Amazon spent $1.8 million on a failed AI project, and didn't notice the overrun for five months

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In a nutshell: Amazon's growing use of AI across its operations is starting to expose a practical problem: the technology can become expensive quickly, and in some cases, no one notices until the bill is already high. Internal discussions reviewed by the Financial Times show that several AI-driven projects at the company have run over budget, sometimes by a wide margin.

In one case, Amazon spent $1.8 million on a project that used Anthropic's Claude Sonnet model to match author information with product listings. The system ultimately failed, and spending exceeded the original budget by 860%. The issue went undetected for five months.

Engineers say the problem isn't limited to one project. As more teams shift from traditional software to AI models, routine mistakes are becoming far more costly. Tasks that once required minimal computing resources now depend on systems that charge based on usage, often measured in tokens. When those systems are misconfigured or left unchecked, costs can climb quickly.