DevOps & Infrastructure Recipes

Your Dockerfile balloons the image to 1.2GB, the Kubernetes rollout is stuck on CrashLoopBackOff, and the pipeline that was green yesterday now fails on a step nobody touched. DevOps work is where small misconfigurations turn into production incidents — and where an AI assistant that can read your manifests, logs, and pipeline files pays for itself fastest.

This section provides battle-tested recipes for containerization, orchestration, CI/CD pipelines, monitoring, and infrastructure as code, with side-by-side workflows for Cursor, Claude Code, and Codex.

What’s in DevOps Recipes?

Our DevOps cookbook covers the entire deployment lifecycle, from local development to production infrastructure:

Docker & Containerization

• Multi-stage builds
• Container optimization
• Docker Compose workflows
• Security best practices

Kubernetes Orchestration

• Manifest generation
• Helm chart creation
• Service mesh configuration
• Auto-scaling patterns

CI/CD Pipelines

• GitHub Actions workflows
• GitLab CI configuration
• Jenkins pipelines
• Automated testing & deployment

Infrastructure as Code

• Terraform modules
• Ansible playbooks
• CloudFormation templates
• GitOps workflows

Featured Recipes

Container Excellence

Docker Patterns Build optimized containers with multi-stage builds, caching strategies, and security scanning

Kubernetes Mastery

Kubernetes Patterns Deploy and manage applications at scale with declarative configurations and GitOps

Continuous Delivery

CI/CD Pipelines Automate your build, test, and deployment workflows across multiple platforms

Observability

Monitoring & Logging Implement comprehensive monitoring, logging, and alerting for production systems

Infrastructure Automation

Infrastructure as Code Manage cloud resources declaratively with version control and automated provisioning

Why AI-Powered DevOps?

AI assistance revolutionizes DevOps practices by:

• Generating complex configurations - From Kubernetes manifests to Terraform modules
• Debugging deployment issues - Analyze logs and suggest fixes in real-time
• Optimizing performance - Identify bottlenecks and recommend improvements
• Ensuring security - Scan for vulnerabilities and apply best practices
• Documenting infrastructure - Keep your runbooks and diagrams up-to-date

Quick Start Examples

1. Containerize an application

   # With Cursor (Agent mode)
   Agent: "Create an optimized Docker setup for this Node.js app with a multi-stage build"
   
   # With Claude Code
   claude "Generate a Dockerfile with security scanning and a minimal attack surface"
   
   # With Codex
   codex "Generate a Dockerfile with a multi-stage build and minimal attack surface"

2. Deploy to Kubernetes

   # With Codex, headless, as a reviewable diff
   codex exec "Create a Kubernetes deployment with service, ingress, and HPA for this app"

3. Set up CI/CD

   # With Cursor (Agent mode)
   Agent: "Set up a CI/CD pipeline with testing, building, and deployment to staging/production"

Best Practices

When using AI for DevOps:

1. Review generated configurations - AI provides excellent starting points but always validate
2. Test in staging first - Never deploy AI-generated configs directly to production
3. Use version control - Track all infrastructure changes in Git
4. Implement security scanning - Integrate vulnerability scanning in your pipelines
5. Document everything - Use AI to maintain up-to-date documentation

Integration with Tools

Our recipes integrate with popular DevOps tools:

• Container Registries: Docker Hub, ECR, GCR, ACR
• Orchestrators: Kubernetes, Docker Swarm, ECS
• CI/CD Platforms: GitHub Actions, GitLab CI, Jenkins, CircleCI
• Cloud Providers: AWS, Azure, GCP, DigitalOcean
• Monitoring: Prometheus, Grafana, DataDog, New Relic
• IaC Tools: Terraform, Ansible, Pulumi, CloudFormation

Ready to transform your DevOps workflows? Start with any recipe above or explore our complete collection!