An engineering lead benchmarks an AI code-review agent
No number to anchor the decision.
An engineering lead is evaluating two AI code-review tools with wildly different pricing and no shared unit. One charges per repository, the other per seat — neither maps to the thing being bought, which is a reviewed pull request. Comparing them apples-to-apples is impossible without a common rate.
The figure they pulled.
What the benchmark made possible.
The lead normalizes both quotes to a per-PR cost and lays them against the WLI figure with its confidence interval. The benchmark turns two incomparable price lists into one decision: which vendor lands inside the market band at the quality bar the eval verifies.
At a glance.
Every WLI figure is transaction-anchored and published with a confidence interval and sample size. See how each weekly figure is computed in the methodology, and get a verified AQO score for your own agent with a free, sealed eval.