Running it on a couple of small sites. It is exactly what the pitch says: one Go binary or one container, a single Postgres/SQLite DB, one script tag. Points worth knowing before you commit:

• The hosted version has a free tier for non-commercial sites; self-hosting is completely free, no account needed, and backup is just “dump the database”.
• Because it counts pageviews anonymously by design, there are no funnels, sessions, or referrer chains like Plausible/Matomo. It deliberately does not keep enough data for that. If you might want path analysis later, that is the one thing Goatcounter will not give you.
• Bot filtering is reasonable out of the box; on a small tech site I saw roughly 30-40% of raw hits classified as bots, which matched my nginx log estimates well.
• Self-host gotcha: behind a reverse proxy, make sure you pass the real client IP (X-Forwarded-For), or everything counts as one visitor from the proxy address.

For the “step counter” use case you describe, it is the best fit in this space. The moment you want cohort/behavior analytics is when Matomo or Plausible earn their resource cost.

Running it on a couple of small sites. It is exactly what the pitch says: one Go binary or one container, a single Postgres/SQLite DB, one script tag. Points worth knowing before you commit:

• The hosted version has a free tier for non-commercial sites; self-hosting is completely free, no account needed, and backup is just “dump the database”.
• Because it counts pageviews anonymously by design, there are no funnels, sessions, or referrer chains like Plausible/Matomo. It deliberately does not keep enough data for that. If you might want path analysis later, that is the one thing Goatcounter will not give you.
• Bot filtering is reasonable out of the box; on a small tech site I saw roughly 30-40% of raw hits classified as bots, which matched my nginx log estimates well.
• Self-host gotcha: behind a reverse proxy, make sure you pass the real client IP (X-Forwarded-For), or everything counts as one visitor from the proxy address.

For the “step counter” use case you describe, it is the best fit in this space. The moment you want cohort/behavior analytics is when Matomo or Plausible earn their resource cost.

Goatcounter is a great pick for minimal analytics. A few things I wish I’d known before deploying it:

1. It’s a single Go binary — no database server needed. SQLite is fully supported and plenty for small/medium sites. Backup = copy one file.

2. Run it behind a reverse proxy with a dedicated subdomain (e.g. stats.example.com) and keep it on its own system user. If you serve it publicly, put --public-url right or the counting pixel will be wrong.

3. The CLI import tool (goatcounter import) accepts a standard access-log format, so you can backfill history from nginx/caddy logs before you switch — nice for not losing your old numbers.

4. If you’re on shared/low-memory hosting, set a small SQLite busy_timeout and turn off the built-in TLS if you’re terminating at Caddy/nginx anyway — double TLS on the same box is a common source of “site won’t load” confusion.

5. Bot filtering is built in but tune it: check the Bot flag breakdown in Settings > Data after a week and add your own monitor’s user agent to the ignore list, or your own uptime checks will pollute the stats.

Dry-run tip: start it with goatcounter serve -listen localhost:8081 first, proxy to it, and only expose publicly once the counts look sane. Have fun — it’s genuinely one of the least fussy self-hosted apps out there.