Cutting our inference bill by 68% by being obsessive about optimization
Curated from Cloudflare Blog
Most teams treat LLM inference costs as a fixed overhead, accepting rising bills as the price of adoption. Cloudflare’s internal engineering team challenges this assumption by dissecting a specific failure: a doubling cost per run in their AI code reviewer. Rather than simply scaling infrastructure, they focused on granular operational controls. The article details how they combined aggressive prompt caching with intelligent model routing to reduce expenses by nearly seventy percent. This is a pragmatic look at how to optimize for both latency and unit economics simultaneously. It moves beyond generic "green AI" platitudes to show the actual architectural changes required to make large-scale LLM deployment financially sustainable. For practitioners currently managing high-volume AI workloads, the key takeaway is that auditing your token flow and implementing tiered model selection can yield immediate, significant cost reductions without sacrificing review quality or speed.
When our AI code reviewer's cost per run doubled, we dug into the token usage to find out why. Learn how we used prompt caching and intelligent model routing to slash our inference bill by 68% while speeding up reviews.
— Cloudflare Blog