Beta: Firebase Genkit is in Beta, which means that it is not subject to any SLA or deprecation policy and could change in backwards-incompatible ways. Throughout the Beta period, Firebase Genkit and its documentation will be updated and improved.

Deploy flows using Cloud Run

You can deploy Genkit flows as HTTPS endpoints using Cloud Run. Cloud Run has several deployment options, including container based deployment; this page will explain how to deploy your flows directly from code.

Before you begin

Install the Google Cloud CLI.
You should be familiar with Genkit's concept of flows, and how to write them. This page assumes that you already have flows that you want to deploy.
It would be helpful, but not required, if you've already used Google Cloud and Cloud Run before.

1. Set up a Google Cloud project

If you don't already have a Google Cloud project set up, follow these steps:

Create a new Google Cloud project using the Cloud console or choose an existing one.
Link the project to a billing account, which is required for Cloud Run.
Configure the Google Cloud CLI to use your project:
```
gcloud init
```

2. Prepare your Node project for deployment

For your flows to be deployable, you will need to make some small changes to your project code:

Add start and build scripts to package.json

When deploying a Node.js project to Cloud Run, the deployment tools expect your project to have a start script and, optionally, a build script. For a typical TypeScript project, the following scripts are usually adequate:

"scripts": {
  "start": "node lib/index.js",
  "build": "tsc"
},

Add code to configure and start the flow server

In the file that's run by your start script, add a call to startFlowServer. This method will start an Express server set up to serve your flows as web endpoints.

When you make the call, specify the flows you want to serve:

ai.startFlowServer({
  flows: [menuSuggestionFlow],
});

There are also some optional parameters you can specify:

port: the network port to listen on. If unspecified, the server listens on the port defined in the PORT environment variable, and if PORT is not set, defaults to 3400.
cors: the flow server's CORS policy. If you will be accessing these endpoints from a web application, you likely need to specify this.
pathPrefix: an optional path prefix to add before your flow endpoints.
jsonParserOptions: options to pass to Express's JSON body parser

Optional: Define an authorization policy

All deployed flows should require some form of authorization; otherwise, your potentially-expensive generative AI flows would be invocable by anyone.

When you deploy your flows with Cloud Run, you have two options for authorization:

Cloud IAM-based authorization: Use Google Cloud's native access management facilities to gate access to your endpoints. See Authentication in the Cloud Run docs for information on providing these credentials.
Authorization policy defined in code: Use the authorization policy feature of Genkit flows to verify authorization info using custom code. This is often, but not necessarily, token-based authorization.

If you want to define an authorization policy in code, use the authPolicy parameter in the flow definition:

const myFlow = ai.defineFlow(
  {
    name: "myFlow",
    authPolicy: (auth, input) => {
      if (!auth) {
        throw new Error("Authorization required.");
      }
      // Custom checks go here...
    },
  },
  async () => {
    // ...
  }
);

The auth parameter of the authorization policy comes from the auth property of the request object. You typically set this property using Express middleware. See Authorization and integrity.

Make API credentials available to deployed flows

Once deployed, your flows need some way to authenticate with any remote services they rely on. Most flows will at a minimum need credentials for accessing the model API service they use.

For this example, do one of the following, depending on the model provider you chose:

Gemini (Google AI)

Make sure Google AI is available in your region.
Generate an API key for the Gemini API using Google AI Studio.
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.

Gemini (Vertex AI)

In the Cloud console, Enable the Vertex AI API for your project.
On the IAM page, ensure that the Default compute service account is granted the Vertex AI User role.

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.

3. Deploy flows to Cloud Run

After you've prepared your project for deployment, you can deploy it using the gcloud tool.

Gemini (Google AI)

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

Gemini (Vertex AI)

gcloud run deploy

The deployment tool will prompt you for any information it requires.

When asked if you want to allow unauthenticated invocations:

Answer Y if you're not using IAM and have instead defined an authorization policy in code.
Answer N to configure your service to require IAM credentials.

Optional: Try the deployed flow

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"}'