How I Discovered Appify: The Marketplace That Lets You Scrape (Almost) Anything

When I first learned about scraping, it felt powerful — but also limited. I could grab data from a single page, maybe a handful of URLs, but scaling that up to real-world use cases (like hundreds of leads, TikTok trends, or job listings) seemed impossible without heavy coding.

That’s when I found Apify (Apify).

And the way I understood it was simple: it’s like an app store for scraping.

Actors: The Secret Superpower

On Appify, there are over 4,500 pre-built “actors”.

Think of actors like virtual assistants who already know how to do a job. You just tell them what you want, and they’ll do the heavy lifting:

Want to find dentists in New York? Use the Google Maps actor.

Want the latest TikTok videos with #AIcontent? Use the TikTok actor.

Need fresh job postings? The LinkedIn job scraper has you covered.

Looking for leads? There’s even one that taps into Apollo’s database.

Each actor is basically a script that’s already built, tested, and ready to go. You don’t need to reinvent the wheel.

The Two-Step Formula

What made Appify click for me was realizing it works best as a two-step process inside n8n (or any automation tool):

Start the actor — This sends a request to begin scraping (like placing an order).

Get the results — Once the scraping finishes, you pull the data back in.

Simple, but powerful.

For example, when I asked Appify to find 50 dentists in New York, the workflow went like this:

I hit “Start Actor” in n8n → Appify spun up the Google Maps extractor.

A few seconds later, the run finished → I pulled back structured JSON with names, addresses, phone numbers, and websites.

Boom. A fresh lead list, no manual copy-pasting.

Why Not Just Run It Synchronously?

At first, I thought: Why not just run it synchronously? (meaning wait until the results are ready).

I tried it. The workflow sat there spinning… and then gave me nothing back.

That’s when I learned: sometimes scraping isn’t instant. The data might take 15–20 seconds to prepare. That’s why the two-step method is more reliable.

If it takes time? Add a wait node in your workflow (I set mine to 22 seconds). By the time it checks again, the data’s ready to go.

Scaling Beyond Basics

Once I had this workflow working, my imagination went wild.

Leads — Scrape 200 plumbers in New York instead of 50.

Market research — Pull trending TikTok hashtags and captions automatically.

Competitor tracking — Monitor new LinkedIn job posts in my industry.

Content pipelines — Scrape Instagram comments, then feed them into an AI summarizer.

And because everything comes back as structured JSON, I can do whatever I want with the data: enrich it, send emails, push to a CRM, or run AI analysis.

The Polling Trick

One last lesson: scraping can be unpredictable. Sometimes an actor finishes in 5 seconds, sometimes 20+.

Instead of setting a fixed wait time, you can use polling:

Start the actor.

Check if results are ready.

If not, wait a bit and check again.

Repeat until the data’s cooked.

This way, your workflow is smart enough to adapt — no more guessing how long to wait.

Why Appify Changed the Game for Me

Before Appify, scraping felt like hacking things together — brittle, messy, unreliable.

After Appify, it feels like browsing an automation menu. Want data from TikTok? Order it. Need leads from Google Maps? Order it.

And best of all: I don’t need to code the scraping logic. Someone already did that for me.

All I do is connect the pieces inside n8n and let the automation run on its own.

✨ If you’ve ever wanted to scrape the web at scale without writing scripts, Appify is the shortcut. Actors are the waiters. The internet is the kitchen. And now, your workflow can order almost anything off the menu.