Track DORA metrics automatically from GitHub

Most teams that "track DORA metrics" are really maintaining a spreadsheet that goes stale within a sprint. FeatureFactory takes a different approach: it reads the events already flowing through GitHub - pull requests, commits, deployment events, release tags, and workflow runs - and computes deploy frequency, lead time for changes, change failure rate, and MTTR continuously.

Connect a repository once and we backfill from your history so you start with a trend, not an empty chart. From then on, every merged PR and every production deploy updates your numbers in place. If you want the underlying definitions before you wire anything up, our what are DORA metrics guide explains each one in plain terms.

Because the data comes from the same source your engineers already work in, there is nothing extra to remember and no separate ritual to keep alive. The metrics stay honest because they are measured, not reported.

DORA is deliberately balanced: two throughput metrics and two stability metrics. Optimizing one pair while ignoring the other is how teams end up shipping fast and breaking things - or shipping safely and shipping nothing. FeatureFactory shows all four side by side so the trade-offs stay visible.

Lead time for changes is often the most actionable of the four, because it exposes where work waits - in review, in CI, or in a release queue. Pair it with our cycle time analytics to see the same journey broken into stages, and use PR quality analytics to understand whether speed is costing you rework.

DORA metrics are also the natural anchor for measuring AI-assisted development. As agents and copilots write more of your diff, deploy frequency and change failure rate tell you whether that output is actually reaching production safely. See how FeatureFactory measures what you ship to close that loop.