Cutting our inference bill by 68% by being obsessive about optimization

When we previously blogged about our AI code reviewer in April, an average review cost a palatable $1.19. By the end of August, that figure had shot up to about $2.46. We hadn't made any significant architectural changes: the coordinator was still handing work to the same set of specialist reviewers, and the same risk tiers decided how intensive of a review we gave.

Around mid-September we tasked our agents with working out where those tokens were going and cutting them — aggressively. With code quality at high effort, the average review now costs just $0.77, down 68% from the $2.43 it averaged over our two most expensive weeks before we started. Our main goal was to cut costs, but as part of this work we also managed to optimize the reviewer, so that the median review now takes just 1m 32s instead of the 4m 09s it took before.

About 40% of the savings came from changes and optimizations to the reviewer's code, and the rest from experimenting with which models we use and how much effort they put in.

How a review doubled in cost

The chart below shows the cost of an Continue reading

Orchestrating AI Code Review at scale

Code review is a fantastic mechanism for catching bugs and sharing knowledge, but it is also one of the most reliable ways to bottleneck an engineering team. A merge request sits in a queue, a reviewer eventually context-switches to read the diff, they leave a handful of nitpicks about variable naming, the author responds, and the cycle repeats. Across our internal projects, the median wait time for a first review was often measured in hours.

When we first started experimenting with AI code review, we took the path that most other people probably take: we tried out a few different AI code review tools and found that a lot of these tools worked pretty well, and a lot of them even offered a good amount of customisation and configurability! Unfortunately, though, the one recurring theme that kept coming up was that they just didn’t offer enough flexibility and customisation for an organisation the size of Cloudflare.

So, we jumped to the next most obvious path, which was to grab a git diff, shove it into a half-baked prompt, and ask a large language model to find bugs. The results were exactly as noisy as you might expect, with a flood Continue reading