Best Web Scraping APIs That Require Zero Installation (IteraTools vs Apify vs ScrapingBee)

Setting up a web scraper locally means Puppeteer, Playwright, managing Chrome binaries, handling proxies, and fighting anti-bot systems. For many use cases — especially in production — a scraping API is far more practical.

The promise: send a URL, get back HTML or structured data. No browser to manage, no proxy rotation to configure, no CAPTCHA headaches. This guide compares the leading options.

What to Consider When Choosing a Scraping API

• JavaScript rendering: Can it handle SPAs and dynamically loaded content?
• Anti-bot bypass: Does it handle Cloudflare, CAPTCHA, and fingerprinting?
• Response format: Raw HTML, markdown, or structured JSON?
• Speed: Time to first byte matters for real-time use cases.
• Price: Per-page or per-credit; watch for hidden costs (JS rendering often costs extra).
• Rate limits: Can it handle burst traffic for bulk scraping jobs?

Comparison Table

Tool	Price	JS Rendering	Anti-Bot	Output Format	Limitations
IteraTools	~$0.002/request (credits)	Yes (headless Chrome)	Basic	HTML, markdown, text	Less advanced anti-bot
Apify	$49+/mo or per compute	Yes (full browser)	Excellent	Flexible (actors)	Complex actor model
ScrapingBee	$49+/mo (250K credits)	Yes	Very Good	HTML, JSON	Expensive for high volume
Browserless	$100+/mo	Yes	Good	Raw browser API	Requires code
Jina AI Reader	Free tier / credits	Basic	None	Markdown	Limited control

IteraTools Scraping — How to Use It

Simple page fetch with markdown output:

curl -X POST https://api.iteratools.com/v1/scrape \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://news.ycombinator.com",
    "render_js": false,
    "output": "markdown"
  }'

For JavaScript-rendered pages:

curl -X POST https://api.iteratools.com/v1/scrape \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://app.example.com/dashboard",
    "render_js": true,
    "wait_for": ".data-loaded",
    "output": "html"
  }'

For crawling multiple pages:

curl -X POST https://api.iteratools.com/v1/crawl \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://docs.example.com",
    "max_pages": 50,
    "output": "markdown"
  }'

Complete Python Example

import requests
import json

API_KEY = "your_api_key_here"
BASE_URL = "https://api.iteratools.com/v1"

def scrape_page(url: str, render_js: bool = False) -> dict:
    """Scrape a single page."""
    response = requests.post(
        f"{BASE_URL}/scrape",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={
            "url": url,
            "render_js": render_js,
            "output": "markdown"
        }
    )
    response.raise_for_status()
    return response.json()

def crawl_site(start_url: str, max_pages: int = 20) -> list[dict]:
    """Crawl a site and return all pages."""
    response = requests.post(
        f"{BASE_URL}/crawl",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={
            "url": start_url,
            "max_pages": max_pages,
            "output": "markdown"
        }
    )
    response.raise_for_status()
    return response.json()["pages"]

def scrape_for_llm(url: str) -> str:
    """Scrape a page and return clean text for LLM processing."""
    result = scrape_page(url, render_js=True)
    return result.get("markdown", result.get("text", ""))

if __name__ == "__main__":
    # Example 1: Scrape a news site
    result = scrape_page("https://techcrunch.com", render_js=False)
    print(f"Scraped {len(result['markdown'])} chars")
    print(result["markdown"][:500])

    # Example 2: Build a knowledge base from docs
    pages = crawl_site("https://docs.iteratools.com", max_pages=30)
    knowledge_base = []
    for page in pages:
        knowledge_base.append({
            "url": page["url"],
            "title": page["title"],
            "content": page["markdown"]
        })

    with open("knowledge_base.json", "w") as f:
        json.dump(knowledge_base, f, indent=2)

    print(f"Knowledge base: {len(knowledge_base)} pages saved")

    # Example 3: Scrape for RAG pipeline
    urls = [
        "https://example.com/page1",
        "https://example.com/page2",
        "https://example.com/page3"
    ]

    documents = []
    for url in urls:
        text = scrape_for_llm(url)
        documents.append({"url": url, "text": text})
        print(f"✓ {url}: {len(text)} chars")

Use Cases by Tool

• IteraTools: Best for developers who need scraping + other capabilities (screenshots, PDF extraction, search) in one API. Perfect for building RAG pipelines and AI agents.
• Apify: Best for complex scraping workflows with the actor marketplace — pre-built scrapers for Amazon, LinkedIn, etc.
• ScrapingBee: Best for enterprise-grade anti-bot bypass with detailed documentation and dedicated support.

Conclusion

If you need web scraping as one of many tools in your stack (not your core business), IteraTools is the most practical choice: single API key, credit-based pricing, and you also get screenshot, crawl, PDF, and 60+ other endpoints. No CLI tools, no proxies to manage.

For high-volume scraping with advanced anti-bot requirements, ScrapingBee or Apify are worth the higher price.

→ Start scraping with IteraTools — no monthly commitment.