Skip to main content

​
Overview

Deploying Facet AI on your own infrastructure gives you complete control over your fine-tuning platform, data, and costs. This guide covers deploying both the backend services and frontend application.
Production Deployment: This deployment is recommended for production use, enterprise customers, or when you need full control over your infrastructure.

​
Architecture

Facet AI consists of two main components:

Cloud Services

  • Dataset preprocessing microservice
  • Training orchestration microservice
  • Training, inference, evaluation GPU workers
  • Model export service
  • API Gateway for unified access

Frontend Application

  • Next.js web application
  • User authentication and dashboard
  • Model management interface
  • Real-time training monitoring

​
Prerequisites

1

Google Cloud Platform Setup

  • Create GCP Project
  • Install Required Tools
  1. Go to Google Cloud Console
  2. Create a new project or select an existing one
  3. Note your project ID for later use
  4. Enable billing on your project
Some APIs require billing to be enabled. Make sure billing is active before deployment.
2

Authentication Setup

  1. Authenticate with Google Cloud: 
    gcloud auth login
gcloud auth application-default login
  2. Set your project: 
    gcloud config set project YOUR_PROJECT_ID
  3. Verify you have the correct permissions (Owner or Editor role)
Verify setup with: gcloud auth list and gcloud config get-value project
Our Terraform IaC includes both production and staging setups. The staging workspace is intended mainly for development and testing; see the staging section for details.

​
Deploy Backend Services

1

Clone and Setup

  1. Clone the cloud services repository: 
    git clone https://github.com/gemma-facet/cloud-services
cd cloud-services/infrastructure
  2. Copy and edit the configuration files for your environment: 
    cp environments/staging.tfvars.example environments/staging.tfvars
cp environments/production.tfvars.example environments/production.tfvars
  3. Edit terraform.tfvars or the appropriate *.tfvars with your project details: 
    project_id = "your-project-id"
region = "us-central1"
2

Initialize and Deploy

Use our automated deployment script:
make init ENV=production
make full-deploy ENV=production
make output ENV=production
The make full-deploy command performs these steps automatically:
  1. Deploy core infrastructure (APIs, IAM, Artifact Registry, Storage, Firebase, networking)
  2. Build and push Docker images to Artifact Registry
  3. Deploy microservices and configure networking (Cloud Run and API Gateway)
Expect the deployment to take ~15-30 minutes depending on region and project size. The process runs unattended in most cases.
3

Our IaC configures Firebase authentication (email/password) for you so the frontend can run out of the box. It does not enable Google OAuth by default. To add Google sign-in, go to Firebase Console → Authentication → Sign-in method and enable Google.

​
Deploy Frontend Application

1

Clone Frontend Repository

git clone https://github.com/gemma-facet/frontend-ui
cd frontend-ui
2

Configure Environment Variables

  1. Copy the example environment file: 
    cp .env.example .env
  2. Fill in the environment variables from your backend deployment: 
    # From terraform output
INFERENCE_SERVICE_URL=https://your-inference-service-url
API_GATEWAY_URL=https://your-api-gateway-url

# Also from terraform output
NEXT_PUBLIC_FIREBASE_API_KEY=your-firebase-api-key
NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN=your-project-id.firebaseapp.com
NEXT_PUBLIC_FIREBASE_PROJECT_ID=your-project-id
NEXT_PUBLIC_FIREBASE_STORAGE_BUCKET=your-project-id.appspot.com
NEXT_PUBLIC_FIREBASE_MESSAGING_SENDER_ID=your-messaging-sender-id
NEXT_PUBLIC_FIREBASE_APP_ID=your-firebase-app-id
3

Running the Frontend

  • Run Locally
  • Deploy to Vercel
We recommend using bun for development, but npm or yarn also work.
npm run install
npm run build
npm run start
Access the app at http://localhost:3000 in your browser.

​
Infrastructure Management

​
Available Commands

make init           # Initialize Terraform
make check          # Validate configuration
make full-deploy    # Complete deployment workflow
make deploy-core    # Deploy core infrastructure only
make deploy-services # Deploy microservices only

make plan           # Plan infrastructure changes
make output         # Show deployment outputs
make destroy        # Destroy all infrastructure
make help           # Show all available commands

make build          # Build all containers
make deploy         # Deploy infrastructure only

​
Staging

The staging workspace in the Terraform setup is mainly intended for development and testing. If you’d like a staging environment for your own development, follow the guide in our repo to set up a staging workspace in three commands: https://github.com/gemma-facet/cloud-services/tree/main/infrastructure
Running npm run dev in the frontend will automatically connect to the staging backend since we use Next.js environment variables to differentiate between staging and production. You can manually edit this by changing the .env file.

​
Infrastructure Components

Core Infrastructure

  • Google Cloud APIs and IAM roles
  • Artifact Registry for container images
  • Service accounts and permissions

Storage Layer

  • Google Cloud Storage buckets
  • Firestore database for metadata
  • Persistent disk volumes

Compute Services

  • Cloud Run services for microservices
  • Cloud Build for container building
  • Cloud Scheduler for batch jobs

Networking

  • API Gateway for unified access
  • VPC and firewall rules
  • Load balancing and SSL certificates

​
Troubleshooting

Terraform errors:
  • Ensure you have Owner/Editor role on the GCP project
  • Check that billing is enabled
  • Verify all required APIs are enabled
Authentication errors:
  • Run gcloud auth application-default login
  • Set quota project: gcloud auth application-default set-quota-project YOUR_PROJECT_ID
Cloud Run services not starting:
  • Check container logs in Cloud Console
  • Verify environment variables are set correctly
  • Ensure service account has proper permissions
Firebase authentication not working:
  • Enable Google sign-in in Firebase Console
  • Verify OAuth client configuration
  • Check Firebase config in frontend environment variables
Resources already exist:
# Import existing resources to Terraform
terraform import module.compute.google_cloud_run_v2_service.preprocessing_service \
  projects/YOUR_PROJECT_ID/locations/us-central1/services/preprocessing-service
Need help with deployment? Check our GitHub repository for the latest updates and open an issue if you encounter problems.
⌘I