paas

What I do

• Deploy applications to PaaS platforms
• Configure managed services and databases
• Implement auto-scaling and high availability
• Set up CI/CD for PaaS deployments
• Manage application performance
• Optimize PaaS costs

When to use me

• When focusing on code, not infrastructure
• When building web applications quickly
• When needing managed databases
• When implementing auto-scaling
• When reducing operational overhead
• When building cloud-native applications

Key Concepts

Heroku Deployment

# app.json - Heroku Review Apps
{
  "name": "myapp",
  "buildpacks": [
    {
      "url": "heroku/python"
    }
  ],
  "environments": {
    "review": {
      "addons": ["heroku-postgresql:hobby-dev"],
      "environment": {
        "LOG_LEVEL": "DEBUG"
      },
      "formation": {
        "web": {
          "quantity": 1,
          "size": "hobby"
        }
      }
    },
    "production": {
      "addons": ["heroku-postgresql:standard-0"],
      "formation": {
        "web": {
          "quantity": 2,
          "size": "performance-m"
        }
      }
    }
  }
}

AWS Elastic Beanstalk

# .ebextensions/python.config
option_settings:
  aws:elasticbeanstalk:container:python:
    WSGIPath: application:application
  aws:elasticbeanstalk:environment:proxy:staticfiles:
    /static: static
  aws:autoscaling:asg:
    MinSize: 2
    MaxSize: 10
  aws:autoscaling:trigger:
    MeasureName: CPUUtilization
    UpperThreshold: 80
    LowerThreshold: 20

Azure App Service

# azure-pipelines.yml
trigger: main

pool:
  vmImage: 'ubuntu-latest'

variables:
  azureSubscription: 'Azure-Service-Connection'
  appName: 'myapp'
  runtime: 'python'
  version: '3.11'

stages:
  - stage: Build
    jobs:
      - job: BuildJob
        steps:
          - task: UsePythonVersion@0
          - script: |
              pip install -r requirements.txt
              pip install pytest
              pytest tests/
          - task: ArchiveFiles@2
            inputs:
              rootFolder: '$(System.DefaultWorkingDirectory)'
              archiveFile: '$(Build.ArtifactStagingDirectory)/$(Build.BuildId).zip'
          - publish: $(Build.ArtifactStagingDirectory)/$(Build.BuildId).zip
            artifact: drop

  - stage: Deploy
    jobs:
      - deployment: DeployJob
        environment: 'production'
        strategy:
          runOnce:
            deploy:
              steps:
                - task: AzureWebApp@1
                  inputs:
                    azureSubscription: $(azureSubscription)
                    appType: 'webApp'
                    appName: $(appName)
                    package: '$(Pipeline.Workspace)/drop/**/*.zip'

GCP App Engine

# app.yaml
runtime: python311
env: standard

instance_class: F2
automatic_scaling:
  min_instances: 1
  max_instances: 10
  target_cpu_utilization: 0.65
  min_latency: '500ms'

inbound_services:
  - warmup

liveness_check:
  path: /_ah/health
  check_interval_seconds: 30
  timeout_seconds: 4
  failure_threshold: 2
  success_threshold: 2

readiness_check:
  path: /_ah/ready
  check_interval_seconds: 5
  timeout_seconds: 4
  failure_threshold: 2
  success_threshold: 1

env_variables:
  LOG_LEVEL: 'INFO'
  DATABASE_URL: 'postgres://...'

beta_settings:
  cloud_sql_instances: 'project:region:instance'

Cloud Run Deployment

# cloudrun.yaml
apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: myapp
  annotations:
    run.googleapis.com/launch-stage: BETA
spec:
  template:
    metadata:
      annotations:
        autoscaling.knative.dev/minScale: "1"
        autoscaling.knative.dev/maxScale: "10"
    spec:
      containers:
        - image: gcr.io/project/myapp:latest
          ports:
            - containerPort: 8080
          resources:
            limits:
              cpu: "1000m"
              memory: "512Mi"
          env:
            - name: PORT
              value: "8080"
          startupProbe:
            httpGet:
              path: /_ah/health
            initialDelaySeconds: 0
            periodSeconds: 10
            timeoutSeconds: 1
            failureThreshold: 3

PaaS Comparison

Platform	Languages	Database	Scaling	Best For
Heroku	All	Add-ons	Auto	Quick deployment
AWS EB	All	RDS, ElastiCache	Auto	AWS integration
Azure App Service	.NET, Node, Python	SQL, Cosmos	Auto	Enterprise .NET
Cloud Run	All	Cloud SQL	Auto	Containers
Vercel	Node, Go, Python	External	Auto	Frontend/JAMstack

Key Benefits

• Managed Runtime: Don't manage OS or middleware
• Auto-scaling: Handle traffic spikes automatically
• Managed Services: Databases, caches, queues
• CI/CD: Built-in deployment pipelines
• Monitoring: Integrated logging and metrics
• Security: Patched and secured platforms