, create a new API key. We suggest naming it something like:
   Gemini Developer API key for Firebase.

3. To this new API key, add API restrictions and only select Generative Language API.
   "Generative Language API" is what the Gemini Developer API is sometimes called in the Google Cloud console.

   Do not add any app restrictions; otherwise the Firebase AI Logic proxy service won't work as expected.

4. Run the following command to set this new key as the Gemini API key that the Firebase AI Logic proxy service should use.

   PROJECT_ID="PROJECT_ID"
   GENERATIVE_LANGUAGE_API_KEY="DEVELOPER_CREATED_GEMINI_API_KEY"
   curl \
     -X PATCH \
     -H "x-goog-user-project: ${PROJECT_ID}" \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json" \
     "https://firebasevertexai.googleapis.com/v1beta/projects/${PROJECT_ID}/locations/global/config" \
     -d "{\"generativeLanguageConfig\": {\"apiKey\": \"${GENERATIVE_LANGUAGE_API_KEY}\"}}"
   

   Learn about the gcloud CLI.

Make sure to not add this new Gemini API key to your app's codebase. Learn more about how to keep your Gemini API key secure.

Can I use my "Firebase API key" as my Gemini API key?

No — you should not use your "Firebase API key" as your Gemini API key. We strongly recommend that you do not add the Gemini Developer API to the allowlist for your Firebase API key.

Your Firebase API key is the API key that's listed in your Firebase configuration file or object that you add into your app's codebase to connect your app to Firebase. It's OK to include your Firebase API key in your code when you use the key only with Firebase-related APIs (like Firebase AI Logic). Learn important information about Firebase API keys.

In the APIs & Services > Credentials panel of the Google Cloud console, this is what Firebase API keys look like:

Because you need to add your Firebase API key into your app's codebase for Firebase-related APIs to work, and because the Gemini Developer API is authorized via API key, we strongly recommend that you do NOT add the Gemini Developer API (called the "Generative Language API" in the Google Cloud console) to the API allowlist for your Firebase API key. If you do, then you're exposing the Gemini Developer API to potential abuse.

How do I keep my Gemini API key secure?

This FAQ describes some recommended best practices to keep your Gemini API key secure.

If you're calling the Gemini Developer API directly from your mobile or web app:

• Use the Firebase AI Logic client SDKs.
• Do not add your Gemini API key into your app's codebase.

Firebase AI Logic provides a proxy service that internally includes your Gemini API key in each request to the Gemini Developer API — completely in the backend.

Additionally, we strongly recommend the following:

• As soon as you start seriously developing your app, integrate with Firebase App Check to help protect your backend resources as well as the APIs used to access generative models.

• Do not reuse the Firebase-generated Gemini API key outside of Firebase AI Logic. If you need a Gemini API key for another use case, create a separate key.

• In general, you should NOT modify the Firebase-generated Gemini API key. This key is named Gemini Developer API key (auto created by Firebase) in the Google Cloud console.

  • Do not add any additional APIs to the API allowlist for your Firebase-generated Gemini API key. In its API allowlist, your Gemini API key should only have the Gemini Developer API (called the "Generative Language API" in the Google Cloud console).

  • Do not add any app restrictions; otherwise the Firebase AI Logic proxy service won't work as expected.

My Gemini API key was compromised. What do I need to do?

If your Gemini API key has been compromised, follow the instructions to change the Gemini API key that's used to call the Gemini Developer API.

Also, review the recommended best practices to keep your Gemini API key secure.