42% Failure Rate and No One Complained — How My Last Scraper Was Silently Dying

My 13th Apify actor had a 42% failure rate. I found out three weeks after deploying it. No user complained. No alert fired. The runs just... failed. Quietly. Here's what happened, why no one told me, and what I changed. The Context I built and deployed 13 Korean data scrapers to the Apify Store over about two weeks. The last one — musinsa-ranking-scraper — went live on March 9th. It scrapes Musinsa, Korea's largest fashion marketplace, for brand rankings and product data. Pay-per-event pricing went live on March 25th. By then, the actor had already accumulated 40 runs in 30 days. Of those 40 runs: 23 succeeded, 17 failed. That's a 42.5% failure rate. Why No One Told Me Three reasons: 1. The failures looked like user errors, not my bug. When a run fails, Apify shows the exit code and log. Users see this, shrug, and try again — or switch to a competitor. They don't file a GitHub issue. 2. The failure mode was silent. The actor didn't crash with a clear error. It started, attempted to ini