We tracked 200K AI requests. Here's where the money actually goes

May 16, 2026 · 7:32 PM UTC ·2 min read · 0 reactions · 0 comments · 33 views

#ai #productivity #devtools #openai #cost optimization #CostLens #OpenAI #Joao Romao #Claude Code #Kiro #Cursor

TL;DR · WeSearch summary

CostLens, a tool initially designed to track OpenAI spending, has evolved into a platform that attributes AI costs by feature and developer, routes requests to cost-efficient models, and measures productivity through coding activity. After analyzing 200,000 AI requests, the team found significant overspending on simple tasks and reduced costs by 30-40% using smart model routing. The tool integrates easily with existing workflows and includes a Slack-based kill switch to prevent runaway AI expenses.

Key facts

▪CostLens now provides cost attribution by feature and developer, not just overall spending.
▪Smart routing redirects simple prompts to cheaper models, achieving 30-40% cost savings.
▪52% of AI requests analyzed were simple tasks running on expensive models.
▪Sessions with no output accounted for 15% of AI spending.
▪The platform includes a Slack kill switch to pause high-spend agent sessions.

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Original publisher	DEV.to (Top)
Canonical URL	https://dev.to/jrmromao/we-tracked-200k-ai-requests-heres-where-the-money-actually-goes-495e
Publication time	Sat, 16 May 2026 19:32:47 +0000
Retrieval time	2026-05-16T19:40:19.037Z
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Opening excerpt (first ~120 words) tap to expand

try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 303292) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Joao Romao Posted on May 16 We tracked 200K AI requests. Here's where the money actually goes #ai #productivity #devtools #openai Six months ago I posted here about CostLens — a tool to reduce OpenAI costs. Since then, we've completely rebuilt it based on one question a VP asked our team: "How much faster are we delivering with AI? What's the number?" Nobody could answer. We had cost dashboards, but no way to connect spend to output. So we built that.

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