Original Summary

Not a pitch; I'm sharing numbers because I couldn't find any. I used Claude Code and Codex heavily for a month and logged every token: ~373M tokens in September, more than 90% of them cache reads. Using published research for energy per token and water per kWh (data-center cooling + electricity generation), that's an estimated 47.8 litres of water , with an honest range of roughly 5x lower to 5x higher. Takeaways for anyone building on LLM APIs: - Cache reads dominate volume. Their cost per token is the biggest unknown in any footprint estimate. - Water from electricity generation is ~88% of the mid estimate; on-site cooling is the small part. - Model choice matters more than prompt length: per token, a frontier model can cost many times a small one. - No provider publishes per-token energy. If you report AI sustainability numbers to customers, you're estimating too. Disclosure: I'm the author of the free, open-source CLI that does this estimate, so you can check your own usage: https://github.com/beausterling/drip-ai-water-usage Happy to hear where the method looks off.   submitted by   /u/Independent_Ad7172 [link]   [comments]

  • 情报分类：商业与市场研究
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  • 信息来源：Reddit · SaaS
  • 发布时间：2026/10/8 00:07:32