A seemingly simple task ended with a $1.8 million bill inside Amazon. The project used Anthropic’s Claude Sonnet to connect information about authors with book listings on the company’s website.
The system was not successfully deployed and exceeded its original budget by 860%. It took Amazon five months to identify the problem.
The case was presented by senior Amazon engineers during an internal meeting, according to the Financial Times. The message shared with employees was straightforward: mistakes that were once inexpensive can become extremely costly when repeated by artificial intelligence systems.
A small bug can create a very large bill.
The problem appeared in other projects
The Claude project was the most expensive example presented, but it was not the only one. A tool developed for financial audits generated approximately $541,000 in unexpected expenses.
Another system, designed to improve delivery speeds across Amazon’s logistics network, accumulated $134,000 in unplanned costs. In that case, the company took more than two weeks to identify the problem.
The incidents highlight one of the challenges of using AI in internal projects. Even when a task appears limited, costs can grow quickly without mechanisms capable of detecting unusual spending.
Amazon said the cases did not represent how the technology was being used across the entire company. According to the company, its teams continue to experiment, learn and search for more cost-efficient ways to use AI.
Amazon is preparing new controls
After identifying the unexpected spending, engineers began developing automated protections for future projects. The goal is to create limits and alerts capable of stopping abnormal expenses before they remain unnoticed for weeks or months.
The case does not suggest that AI is no longer useful for businesses. It shows that automating a task does not remove the need to monitor the project closely.
At Amazon, a task involving author data ended 860% over budget. The next challenge is not only making AI work, but quickly recognizing when its cost no longer makes sense.
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