Cloud Deployment

Why "it works on my machine" is the beginning of the story, not the end

Getting code to run in production is a distinct skill from writing code. Environment parity, secret management, networking configuration, SSL termination, load balancing, auto-scaling — none of this is visible in a local development environment. Each one is an opportunity for something to fail in production that worked perfectly in development.

Cloud providers are powerful but complex. AWS alone has hundreds of services, and the right combination for a given workload requires experience with what's available, what's cost-effective, and what will actually hold up. Choosing the wrong architecture at deployment time can cost you 10x what the right one would.

Observability is the other half of deployment. An app is not production-ready until you can answer: is it running, is it healthy, what is it doing, and how will I know if something goes wrong?

How I deploy applications to production

I treat deployment as infrastructure-as-code — documented, reproducible, and version-controlled.

• AWS EC2, ECS, or Lambda selected based on your workload — persistent servers, containers, or serverless
• Oracle Cloud (OCI) for cost-efficient compute with generous free tier for startups watching burn rate
• Vercel or Netlify for frontend and Next.js deployments with zero-config edge network
• Uptime monitoring, error tracking, and log aggregation so you have observability from day one

Every deployment I set up comes with a runbook covering how to deploy, how to roll back, and how to respond to common incidents.