DevOps Skill

Comprehensive guide for deploying and managing cloud infrastructure across Cloudflare edge platform, Docker containerization, and Google Cloud Platform.

When to Use This Skill

Use this skill when:

• Deploying serverless applications to Cloudflare Workers

• Containerizing applications with Docker

• Managing Google Cloud infrastructure with gcloud CLI

• Setting up CI/CD pipelines across platforms

• Optimizing cloud infrastructure costs

• Implementing multi-region deployments

• Building edge-first architectures

• Managing container orchestration with Kubernetes

• Configuring cloud storage solutions (R2, Cloud Storage)

• Automating infrastructure with scripts and IaC

Platform Selection Guide

When to Use Cloudflare

Best For:

• Edge-first applications with global distribution

• Ultra-low latency requirements (<50ms)

• Static sites with serverless functions

• Zero egress cost scenarios (R2 storage)

• WebSocket/real-time applications (Durable Objects)

• AI/ML at the edge (Workers AI)

Key Products:

• Workers (serverless functions)

• R2 (object storage, S3-compatible)

• D1 (SQLite database with global replication)

• KV (key-value store)

• Pages (static hosting + functions)

• Durable Objects (stateful compute)

• Browser Rendering (headless browser automation)

Cost Profile: Pay-per-request, generous free tier, zero egress fees

When to Use Docker

Best For:

• Local development consistency

• Microservices architectures

• Multi-language stack applications

• Traditional VPS/VM deployments

• Kubernetes orchestration

• CI/CD build environments

• Database containerization (dev/test)

Key Capabilities:

• Application isolation and portability

• Multi-stage builds for optimization

• Docker Compose for multi-container apps

• Volume management for data persistence

• Network configuration and service discovery

• Cross-platform compatibility (amd64, arm64)

Cost Profile: Infrastructure cost only (compute + storage)

When to Use Google Cloud

Best For:

• Enterprise-scale applications

• Data analytics and ML pipelines (BigQuery, Vertex AI)

• Hybrid/multi-cloud deployments

• Kubernetes at scale (GKE)

• Managed databases (Cloud SQL, Firestore, Spanner)

• Complex IAM and compliance requirements

Key Services:

• Compute Engine (VMs)

• GKE (managed Kubernetes)

• Cloud Run (containerized serverless)

• App Engine (PaaS)

• Cloud Storage (object storage)

• Cloud SQL (managed databases)

Cost Profile: Varied pricing, sustained use discounts, committed use contracts

Quick Start

Cloudflare Workers

Install Wrangler CLI

npm install -g wrangler

Create and deploy Worker

wrangler init my-worker cd my-worker wrangler deploy

See: references/cloudflare-workers-basics.md

Docker Container

Create Dockerfile

cat > Dockerfile <<EOF FROM node:20-alpine WORKDIR /app COPY package*.json ./ RUN npm ci --production COPY . . EXPOSE 3000 CMD ["node", "server.js"] EOF

Build and run

docker build -t myapp . docker run -p 3000:3000 myapp

See: references/docker-basics.md

Google Cloud Deployment

Install and authenticate

curl https://sdk.cloud.google.com | bash gcloud init gcloud auth login

Deploy to Cloud Run

gcloud run deploy my-service
--image gcr.io/project/image
--region us-central1

See: references/gcloud-platform.md

Reference Navigation

Cloudflare Platform

• cloudflare-platform.md

• Edge computing overview, key components

• cloudflare-workers-basics.md

• Getting started, handler types, basic patterns

• cloudflare-workers-advanced.md

• Advanced patterns, performance, optimization

• cloudflare-workers-apis.md

• Runtime APIs, bindings, integrations

• cloudflare-r2-storage.md

• R2 object storage, S3 compatibility, best practices

• cloudflare-d1-kv.md

• D1 SQLite database, KV store, use cases

• browser-rendering.md

• Puppeteer/Playwright automation on Cloudflare

Docker Containerization

• docker-basics.md

• Core concepts, Dockerfile, images, containers

• docker-compose.md

• Multi-container apps, networking, volumes

Google Cloud Platform

• gcloud-platform.md

• GCP overview, gcloud CLI, authentication

• gcloud-services.md

• Compute Engine, GKE, Cloud Run, App Engine

Python Utilities

• scripts/cloudflare-deploy.py

• Automate Cloudflare Worker deployments

• scripts/docker-optimize.py

• Analyze and optimize Dockerfiles

Common Workflows

Edge + Container Hybrid

Cloudflare Workers (API Gateway)

-> Docker containers on Cloud Run (Backend Services)

-> R2 (Object Storage)

Benefits:

- Edge caching and routing

- Containerized business logic

- Global distribution

Multi-Stage Docker Build

Build stage

FROM node:20-alpine AS build WORKDIR /app COPY package*.json ./ RUN npm ci COPY . . RUN npm run build

Production stage

FROM node:20-alpine WORKDIR /app COPY --from=build /app/dist ./dist COPY --from=build /app/node_modules ./node_modules USER node CMD ["node", "dist/server.js"]

CI/CD Pipeline Pattern

1. Build: Docker multi-stage build

2. Test: Run tests in container

3. Push: Push to registry (GCR, Docker Hub)

4. Deploy: Deploy to Cloudflare Workers / Cloud Run

5. Verify: Health checks and smoke tests

Best Practices

Security

• Run containers as non-root user

• Use service account impersonation (GCP)

• Store secrets in environment variables, not code

• Scan images for vulnerabilities (Docker Scout)

• Use API tokens with minimal permissions

Performance

• Multi-stage Docker builds to reduce image size

• Edge caching with Cloudflare KV

• Use R2 for zero egress cost storage

• Implement health checks for containers

• Set appropriate timeouts and resource limits

Cost Optimization

• Use Cloudflare R2 instead of S3 for large egress

• Implement caching strategies (edge + KV)

• Right-size container resources

• Use sustained use discounts (GCP)

• Monitor usage with cloud provider dashboards

Development

• Use Docker Compose for local development

• Wrangler dev for local Worker testing

• Named gcloud configurations for multi-environment

• Version control infrastructure code

• Implement automated testing in CI/CD

Decision Matrix

Need Choose

Sub-50ms latency globally Cloudflare Workers

Large file storage (zero egress) Cloudflare R2

SQL database (global reads) Cloudflare D1

Containerized workloads Docker + Cloud Run/GKE

Enterprise Kubernetes GKE

Managed relational DB Cloud SQL

Static site + API Cloudflare Pages

WebSocket/real-time Cloudflare Durable Objects

ML/AI pipelines GCP Vertex AI

Browser automation Cloudflare Browser Rendering

Resources

• Cloudflare Docs: https://developers.cloudflare.com

• Docker Docs: https://docs.docker.com

• GCP Docs: https://cloud.google.com/docs

• Wrangler CLI: https://developers.cloudflare.com/workers/wrangler/

• gcloud CLI: https://cloud.google.com/sdk/gcloud

Implementation Checklist

Cloudflare Workers

• Install Wrangler CLI

• Create Worker project

• Configure wrangler.toml (bindings, routes)

• Test locally with wrangler dev

• Deploy with wrangler deploy

Docker

• Write Dockerfile with multi-stage builds

• Create .dockerignore file

• Test build locally

• Push to registry

• Deploy to target platform

Google Cloud

• Install gcloud CLI

• Authenticate with service account

• Create project and enable APIs

• Configure IAM permissions

• Deploy and monitor resources