# Deploying Applications

Deployment patterns from Kubernetes to serverless and edge functions.

- Repository: https://github.com/ancoleman/ai-design-components (526 stars)
- Category: DevOps
- License: MIT
- Last updated: 2025-12-11
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add ancoleman/ai-design-components --skill deploying-applications -g -a claude-code -y`
Codex: `npx skills add ancoleman/ai-design-components --skill deploying-applications -g -a codex -y`
Cursor: `npx skills add ancoleman/ai-design-components --skill deploying-applications -g -a cursor -y`
Gemini CLI: `npx skills add ancoleman/ai-design-components --skill deploying-applications -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add ancoleman/ai-design-components`

## Description

Deployment patterns from Kubernetes to serverless and edge functions. Use when deploying applications, setting up CI/CD, or managing infrastructure. Covers Kubernetes (Helm, ArgoCD), serverless (Vercel, Lambda), edge (Cloudflare Workers, Deno), IaC (Pulumi, OpenTofu, SST), and GitOps patterns.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Deploying Applications

Production deployment patterns from Kubernetes to serverless and edge functions. Bridges the gap from application assembly to production infrastructure.

## Purpose

This skill provides clear guidance for:
- Selecting the right deployment strategy (Kubernetes, serverless, containers, edge)
- Implementing Infrastructure as Code with Pulumi or OpenTofu
- Setting up GitOps automation with ArgoCD or Flux
- Choosing serverless databases (Neon, Turso, PlanetScale)
- Deploying edge functions (Cloudflare Workers, Deno Deploy)

## When to Use This Skill

Use this skill when:
- Deploying applications to production infrastructure
- Setting up CI/CD pipelines and GitOps workflows
- Choosing between Kubernetes, serverless, or edge deployment
- Implementing Infrastructure as Code (Pulumi, OpenTofu, SST)
- Migrating from manual deployment to automated infrastructure
- Integrating with `assembling-components` for complete deployment flow

## Deployment Strategy Decision Tree

```
WORKLOAD TYPE?

├── COMPLEX MICROSERVICES (10+ services)
│   └─ Kubernetes + ArgoCD/Flux (GitOps)
│       ├─ Helm 4.0 for packaging
│       ├─ Service mesh: Linkerd (5-10% overhead) or Istio (25-35%)
│       └─ See references/kubernetes-patterns.md

├── VARIABLE TRAFFIC / COST-SENSITIVE
│   └─ Serverless
│       ├─ Database: Neon/Turso (scale-to-zero)
│       ├─ Compute: Vercel, AWS Lambda, Cloud Functions
│       ├─ Edge: Cloudflare Workers (<5ms cold start)
│       └─ See references/serverless-dbs.md and references/edge-functions.md

├── CONSISTENT LOAD / PREDICTABLE TRAFFIC
│   └─ Containers (ECS, Cloud Run, Fly.io)
│       ├─ ECS Fargate: AWS-native, serverless containers
│       ├─ Cloud Run: GCP, scale-to-zero containers
│       └─ Fly.io: Global edge, multi-region

├── GLOBAL LOW-LATENCY (<50ms)
│   └─ Edge Functions + Edge Database
│       ├─ Cloudflare Workers + D1 (SQLite)
│       ├─ Deno Deploy + Turso (libSQL)
│       └─ See references/edge-functions.md

The rest: https://github.com/ancoleman/ai-design-components/blob/HEAD/skills/deploying-applications/SKILL.md