Firebase Genkit with Cloud Run

You can deploy Firebase Genkit flows as web services using Cloud Run. This page, as an example, walks you through the process of deploying the default sample flow.

  1. Install the required tools:

    1. Make sure you are using Node.js version 20 or higher (run node --version to check).

    2. Install the Google Cloud CLI.

  2. Create a new Google Cloud project using the Cloud console or choose an existing one. The project must be linked to a billing account.

    After you create or choose a project, configure the Google Cloud CLI to use it:

    gcloud init

  3. Create a directory for the Genkit sample project:

    mkdir -p ~/tmp/genkit-cloud-project
cd ~/tmp/genkit-cloud-project

    If you're going to use an IDE, open it to this directory.

  4. Initialize a Node.js project in your project directory:

    npm init -y

  5. Initialize Genkit in your Node.js project:

    genkit init
    • Select Google Cloud as the deployment platform.
    • Select the model provider you want to use.

    Accept the defaults for the remaining prompts. The genkit tool will create some sample source files to get you started developing your own AI flows. For the rest of this tutorial, however, you'll just deploy the sample flow.

  6. Make API credentials available to your deployed function. Do one of the following, depending on the model provider you chose:

    Gemini (Google AI)

    1. Make sure Google AI is available in your region.

    2. Generate an API key for the Gemini API using Google AI Studio.

    3. Make the API key available in the Cloud Run environment:

      1. In the Cloud console, enable the Secret Manager API.
      2. On the Secret Manager page, create a new secret containing your API key.
      3. After you create the secret, on the same page, grant your default compute service account access to the secret with the Secret Manager Secret Accessor role. (You can look up the name of the default compute service account on the IAM page.)

      In a later step, when you deploy your service, you will need to reference the name of this secret.

    4. Optional: If you want to run your flow locally, as in the next step, set the GOOGLE_GENAI_API_KEY environment variable to your key:

      export GOOGLE_GENAI_API_KEY=<your API key>

    Gemini (Vertex AI)

    1. In the Cloud console, Enable the Vertex AI API for your project.

    2. On the IAM page, ensure that the Default compute service account is granted the Vertex AI User role.

    3. Optional: If you want to run your flow locally, as in the next step, set some additional environment variables and use the gcloud tool to set up application default credentials:

      export GCLOUD_PROJECT=<your project ID>
export GCLOUD_LOCATION=us-central1
gcloud auth application-default login

    The only secret you need to set up for this tutorial is for the model provider, but in general, you must do something similar for each service your flow uses.

  7. Optional: Try your flow in the developer UI:

    1. Start the UI:

      genkit start

    2. In the developer UI (http://localhost:4000/), run the flow:

      1. Click menuSuggestionFlow.

      2. On the Input JSON tab, provide a subject for the model:

        "banana"

      3. Click Run.

  8. If everything's working as expected so far, you can build and deploy the flow:

    Gemini (Google AI)

    npm run build
gcloud run deploy --update-secrets=GOOGLE_GENAI_API_KEY=<your-secret-name>:latest

    Gemini (Vertex AI)

    npm run build
gcloud run deploy

    Choose N when asked if you want to allow unauthenticated invocations. Answering N will configure your service to require IAM credentials. See Authentication in the Cloud Run docs for information on providing these credentials.

After deployment finishes, the tool will print the service URL. You can test it with curl:

curl -X POST https://<service-url>/menuSuggestionFlow \
-H "Authorization: Bearer $(gcloud auth print-identity-token)" \
-H "Content-Type: application/json" -d '{"data": "banana"}'