A team cut their AI inference bill by more than half. Three months later, customer satisfaction was dropping and the cost savings were tied to the quality loss.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Towards Data Science.
Most RAG systems are optimized for answer quality, not cost—and that blind spot gets expensive fast. In this article, I break down a production-ready cost control layer combining semantic caching, query routing, token budgeting, and circuit breaking, achieving an 85% reduction in LLM costs without sacrificing answer quality.
Using mathematical optimization to solve a pickup-and-delivery problem with time windows. The post “Los Movimientos”: The Routing Problem That Nearly Broke My Spirit appeared first on Towards Data Science .
An AI agent passed every metric in the eval harness I published, then the CFO killed it — its successful resolutions cost more than the humans it replaced. The one metric that predicts whether an agent survives production, and how to measure it without a rebuild.
You "vibe coded" the import. Understand Adam's optimization dynamics, why it fails spectacularly, and how to fix it.
The article titled "Your AI Bill Is a Toll Booth. Stop Paying Twice." discusses how users are unexpectedly paying more for AI services than anticipated, likening the experience to a toll booth where one pays twice. It highlights the unseen costs that can arise when using AI tools and urges readers to be vigilant about their expenses. The piece was first published on Towards Data Science.
arXiv:2607. 25068v1 Announce Type: new Abstract: Routing decisions between a cheap heuristic and an expensive large language model (LLM) are typically framed as a difficulty problem: send the hard cases to the expensive path.