Gcp Cloud Architect

Select from recommended patterns:

• Serverless Web: Cloud Storage + Cloud CDN + Cloud Run + Firestore
• Microservices on GKE: GKE Autopilot + Cloud SQL + Memorystore + Cloud Pub/Sub
• Serverless Data Pipeline: Pub/Sub + Dataflow + BigQuery + Looker
• ML Platform: Vertex AI + Cloud Storage + BigQuery + Cloud Functions

See references/architecture_patterns.md for detailed pattern specifications.

Validation checkpoint: Confirm the recommended pattern matches the team's operational maturity and compliance requirements before proceeding to Step 3.

Step 3: Estimate Cost

Analyze estimated costs and optimization opportunities:

python scripts/cost_optimizer.py --resources current_setup.json --monthly-spend 2000

Example output:

{
  "current_monthly_usd": 2000,
  "recommendations": [
    { "action": "Right-size Cloud SQL db-custom-4-16384 to db-custom-2-8192", "savings_usd": 380, "priority": "high" },
    { "action": "Purchase 1-yr committed use discount for GKE nodes", "savings_usd": 290, "priority": "high" },
    { "action": "Move Cloud Storage objects >90 days to Nearline", "savings_usd": 75, "priority": "medium" }
  ],
  "total_potential_savings_usd": 745
}

Output includes:

• Monthly cost breakdown by service
• Right-sizing recommendations
• Committed use discount opportunities
• Sustained use discount analysis
• Potential monthly savings

Use the GCP Pricing Calculator for detailed estimates.

Step 4: Generate IaC

Create infrastructure-as-code for the selected pattern:

python scripts/deployment_manager.py --app-name my-app --pattern serverless_web --region us-central1

Example Terraform HCL output (Cloud Run + Firestore):

terraform {
  required_providers {
    google = {
      source  = "hashicorp/google"
      version = "~> 5.0"
    }
  }
}

provider "google" {
  project = var.project_id
  region  = var.region
}

variable "project_id" {
  description = "GCP project ID"
  type        = string
}

variable "region" {
  description = "GCP region"
  type        = string
  default     = "us-central1"
}

resource "google_cloud_run_v2_service" "api" {
  name     = "${var.environment}-${var.app_name}-api"
  location = var.region

  template {
    containers {
      image = "gcr.io/${var.project_id}/${var.app_name}:latest"
      resources {
        limits = {
          cpu    = "1000m"
          memory = "512Mi"
        }
      }
      env {
        name  = "FIRESTORE_PROJECT"
        value = var.project_id
      }
    }
    scaling {
      min_instance_count = 0
      max_instance_count = 10
    }
  }
}

resource "google_firestore_database" "default" {
  project     = var.project_id
  name        = "(default)"
  location_id = var.region
  type        = "FIRESTORE_NATIVE"
}

Example gcloud CLI deployment:

# Deploy Cloud Run service
gcloud run deploy my-app-api \
  --image gcr.io/$PROJECT_ID/my-app:latest \
  --region us-central1 \
  --platform managed \
  --allow-unauthenticated \
  --memory 512Mi \
  --cpu 1 \
  --min-instances 0 \
  --max-instances 10

# Create Firestore database
gcloud firestore databases create --location=us-central1

Full templates including Cloud CDN, Identity Platform, IAM, and Cloud Monitoring are generated by deployment_manager.py and also available in references/architecture_patterns.md.

Step 5: Configure CI/CD

Set up automated deployment with Cloud Build or GitHub Actions:

# cloudbuild.yaml