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Apify: guides & tutorials
Cloud scraper hosting: run reliable extraction without your own server fleet or ops overhead. Find pre-built scrapers in the Apify Store or deploy custom actors.
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Apify is a cloud platform for web scraping and automation that lets you run reliable extraction jobs without managing your own servers. Run pre-built scrapers from the Apify Store for sites like Google Maps, LinkedIn, and Amazon, or deploy custom actors in JavaScript or Python. Results land in structured datasets you can download or pipe into your tools.
These guides cover the whole platform: what Actors are, how the free tier and pricing work, building and deploying your own scrapers, and wiring Apify into Make, n8n, Zapier, Google Sheets, and databases via the API and webhooks. Below you will find beginner walkthroughs, developer tutorials, and practical how-tos for getting data out of Apify and into production.
Many RAG (Retrieval-Augmented Generation) projects fail in production not because the technology doesn't work, but because teams skip the hard parts: chunking strategy, embedding model selection, retrieval quality measurement, and stale data management. Validate each step on your own corpus — field names, SDK versions, and Actor outputs change over time.
The pipeline: crawl websites → chunk intelligently → embed → store in vector DB → retrieve with reranking → generate answers with citations.
TL;DR:
| Stage | Tool | Key decisions |
|---|
| Crawl | Apify Website Content Crawler | Markdown output, max depth, content filtering |
| Chunk | LangChain RecursiveCharacterTextSplitter | ~2000 character chunks, ~200 overlap (~500 tokens/chunk at ~4 chars/token — tune for your content) |
| Embed | OpenAI text-embedding-3-small or local all-MiniLM-L6-v2 | Cost vs quality trade-off |
| Store | Qdrant or pgvector | Managed vs self-hosted |
| Retrieve | Dense vector search + Cohere rerank (sample below) | Top-k=20 candidates, rerank to top-5 — add BM25 / sparse hybrid in Qdrant if you need keyword-heavy queries |
| Generate | Claude Sonnet | With source citations |
Prerequisites:
- Python 3.10+ or Node.js 18+
- Apify account (sign up)
- Vector database (Qdrant Cloud free tier or self-hosted)
- LLM API key (Claude, GPT-4, or self-hosted Ollama)
Enterprise competitive intelligence tools — Crayon, Klue, Kompyte, Similarweb — charge $300–$2,000/month (quote-based, plan-dependent) for competitive monitoring dashboards. This same monitoring can be built with Apify for data collection, Claude for analysis, n8n for orchestration, and a free dashboard — for under $50/month (starting cost; scales with competitor count, Actor fees, and proxy usage).
This guide builds it step by step: from identifying what to monitor, to automated daily scrapes, to AI-powered change detection that alerts your team in Slack when competitors make moves that matter.
TL;DR:
| Component | Tool | Cost |
|---|
| Data collection | Apify (5 competitors, daily) | ~$30/mo |
| Analysis | Claude API (change detection, summarization) | ~$5–15/mo |
| Orchestration | n8n (self-hosted) | $0 |
| Dashboard | Google Sheets or Grafana | $0 |
| Alerts | Slack webhooks | $0 |
| Total | | ~$35–45/mo |
The key is not just scraping data — it is building a complete pipeline that goes from raw web data to scored, enriched leads in your CRM, automatically.
This playbook covers the architecture, tool-by-tool setup, cost model, and compliance framework for building an AI lead generation system in 2026.
TL;DR:
| Pipeline stage | Tool | What it does |
|---|
| Source | Apify Google Maps, LinkedIn, directory scrapers | Collect raw lead data from public sources |
| Enrich | Claude API / Ollama (local) | Add company data, tech stack, revenue estimates |
| Score | Claude API / Ollama | Rate leads 1–10 against your Ideal Customer Profile (ICP) |
| Route | Clay, HubSpot, Google Sheets | Push scored leads to CRM |
| Orchestrate | n8n / Make.com | Automate the entire pipeline on schedule |
Prerequisites:
- Apify account (Starter plan: $29/mo for production use)
- Claude API key or self-hosted Ollama (see Self-Host AI Stack)
- CRM account (HubSpot free, Clay, or Google Sheets)
- n8n or Make.com for orchestration
Clay (now Mesh) does a lot of the heavy lifting when you connect email, calendar, LinkedIn, and Twitter. What it won’t do on its own is keep polling the open web forever: enrichment tends to reflect what was true when the contact landed in your book, not every headline or title change afterward.
Apify is where scheduled scraping helps — job moves, company news, fresh posts, GitHub activity — then you fold those findings back into Mesh as notes or updates.
Here are three workflows that combine the two without pretending there’s a single “native” button for it.
