Cloud Deployment Vibe Code Cleanup

What AI Gets Wrong in Deployment

AI generates deployment configurations that get the application running. That’s different from deployment that’s production-ready. The Terraform file creates an EC2 instance with a public IP, security group allowing all inbound traffic, and the application running directly on the instance. It works. It’s also insecure, unreliable, and unscalable.

Platform selection from AI is based on popularity, not fit. AI recommends AWS for everything because it has the most training data. A simple web application gets ECS, RDS, ElastiCache, ALB, and Route53 - infrastructure for a Fortune 500 when Railway would have been $20/month. Or the opposite: a high-traffic application gets deployed to a $7 VPS because the AI optimized for simplicity.

Environment isolation doesn’t exist. AI generates one deployment configuration. Staging and production share the same database. Environment variables are hardcoded. There’s no way to test deployments before they hit production because there’s no separate environment to test in.

Our Deployment Cleanup Process

We start with platform fit assessment. Does the current hosting match the workload? We’ve moved applications from over-engineered AWS setups to Railway and saved 90% monthly costs. We’ve moved applications from under-provisioned shared hosting to proper cloud infrastructure when reliability required it. The right answer depends on traffic, complexity, and budget.

Infrastructure-as-code gets rewritten for maintainability. Terraform modules for reusable components. Variables for environment differences. State management with proper locking. Security groups that allow only necessary traffic. The infrastructure becomes reproducible and reviewable instead of a black box.

Deployment gets a proper pipeline. Automated builds triggered by git push. Staging deployment for verification. Production deployment with health checks and rollback. The team ships changes without logging into servers or running scripts from laptops.

Before and After

Before: An application running on an EC2 instance provisioned by AI-generated Terraform. No auto-scaling. No health checks. No staging environment. A security group allowing all traffic. A $400/month AWS bill for an application that serves 100 users.

After: The same application on Railway for $20/month - or on properly configured AWS with auto-scaling, health checks, and environment separation if the workload warrants it. Deployments are automated. Rollback is one click. Staging catches problems before production. The infrastructure matches the application’s actual needs.

Ongoing Infrastructure Hygiene

The cleanup is not a one-time event. We set up infrastructure drift detection so Terraform plan runs on a schedule and flags any manual changes made outside of code. Resource tagging conventions are enforced so every cloud resource is attributable to a service, environment, and cost center. Budget alerts notify the team before cloud spend surprises appear on the monthly invoice. For teams on AWS, we configure Trusted Advisor checks and Cost Explorer reports that surface idle resources, underutilized instances, and unattached EBS volumes. The infrastructure stays clean because the tooling makes drift visible immediately.