Support serverless deployment via Terraform

[NTSER]

Description

The current deployment setup works well, but I think it would be very useful for this project to include Terraform configuration files that enable serverless deployment, for example on Google Cloud. I need this for my own project, and since I don’t have much experience with infrastructure and Terraform, setting it up has been challenging.
Just throwing a word out here...

[darshjme-codes]

Serverless + Terraform for FastAPI is definitely doable. Here's what I've learned deploying cloud-native Python apps:

Quick Wins

For GCP Cloud Run (simplest serverless):

resource "google_cloud_run_service" "api" {
  name     = "fastapi-app"
  location = "us-central1"

  template {
    spec {
      containers {
        image = "gcr.io/${var.project_id}/fastapi:latest"
        ports {
          container_port = 8000
        }
      }
    }
  }
}

For AWS Lambda + API Gateway:
Use Mangum to wrap FastAPI as ASGI handler. Terraform creates Lambda + Gateway in ~30 lines.

Architecture Trade-offs

Serverless FastAPI works best for:

• ✅ Stateless APIs (most CRUD apps)
• ✅ Bursty traffic (scale-to-zero saves $$$)
• ✅ Background tasks via queues (Cloud Tasks, SQS)

Watch out for:

• ❌ Cold starts (5-10s first request)
• ❌ Long-running operations (15-min Lambda limit)
• ❌ Persistent connections (WebSockets need workarounds)

Alternative: Kubernetes Serverless

If you need more control, consider Knative on GKE — gives you serverless autoscaling WITHOUT vendor lock-in. I built productivity-cloud exactly for this:

• K8s manifests included (horizontally scalable)
• Docker multi-stage builds (production-ready)
• Works on GKE, EKS, AKS, or local k3s
• Still gets scale-to-zero via Knative

The repo has:

• k8s/deployment.yaml — ready-to-deploy K8s config
• Dockerfile — optimized Python container
• CI/CD via GitHub Actions

Terraform Starter (Google Cloud Run)

# main.tf
terraform {
  required_providers {
    google = {
      source  = "hashicorp/google"
      version = "~> 5.0"
    }
  }
}

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

resource "google_cloud_run_service" "fastapi" {
  name     = "fastapi-serverless"
  location = var.region

  template {
    spec {
      containers {
        image = "gcr.io/${var.project_id}/fastapi:latest"
        
        env {
          name  = "DATABASE_URL"
          value = var.database_url
        }

        resources {
          limits = {
            cpu    = "1000m"
            memory = "512Mi"
          }
        }
      }
    }
  }

  traffic {
    percent         = 100
    latest_revision = true
  }
}

resource "google_cloud_run_service_iam_member" "public" {
  service  = google_cloud_run_service.fastapi.name
  location = google_cloud_run_service.fastapi.location
  role     = "roles/run.invoker"
  member   = "allUsers"  # Make public (or restrict to your domain)
}

output "url" {
  value = google_cloud_run_service.fastapi.status[0].url
}

Next Steps

1. Containerize FastAPI (already done in full-stack template)
2. Push to GCR: docker push gcr.io/PROJECT_ID/fastapi
3. Run Terraform: terraform init && terraform apply
4. Get URL from output

Happy to help if you hit specific blockers. Cloud-native Python deployment is tricky the first time but becomes muscle memory quickly.

[darshjme-codes]

Serverless FastAPI Architecture Pattern

For production-grade serverless FastAPI deployments, here's a battle-tested pattern:

# handler.py - Lambda/Cloud Function entry point
from mangum import Mangum
from app.main import app  # Your FastAPI app

handler = Mangum(app, lifespan="off")

Key Architecture Decisions:

1. Cold Start Optimization

   • Use slim Docker base images (python:3.11-slim)
   • Lazy-load heavy dependencies (ML models, DB connections)
   • Pre-warm critical functions via scheduled pings

2. State Management

   • Externalize session state (Redis/DynamoDB)
   • Use environment variables for config
   • Implement circuit breakers for external service calls

3. Cost Control

   • Set aggressive function timeouts (30s max for API routes)
   • Use provisioned concurrency only for hot paths
   • Monitor invocation patterns and adjust memory allocation

Real-World Example:

I built productivity-cloud - a FastAPI-based task orchestration platform designed for serverless deployment:

• Architecture: FastAPI + Redis state + AWS Lambda/Google Cloud Functions
• Deployment: GitHub Actions → Docker → Cloud Run
• Cost: <$5/month for 10k requests (vs $50+ for always-on VM)

# Dockerfile optimized for serverless
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8080"]

Production Checklist:

✅ Enable CloudWatch/Stackdriver logging
✅ Add OpenTelemetry traces for cold start analysis
✅ Implement API Gateway caching (1-60s TTL)
✅ Use Lambda Layers for shared dependencies
✅ Set up canary deployments (10% traffic rollout)

Happy to share more deployment patterns if anyone's tackling multi-region serverless FastAPI!