AI lowered the build barrier, not the risk

I currently deploy everything I build in containers, and I advise everyone to do so as well. It will take extra work and time to learn the specifics of docker and the infrastructure to run them. The good news is AI exists and can walk you through it and even create personal courses to teach you how.

I also understand that if you are a recent convert to building with AI, deploying on managed rails (pre-packaged or managed infrastructure) may make sense. You get to start up your project faster, there is less to run, and the platform you choose reduces the work you must do. The problem is the risk is still there, and you are hoping your AI model and the deployment platform cover you.

If you are going to stay on managed rails, here are four things I would do so they do not catch you off guard.

1. Avoid background processes that loop unless you truly understand the risk. AI is notoriously bad at understanding the varying states of data your program may run into, and by default will not add stopping conditions. You can end up with a large surprise bill.
2. Secure your AI API keys by IP, and have guards in place to stop runaway usage. API keys are great, but when compromised they can result in enormous bills, so you must have confidence in the steps you have taken to secure the token.
3. Know what triggers an upgrade to a higher tier of compute, database, or other service, and what it will cost, before it surprises you.
4. Understand the counters that add cost outside your base plan, like storage and network traffic.

If there are risks I did not cover, add them in the comments to help others.

When a provider raises prices or I outgrow the platform, moving is a deployment, with some migration and setup. Platform and network security and setup have to happen no matter where you run. The freedom to move is the whole reason I take on the extra work, and I encourage you to do the same. Since this post is already long, I wrote a companion article about what I do.