Break cycle time into stages and find the real bottleneck

A single cycle time figure tells you delivery is slow, but not why. Two teams can share the same 40-hour cycle time for completely different reasons: one is drowning in review wait, the other is blocked by flaky deploys. Treating cycle time as one blob leads to guesswork and the wrong fixes.

Cycle time analytics splits every change into three measurable stages: coding (first commit to PR open), review (PR open to approval), and deploy (merge to production). Each stage has its own owner, its own failure modes, and its own lever. When you can see the split, the conversation shifts from "we are slow" to "review pickup is our constraint this quarter."

This is the same philosophy behind our engineering metrics approach and our DORA metrics tracking: measure the pipeline as stages, not as a mystery total.

The point of the breakdown is action. Once analytics show that review consumes 60% of your cycle time, you know where to spend effort: reviewer assignment, smaller PRs, and clearer code review signals beat any generic "work faster" push. If deploy dominates, the answer lives in CI and release automation instead.

We surface bottlenecks by team, service and PR size, and track the p75/p90 tail so a handful of week-long PRs cannot hide behind a healthy median. You can set stage-level targets and get alerted when review or deploy time drifts, turning a static chart into an operating metric.

Because we segment by AI-generated code, you can also verify whether coding agents actually shorten the path to production or just relocate the bottleneck. Explore the full loop on our measure product or estimate your own numbers with the cycle time calculator.