Amazon has found that artificial intelligence can turn a trivial mistake into a seven-figure one. The company’s own internal metrics show projects running wildly over budget, including one that burned $1.8m before anyone noticed. Senior engineers called the results “catastrophically expensive.”
The cases were laid out at an internal meeting and first reported by the Financial Times. The worst one used Anthropic’s Claude to match author records against product listings on Amazon’s store. It ran 860 per cent over budget, took five months to spot, and the deployment failed anyway.
Two smaller blunders sat alongside it, Tom’s Hardware noted. A financial auditing tool overshot by about $541,000. A system meant to speed up deliveries added $134,000. Each was the kind of error that used to cost almost nothing.
How a cheap mistake becomes a huge bill #
The reason sits on the price list. When a person writes buggy code, it throws an error and crashes. When a model does the work, a bad configuration just keeps running and quietly bills you. A retry loop that re-sends the same prompt, or a job pointed at the whole catalogue instead of a sample, produces no crash. It produces an invoice.
That invoice arrives on a monthly cycle, not in a build log. The gap is how a misconfigured job runs for five months before anyone connects it to a number. The shift to per-token pricing makes it worse. AI agents, which fire off far more tokens than a chatbot, pour fuel on it.
None of this is unique to Amazon. It is the same trap that produced a $1.3m OpenAI bill for one developer, and the reason enterprise AI bills keep climbing even as the price per token falls.
The company that sells the fix #
Here is the awkward part. Amazon overspent on AI while its own cloud sells the exact tools to stop it. AWS’s Bedrock offers batch inference at half price, a discounted “Flex” tier, prompt caching at a tenth of the input rate, and prompt routing that pushes simple jobs to cheaper models.
Cheaper models were an option too. Anthropic’s Haiku costs a third of the Sonnet model Amazon reached for. The overspend came from picking the frontier model by default and leaving the guardrails off, exactly the mistake Amazon and Anthropic tell other companies to avoid.
Amazon’s answer #
Amazon played it down. “As with any new technology, we’re experimenting, learning and improving how we use it,” it said, adding that a few isolated examples do not reflect how its teams work. The cases involved a small number of groups inside a corporate workforce of roughly 300,000.
The maths backs the shrug. Amazon’s quarterly revenue sits above $180bn, so the blown budgets do not add up to 0.1 per cent of a single month’s sales. The AWS cost expert Corey Quinn was unsympathetic, posting that “getting a bill with two commas that you weren’t expecting must be so hard for you.”
Behind the scenes, the response is firmer. Amazon has already scrapped an internal leaderboard that ranked staff by AI usage, after employees gamed it by inflating their token consumption. Its engineers are now building automated guardrails to cap what a project can spend before the invoice lands.
That is the real lesson, and it is a governance one, not a financial one. For Amazon, $1.8m is a rounding error. For the thousands of firms now leaning on AI to cut costs, a runaway job that hides for five months is the difference between a budget and a crisis.
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