I thought our cloud cleanup worker was working.
It wasn't.
A small Python "TypeError" caused one of our ephemeral DigitalOcean droplets to survive for 3 days instead of ~45 minutes.
The weird part? Nothing crashed.
The cleanup loop was still running. It just silently treated the failed timestamp calculation as "0" seconds of age — so the droplet looked perfectly healthy.
And that wasn't even the only bug.
We eventually found three separate failure modes:
→ A "datetime"/"str" mismatch hidden by broad exception handling
→ Cloud resources being deleted from our tracking state even when destruction failed
→ In-memory state being used as the source of truth, meaning a restart could orphan resources
The bill?
About $5.
Not catastrophic. But the failure mode could have been.
The biggest lesson wasn't "be careful with Python types."
It was:
Never let your cleanup system silently fail closed when the thing you're cleaning up costs money.
We wrote up the entire incident, including the failure chain, what we changed, and the architecture lessons:
How a TypeError Costed us Cloud Credits
If you're running ephemeral infrastructure, background workers, or anything that creates cloud resources dynamically, this is the kind of bug worth finding before your credits do.
Top comments (1)
Happy to see a real production debugging story in the midst of all the Agentic AI hype 🤖
A simple Python
TypeError, caused by an unexpected return type, turned into a good reminder of how important it is to understand the behavior and return values of the tools and APIs we rely on 🔍These are the kinds of practical engineering lessons that are easy to overlook 🛠️