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How does your GitHub and Copilot spend compare? A guide to benchmarking engineering costs — FinOpsAid blog

How does your GitHub and Copilot spend compare? A guide to benchmarking engineering costs

You cut Copilot spend 10% — good or bad? An absolute number can't tell you. Only a benchmark can. Here's how to compare your GitHub costs against your own history and against peers, without trusting made-up averages.

Suppose you spend forty dollars per developer per month on Copilot. Is that good? You genuinely cannot answer that from the number alone. It might be a bargain for your engineering profile or a sign you're carrying dead seats — and nothing about the figure "forty dollars" tells you which. Absolute cost numbers answer how much. They can't answer is that good. For that you need a reference point, and finding an honest one is harder than it sounds.

The number that means nothing on its own

Most cost dashboards are built to report absolutes: this much on Actions, this many seats, this per developer. Those numbers are necessary and completely insufficient for judgment. "Is our spend efficient?" is a comparative question, and a report full of standalone figures quietly refuses to answer it.

There are only two things you can compare against: your own past, and other organizations. The first is easy to get and underused. The second is genuinely useful and easy to fake. Both are worth doing carefully.

Internal benchmarks: your own history first

The cheapest benchmark you own is your former self. Is cost per active developer higher or lower than last quarter? Is your Copilot active-to-licensed ratio improving as you reclaim seats, or sliding as onboarding outpaces cleanup? Are Actions minutes per repository trending up? None of these need an external dataset — they need only that you kept your history, which is exactly the thing GitHub discards after a few months and the reason snapshotting matters.

Trend against yourself first, because it's unimpeachable. Nobody can argue your cohort is wrong when the cohort is you. A metric moving the wrong way against your own baseline is a real finding before you've compared to a single other company.

External benchmarks: the peer cohort

Reading a peer cohort, and what suppression looks like A cohort distribution showing the twenty-fifth, fiftieth and seventy-fifth percentiles with your organization marked at the thirty-first percentile. Below it, a second cohort with too few organizations to publish, rendered as a suppressed state rather than a risky number. COST PER ACTIVE COPILOT USER — PEER COHORT p25 p50 p75 YOU · 31ST PERCENTILE Cheaper than the median — but the useful question is whether the gap is seat hygiene or scale. SAME METRIC, NARROWER COHORT SUPPRESSED Too few organizations to publish without identifying one of them
A percentile is only honest if the cohort behind it is big enough to hide in. Below the k-anonymity threshold the correct output is no number at all — a benchmark you can't publish safely is not a benchmark you should trust. Illustrative distribution.

The harder, higher-value comparison is against organizations like yours. The metrics worth benchmarking externally are the unit economics, not the totals:

The question these answer is positional: are you at the twenty-fifth percentile or the seventy-fifth? That framing — where do I sit in the distribution — is what turns "we spend forty dollars per dev" into "we spend more than three-quarters of orgs our size, and here's the seat waste driving it."

Why honest external benchmarks are hard to get

Here's the problem: no organization publishes its real GitHub spend, and the "industry averages" that circulate in vendor decks are usually marketing with a number attached. A benchmark is only as trustworthy as the cohort behind it, and most benchmarks can't tell you what their cohort is.

A credible peer benchmark has to come from aggregated, anonymized data across many real organizations — and it has to be gated so that no individual org can be reverse-engineered out of it. That last part matters both ethically and statistically: a "peer average" computed from three companies isn't a benchmark, it's an exposure of those three companies. Real cohort benchmarking suppresses any group too small to anonymize (a k-anonymity threshold), which means sometimes the honest answer is "not enough peers to compare yet." Be suspicious of any benchmark that always has a number and never explains where it came from.

Using benchmarks without abusing them

A benchmark is a question, not a verdict. Landing below the median on acceptance rate doesn't mean you're failing — it means ask why. Maybe your codebase is unusual, maybe your rollout is young, maybe there's a real adoption problem to fix. The benchmark's job is to point at where to look, not to hand down a grade.

The failure mode is managing to the benchmark — optimizing a metric to hit a percentile rather than to improve the underlying reality. Use peer data to find the gaps worth investigating, pair it with your own trend, and decide with context. A benchmark that starts a conversation is doing its job; one that ends a conversation is being misused.

Together, the two references answer the two questions: your history tells you which way you're moving, and the peer cohort tells you whether where you've landed is good. That comparison — your unit economics against an anonymized cohort of similar orgs, with small cohorts suppressed so the numbers stay honest — is one of the few things no single-tenant GitHub script can produce, and it's exactly what FinOpsAid's Benchmarking view is for. It pairs naturally with measuring Copilot ROI and attributing spend to teams: benchmarks tell you where to look, attribution tells you whose it is.

Frequently asked questions

Why can't I tell whether our GitHub spend is reasonable?

Because an absolute number carries no reference point. Cutting Copilot spend 10% is good or bad depending entirely on what comparable organizations did over the same period. Only a benchmark — your own history, or a genuine peer cohort — turns a figure into a judgement.

Which benchmark should I start with, internal or external?

Internal. Your own history is available immediately, needs nobody's cooperation, and controls for everything that makes cross-organization comparison hard. External cohorts add real information but only once the internal trend is established and you know what normal looks like for you.

Why are external GitHub cost benchmarks hard to trust?

Because the denominators rarely mean the same thing. One organization's monorepo is another's two hundred services, and definitions of an active developer vary. A comparison is only meaningful where both sides compute the metric identically, which is why a short list of comparable numbers beats a long list of plausible ones.

Want to see where your GitHub and Copilot spend sits against comparable orgs? Connect your GitHub org — read-only and free during beta — or explore the demo dashboard first.