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FeatureFactory

Engineering Benchmarks

Benchmark your team against baselines you can actually trust

Compare cycle time, deploy frequency, and stability against your own trailing history and transparent industry bands - measured from GitHub, built to resist gaming.

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Transparent baselines

Every benchmark shows exactly which signals feed it and how the band was drawn, so you can trust the comparison instead of squinting at a black box.

Compare against yourself first

Your own trailing trend is the primary baseline - external bands are context, not a leaderboard you are pressured to climb.

Gaming-resistant by design

Benchmarks are built from balanced throughput and stability metrics, so juicing one number visibly drags another and the trade-off stays honest.

Team and repo segmentation

Slice benchmarks by team, service, or repository so a monolith and a greenfield project are never judged against the same bar.

Percentile bands, not single points

See where you sit across a distribution rather than chasing one average that hides your slow tail of stuck pull requests.

Straight from GitHub

Benchmarks derive from the pull requests, commits, and deploys already in your history - connect a repo once and start with a real baseline.

Benchmarks that give you a baseline, not a leaderboard

Most engineering benchmarks fail in one of two ways: they compare you to an anonymous industry average you cannot inspect, or they hand managers a single number that is trivially easy to game. FeatureFactory takes the opposite stance - a benchmark should be a transparent baseline you can interrogate, not a leaderboard that quietly changes behavior for the worse.

The first and most important baseline is your own trailing history. Comparing this quarter to last quarter, on the same repositories with the same team shape, tells you far more than landing in an external band on a given week. External ranges like the DORA Elite/High/Medium/Low bands add context, but we treat them as reference lines, not report cards.

Because every benchmark exposes the signals underneath it - the pull requests, commits, reviews, and deploys - you can always trace a number back to the work that produced it. That traceability is what makes the comparison worth trusting. Start from the raw signals in engineering metrics and layer benchmarks on top.

Why balanced metrics resist gaming

The moment a single metric becomes a target, someone optimizes the metric instead of the outcome. Ask a team to cut cycle time and you might get a flood of tiny, under-reviewed pull requests. Ask for more deploys and you might get riskier releases. The fix is not surveillance - it is measuring pairs that trade off against each other.

FeatureFactory benchmarks throughput and stability together, so improving one at the expense of the other is immediately visible. A faster cycle time that comes with a climbing change failure rate is not progress, and the benchmark makes that plain. Pair this with PR quality analytics to confirm that speed is not quietly buying you rework.

This balance matters even more as agents and copilots write more of your diff. Benchmarking AI-assisted delivery only works if the numbers stay honest under pressure. See how FeatureFactory measures what you ship to keep throughput, quality, and stability on one loop.

Related tools & solutions

Engineering metricsDORA metrics trackingCycle time analyticsPR quality analyticsWhat are DORA metrics?Measure

Frequently asked questions

Engineering benchmarks are reference ranges that let you compare a team's delivery signals - like cycle time, deploy frequency, review latency, and change failure rate - against a baseline. The most useful baseline is your own trailing history; industry bands such as the DORA Elite/High/Medium/Low ranges add context but should never become the only target.

From our design partners

“We finally have one number for whether the AI-written PRs are actually good. It changed how we staff reviews.”
SStaff EngineerSeries B fintech
“Plan from real signal, ship with agents, then see if the metric moved. That loop is the whole point.”
EEng ManagerDeveloper tools
“The measurement is transparent and the code is ours. That was the dealbreaker with the enterprise options.”
VVP EngineeringHealthcare SaaS

Measure what you ship.

Connect one repository and get your first delivery baseline.

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