Deploying an MVP with a Fixed Cost of $10/Month Instead of Vercel

If you ask an AI agent for a tech stack, nine times out of ten it will recommend Vercel or Supabase. While they are convenient to set up immediately, your bill can skyrocket to unmanageable levels as soon as your user base grows even slightly. For a solo founder, unpredictable variable costs are poison. You must enforce a stack that fits your wallet rather than following the "easy path" suggested by AI.

How to Keep Fixed Monthly Costs Under $15

Usage-based billing suggested by many AIs attaches a high margin in exchange for convenience. For instance, Vercel charges a $20 "seat tax" per user and has expensive bandwidth costs. On the other hand, using a VPS (Virtual Private Server) like Hetzner changes the story. Even the CX23 model, which costs about €4.08 per month, can easily handle tens of thousands of users.

The method is simple. First, buy a VPS. Then, instruct the AI as follows: "Write a script to install Coolify, an open-source PaaS, on a Hetzner VPS and deploy Next.js based on Docker." This allows you to maintain the convenience of Vercel while locking in costs at the price of a few cups of coffee. You must prevent the foolish situation where you hesitate to market your service because you're afraid of infrastructure operating costs.

Forcing Specific DB Engines Based on Data Characteristics

AI often recommends NoSQL because of its flexible schema design. However, using NoSQL for payment or inventory management systems where relationships between data are crucial is a disaster. You'll end up spending more time on maintenance, trying to fix data integrity issues through code. A wrong DB choice eventually leads to a multi-thousand dollar accident where you have to rewrite the entire service.

Before choosing a DB, ask the AI these three things:

• Does it require complex operations involving users, orders, and payments?
• Is even a single data error critical to the business?
• Do I need to filter data using various conditions?

If any of these apply, command the AI to "Use PostgreSQL and write a SQL DDL that specifies foreign key constraints." Don't just blindly follow what the AI says; you must specify the engine yourself to prevent the data from becoming a mess.

Validating Tech Debt Through Job Market Share

When your service grows and you need to hire people, recruitment costs will soar if you've built it with a stack no one has ever heard of. Looking at the 2025 Stack Overflow Developer Survey, JavaScript and PostgreSQL remain the most trusted technologies. No matter how trendy a technology recommended by AI might be, discard it if it hasn't been proven in the job market.

First, check the number of job postings for that technology on sites like Wanted or Jumpit. If the postings are less than 20% compared to React, that technology is risky. Then, have the AI investigate the trend of GitHub stars and the number of unresolved security issues for that framework. Choosing a technology with a dying community is like boarding a sinking ship.

Ensuring Portability to Prevent Vendor Lock-in

Once you start using cloud-specific SDKs, you'll have to rewrite all your code if you want to move elsewhere later. To remain infrastructure-independent, all code should be container-based.

Instruct the AI: "Write a Dockerfile applying multi-stage builds and manage environment variables with .env." Also, use an ORM like Prisma or Drizzle to separate layers so that switching DB engines is easy. Finally, create a script that "dumps the DB every day at 3 AM, uploads it to external storage, and deletes files older than 30 days," and register it on the server. It's only truly your service if you can revive it on another server within an hour if a platform issue occurs.