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My click tracker said 518 humans. Only 134 people loaded the page.

StackPick — Sun, 26 Jul 2026 07:30:44 +0000

Disclosure: this post contains links to paid products and affiliate programs. I may receive compensation if you sign up or buy through them. Pricing shown is verified against the vendor's public pricing page.

For a few weeks my analytics told me a comfortable story: 518 humans had clicked an outbound link in the last 30 days. Then I put that number next to a different one from the same database — 134 page views — and the story fell apart.

Nobody clicks a link on your site more times than they load a page on your site. A ratio above 1.0 is not a suspicious trend to monitor. It is arithmetic proof that the tracker is wrong.

Mine was at 3.9.

The setup

The site is a static build on Cloudflare Pages. Outbound links do not point straight at the destination — they go through a redirect worker at /r/{slug}, which records the click server-side and then 302s the visitor onward. Standard pattern, and it survives ad blockers, which is why I used it.

Each click row stores a source_page so I know which page produced it. The worker fills it like this:

const sourcePage = request.headers.get("Referer") || `cta:${ctaId}`;

The fallback exists for a real reason. Some browsers and privacy settings strip the Referer header, and I did not want those clicks landing in the database as anonymous rows I could never attribute. So if there is no referer, fall back to the CTA id, which I already have.

Downstream, the bot filter decided whether a click was human. Its reasoning, roughly: a real browser on my site sends context, so a row that has a source_page came from a browser, and an empty one is suspect.

-- what "human" used to mean
WHERE source_page IS NOT NULL AND source_page != ''

Read those two snippets together and the bug is obvious. It was not obvious to me for weeks, because they live in different files written on different days.

Why the fallback poisoned the filter

ctaId comes from the query string. The link in the HTML is literally:

<a href="/r/wati?cta=best-whatsapp-wati">Try Wati</a>

A crawler fetching that URL sends no Referer. So the worker takes the fallback branch and writes source_page = "cta:best-whatsapp-wati".

That is a non-empty source_page. Which is exactly what the filter accepted as proof of a browser.

The fallback was not recording "a human whose referer was stripped". It was recording "the value that was already sitting in the href" — a value any crawler following the link reproduces perfectly, because it is in the markup. I had built a bot detector whose evidence of humanity was a string the bot could read off the page.

The fix

One condition. A real browser referer is an absolute URL, so require that shape instead of mere presence:

-- source_page now has to look like a referer, not like something copied from the href
WHERE source_page LIKE 'http%'

Reclassifying the whole table moved 518 "humans" down to 218. Ratio went from 3.9 to 1.6.

1.6 is still wrong, and that is the useful part

The honest reading of 1.6 is "still inflated, just less". Plenty of crawlers do send a Referer, so the new rule catches the lazy ones and misses the polite ones. I did not want to ship a number I would have to re-litigate every month, so the reporting script now prints both figures and the ratio between them:

"reality_check_30d": {
  "page_views_js": 134,
  "redirect_clicks_human": 218,
  "inflation_ratio": 1.6,
  "verdict": "inflated"
}

That block is deliberately annoying. As long as the ratio sits above 1.0, every report I read carries a note that the click number is not trustworthy. A metric that announces its own error bar beats a metric that quietly looks fine.

What I would generalise from this

Cross-check server-side counts against a client-side one. Server-side tracking is popular precisely because ad blockers cannot stop it — but that same property means bots cannot be stopped either. The JavaScript page_view event undercounts (blockers, no-JS) while the server-side click count overcounts (bots). Neither is truth, and the gap between them is the diagnostic. One number alone would never have told me anything was wrong.

Write down the invariants your data must satisfy. Not thresholds like "alert if clicks drop 20%" — invariants, things that cannot be true in a working system. Outbound clicks ≤ page views is one. They cost nothing to assert and they fail loudly the moment a fallback quietly changes meaning.

Watch fallback values that come from the request. A default is fine when it encodes something you know independently. It is dangerous when it echoes attacker- or crawler-controlled input back into your data, because downstream code cannot tell the echo from the real thing. My fallback did not fabricate the value — it copied it from the href, which made it indistinguishable from the genuine article.

The bug is rarely inside one file. Both snippets were reasonable in isolation. Neither author — both of them me — was thinking about the other. The defect lived in the seam, and the only thing that surfaced it was comparing two numbers that had no business disagreeing.

I keep a public verified pricing index for automation and messaging tools, which is what the redirect endpoint was tracking clicks on in the first place. If you want the arithmetic-check idea without the write-up, the short version is: find two metrics in your system that must hold an order relationship, and assert it.

n8n vs Make

StackPick — Sun, 26 Jul 2026 02:21:05 +0000

Feature lists won't settle this one; both platforms can build almost any workflow. The decision comes down to two things: how each one bills, and whether you're willing to self-host. Use the Quick answer, cost table (1k / 10k / 50k) and 3-year TCO above — then read the billing caveats below.

The billing unit is the whole game

Make bills in credits — standard module runs generally consume one credit, while some Make AI features use variable credits. A 15-module standard scenario that runs 1,000 times is roughly 15,000 credits.
n8n bills per execution — one full workflow run counts once, whether it has 5 nodes or 50.

That means workflow shape matters. A multi-module workflow can consume many Make credits per run while n8n Cloud generally bills one workflow execution. AI modules and retries can change actual usage; measure both before choosing.

Simple, short scenarios flip it: Make's free and low tiers are generous when workflows are 2–4 modules, and its per-operation price is tiny at small scale.

Self-hosting: n8n's trump card

n8n's Community Edition is source-available and free with unlimited executions on your own server — a $5/month VPS runs serious automation loads. Make has no equivalent. If your team can run Docker, Cloud TCO above is the ceiling, not the floor: self-host drops execution fees to $0. That's also the honest caveat: self-hosting means you own updates, backups and uptime. The cost table shows n8n Cloud so SaaS quotes stay comparable to Make.

AI workflows

n8n treats AI agents as first-class citizens: native LangChain agent nodes, vector stores, RAG pipelines, and a template library where 67% of new templates already use AI nodes. Make has AI modules and an assistant, but agentic patterns (tool-calling loops, multi-agent handoffs) fit n8n's model more naturally. Most of the ready-made AI agent templates you'll find — including ours — ship as n8n JSON.

Hidden costs and limits

n8n Cloud caps active workflows on lower tiers (5 on Starter) — automation-heavy teams hit that before they hit execution limits.
Make credits can rise when scenarios include iterators, retries or variable-credit AI modules; model a worst-case month in the automation cost calculator.
n8n's learning curve is real: expressions, JSON wrangling and node debugging. Make's UI is friendlier for marketers.

When to choose neither

Two apps, one trigger, no logic — Zapier's free tier or a native integration does it with less friction (see Zapier vs Make). Enterprise governance needs (SSO, audit logs, SOC2 reporting) point to Workato instead.

Choose n8n if workflows are complex or AI-driven, someone on the team is technical, or you want the self-hosted free option in your back pocket. Choose Make if visual simplicity for non-developers matters more and your scenarios stay short.

If the real pain is slow lead response (not tool price), model incremental gross profit with the lead recovery ROI calculator before you buy more seats.

Pricing sources

n8n pricing — verified 2026-07-01
Make pricing — verified 2026-06-12

Originally published on StackPick — verified SaaS pricing, cost calculators and tool comparisons. Free n8n templates: github.com/marcelobm33/stackpick-n8n-templates

How to Make Money with n8n (2026)

StackPick — Sun, 26 Jul 2026 02:19:36 +0000

Disclosure: this post contains links to paid products and affiliate programs. I may receive compensation if you sign up or buy through them. Pricing shown is verified against the vendor's public pricing page.

How to make money with n8n in practice means shipping value someone pays for — not “set and forget” YouTube schemes. n8n is free to self-host and bills Cloud by workflow execution, not per step. That cost shape is why freelancers and agencies productize automation. Four paths that match real demand:

Quick answer

Product — free JSON proof → paid pack with docs
Service — migrate Zapier/Make → n8n for a niche
Media — pricing comparisons + affiliate (slow)
Ops — use n8n inside your own business to stop lead leakage

Start with one niche pain you already understand. Distribution beats another unused template.

1. Sell ready-made workflows (product)

Ship a tested n8n JSON for a painful niche job: missed-call → WhatsApp reply, lead enrichment, invoice chase. Price a free starter JSON to prove competence, then a paid pack with docs and support.

Free reference: WhatsApp lead-reply gist · GitHub templates
Low-friction paid entry (no n8n required): WhatsApp Meta Template Kit ($19) — Meta-ready copy + approval checklist
Full automation pack: dealership lead-recovery pack ($49)

Reality check: buyers want setup notes, template message approval steps and failure modes — not a raw export.

2. Freelance / agency automation (service)

Charge setup + monthly care for Make/Zapier migrations onto n8n Cloud or a VPS. Typical offer: audit current Zaps → rebuild critical paths → document ownership.

Compare cost units before quoting clients: n8n vs Make · Zapier vs Make · automation cost calculator.

3. Affiliate + comparison content (media)

Publish honest pricing pages for tools your workflows use (WhatsApp BSPs, email, CRM). Disclose affiliate links. Revenue only appears after someone buys — clicks alone pay $0.

StackPick model: verified pricing tables → tracked /r/ links → VPS metrics. See how we make money.

4. Internal ops (save money = make money)

If you already run a business, n8n recovers leaked leads and after-hours calls. Model ROI before buying seats: lead recovery ROI calculator · lead follow-up stack.

What does not work

“Set and forget” schemes with no niche, no distribution, no support
Scraping random templates without testing Meta WhatsApp / API credentials
Expecting Google page-1 traffic on day one for head terms like “best automation software”

Minimal stack to start

n8n Cloud trial or $5–10/mo VPS self-host
One painful workflow in a niche you know
One distribution channel (Reddit niche, community.n8n, client outreach)
Optional: affiliate programs for tools you already recommend (pricing index)

FAQ — how to make money with n8n

Can you make money with n8n as a beginner?

Yes, if you pick a narrow job (one industry + one workflow) and sell setup help or a documented JSON. Broader “AI automation agency” pitches without a niche usually stall.

Is selling n8n templates profitable?

Some people earn from Gumroad/marketplaces; most revenue comes from the docs + support, not the file. Free templates build trust; paid packs convert after proof.

n8n Cloud or self-host for a side business?

Self-host if you want margin control and can maintain a VPS. Cloud if clients need reliability without you babysitting Docker. Model executions: n8n vs Make pricing.

Next reads

Originally published on StackPick — verified SaaS pricing, cost calculators and tool comparisons. Free n8n templates: github.com/marcelobm33/stackpick-n8n-templates