| name | devops |
| description | Deploy and manage cloud infrastructure on Cloudflare (Workers, R2, D1, KV, Pages, Durable Objects, Browser Rendering), Docker containers, and Google Cloud Platform (Compute Engine, GKE, Cloud Run, App Engine, Cloud Storage). Use when deploying serverless functions to the edge, configuring edge computing solutions, managing Docker containers and images, setting up CI/CD pipelines, optimizing cloud infrastructure costs, implementing global caching strategies, working with cloud databases, or building cloud-native applications. |
| license | MIT |
| version | 1.0.0 |
DevOps Skill
Comprehensive guide for deploying and managing cloud infrastructure across Cloudflare edge platform, Docker containerization, and Google Cloud Platform.
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 <
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
Never commit secrets, API keys, or credentials to version control
Always run containers as non-root user in production
Scan container images for vulnerabilities before deployment
Implement health checks for all containerized services
Use multi-stage Docker builds to minimize image size
Set resource limits (CPU, memory) for all containers
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
Implementation Checklist
Cloudflare Workers
Docker
Google Cloud