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 componentscloudflare-workers-basics.md- Getting started, handler types, basic patternscloudflare-workers-advanced.md- Advanced patterns, performance, optimizationcloudflare-workers-apis.md- Runtime APIs, bindings, integrationscloudflare-r2-storage.md- R2 object storage, S3 compatibility, best practicescloudflare-d1-kv.md- D1 SQLite database, KV store, use casesbrowser-rendering.md- Puppeteer/Playwright automation on Cloudflare
Docker Containerization
docker-basics.md- Core concepts, Dockerfile, images, containersdocker-compose.md- Multi-container apps, networking, volumes
Google Cloud Platform
gcloud-platform.md- GCP overview, gcloud CLI, authenticationgcloud-services.md- Compute Engine, GKE, Cloud Run, App Engine
Python Utilities
scripts/cloudflare-deploy.py- Automate Cloudflare Worker deploymentsscripts/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