Self-Hosting Statistics: We Measured It Two Ways and Got Opposite Answers

Self-hosting statistics articles quote market-size forecasts. We wanted to know what people actually run, so we measured two things that are publicly available and never published side by side.

The two rankings came out nearly inverted. That’s the finding, and it’s more useful than either list.

Docker pulls, normalised by age

Cumulative pull counts flatter old projects, so we divided each by the months since its Docker Hub repository was registered. Measured 18 August 2026:

Nextcloud has passed a billion pulls. And note Ollama: fifth place on a lifetime average, from a repository barely three years old against Nextcloud’s nine and a half.

GitHub stars, normalised the same way

One aside worth its own sentence: awesome-selfhosted — a list of self-hosted software — has more stars than any of the software on it, at 313,225. The index is the most-bookmarked thing in the category.

The inversion

Put the two rankings side by side and they fight:

Spearman rank correlation: −0.70. These aren’t two noisy estimates of the same thing. They’re anti-correlated: the projects pulled most are starred least.

Why, and what each one is really counting

A pull is not an install. Docker Hub counts every pull, including automated ones — continuous integration jobs, image rebuilds, orchestrators re-pulling on restart, and every scripted deployment. A mature, widely-automated project accrues pulls indefinitely from infrastructure that already exists. Nextcloud is also an Official Image, which attracts more automated traffic than a community image does.

A star is a bookmark placed at the moment of discovery. It costs nothing, implies no installation, and it’s overwhelmingly weighted toward things that are new and being talked about. It never decays.

So the two metrics measure opposite ends of a project’s life: stars capture arrival, pulls capture entrenchment. Read that way the inversion isn’t a paradox, it’s mechanical — and it means any “most popular self-hosted app” ranking is really a statement about which metric its author chose.

What neither measures is how many people are running the software. We don’t have that number and neither does anyone publishing a ranking of this kind.

Where AI tooling actually sits

The premise we set out to test was that AI and agent tooling is the fastest-growing self-hosting category. The honest answer is that our data is consistent with that but cannot prove it.

What we can say: Ollama tops the attention metric outright at 4,740 stars/month, ahead of projects two and four times its age, and it reaches fifth on the deployment metric from a repository a third the age of the leaders. A project accumulating pulls at 4.38 million a month after three years, when the top of the table has had a decade to build automated deployment, is not a marginal presence.

Why we can’t claim more. Proving fastest-growing requires a pull-count delta between two dates, and we have one snapshot. A lifetime average also actively understates a fast-growing young project, because its quiet early months are in the denominator — Ollama’s current rate is very likely well above 4.38M/month, but “very likely” isn’t a measurement. We’ve written the number we have rather than the number we’d like.

For the agent-specific picture, we measured star velocity across eight agent projects separately and found the same shape at a more extreme scale.

What we could not get

Stating this plainly, because a statistics article’s gaps matter as much as its numbers.

Market-size forecasts. The brief for this article cited self-hosting reaching $85.2 billion by 2034 at roughly 18.5% compound growth. We could not trace that to a primary source and haven’t included it. Figures like these are typically secondary aggregations of several analyst houses, and an unsourced projection in a statistics piece is worse than a gap.

Subreddit activity. We tried to measure r/selfhosted and r/homelab subscriber counts and post volume. Reddit returned HTTP 403 to every unauthenticated request, which is its current policy. No data, so no claims.

Hardware split. Mini-PC versus Raspberry Pi versus VPS versus decommissioned enterprise gear is a genuinely interesting breakdown and it requires survey data we don’t hold and couldn’t verify.

Cost distribution. Same problem. What people spend on self-hosting is survey territory.

What it costs to run this stuff

The one number we can supply from our own research is hosting cost, since most of the projects above run comfortably on a small VPS pointed at an external model API where relevant.

Hetzner — the best hardware per euro for a home-lab-in-the-cloud, at €19.99 for 4GB on the CPX line plus €0.50/month for an IPv4 address. Check availability on the cheaper cost-optimized tier, which has been showing as unavailable since the June 2026 repricing.

Hostinger — the cheapest capable option at $11.99 renewal for 4GB, rising to $14.99 for 8GB, with one-click Docker templates covering several of the projects above. That template support is the real argument: most first attempts fail at installation rather than in operation.

DigitalOcean — the documentation choice at $24 for 4GB, roughly double Hostinger’s rate, and its self-hosting guides are among the best written anywhere.

Contabo is the memory-per-pound winner in our cheapest VPS index at around $0.87 per GB — relevant if you’re running several of these at once. Its tracked link isn’t working on our end, so we’ve named it without linking.

For sizing, what a self-hosted stack actually needs covers the RAM floors, and price per gigabyte across the market is the normalised view.

How we checked this

Both datasets are ours, measured on 18 August 2026, and both are reproducible in a few minutes.

Pull counts came from hub.docker.com/v2/repositories/{namespace}/{repo}/, taking pull_count and date_registered from the JSON response. Star counts came from api.github.com/repos/{owner}/{repo}, taking stargazers_count and created_at. Months and the per-month rates are our arithmetic: days elapsed divided by 30.44, then the total divided by months. The Spearman rank correlation is computed over the nine projects present in both datasets. One presentational note: the per-month figures were computed from exact day counts, while the tables display months rounded to one decimal place — so recomputing from the displayed columns gives answers a few thousandths out. The rates as printed are the more precise ones.

The single most important caveat is that a pull is not an install. Docker Hub counts automated pulls identically to human ones — CI pipelines, image rebuilds, orchestrator restarts. Nextcloud’s position is additionally inflated by being an Official Image under the library/ namespace, which attracts automated traffic that community images don’t. Treat pull counts as a measure of automated plus human deployment activity, weighted heavily toward maturity.

Stars have the mirror-image problem, which we’ve covered before: a star is a free, permanent bookmark that implies nothing about installation, and velocity-since-creation flatters young projects because launch attention is front-loaded.

Two normalisation choices you could reasonably disagree with. We divided by age-since-registration, which assumes a roughly constant rate — it isn’t constant for anything on either list. And Docker Hub registration date is not the project’s start date; Home Assistant’s GitHub repository dates from 2013 and its Docker Hub repository from 2016, so the two tables measure slightly different clocks. Neither table is a growth rate; both are lifetime averages.

We measured one image per project, choosing the primary published image. Several of these projects also distribute through GitHub Container Registry, Quay or distribution-specific packages, and those installs are invisible to our numbers. Open WebUI is absent from the pull table for exactly this reason — it publishes to ghcr.io rather than Docker Hub, so no comparable figure exists here.

What we did not do: run any of these projects, survey any users, obtain telemetry, or measure change over time. Every figure is a single snapshot, and the article’s central claim is about the relationship between two metrics rather than about any project’s popularity.

Provider pricing is each host’s published rates read across this series in August 2026 rather than re-verified today.

The host links above are affiliate links. The cheapest option in the closing section is the one we can’t link to, and the article’s main conclusion — that popularity rankings in this category are artefacts of metric choice — undercuts the genre it belongs to.

FAQ

What is the most popular self-hosted application?

It depends entirely on which metric you use, and that’s the point. By Docker pulls per month it’s Nextcloud, which has passed a billion pulls. By GitHub stars per month it’s Ollama. Those two rankings are anti-correlated at −0.70.

Why do Docker pull counts and GitHub stars disagree?

Because they measure opposite ends of a project’s life. Pulls accumulate from automated redeployment of mature infrastructure, while stars are one-off bookmarks placed when something is newly discovered. Neither counts people running the software.

Is self-hosted AI tooling growing fastest?

Our data is consistent with it but doesn’t prove it. Ollama leads on stars per month and reaches fifth on pulls per month from a repository a third the age of the leaders. Proving “fastest-growing” needs two measurements over time and we have one.

How many pulls does Nextcloud have?

1,022,995,749 as of 18 August 2026 — about 8.85 million a month averaged over the life of its Docker Hub repository. Note it’s an Official Image, which attracts more automated pulls than community images.

Do Docker pull counts mean installations?

No. Every automated pull counts — CI jobs, rebuilds, orchestrators restarting containers. A single deployment can generate many pulls over its lifetime, and a pull can happen with no human involved.

How big is the self-hosting market?

We don’t know, and we’ve deliberately not quoted a figure. The forecast we were asked to cite couldn’t be traced to a primary source, and market-size numbers in this space are usually aggregations of several analyst estimates.

What does it cost to self-host these?

Most of them run on a 4GB VPS, which is €19.99 to $24 a month depending on provider — or under $7 per 8GB if you optimise purely for memory per pound. The exception is running language models locally, which is a different class of machine.