Make.com Web Scraping: No-Code Data Extraction Compared to Developer APIs

Make.com (formerly Integromat) is one of the most popular no-code automation platforms, with over 3,000 app integrations and a visual scenario builder. But when it comes to Make.com web scraping, the platform has real limitations that developers and data teams run into quickly.

This comparison breaks down what Make.com actually offers for web scraping, how it compares to dedicated scraping APIs like SearchHive, and when each approach makes sense.

Key Takeaways

• Make.com works for simple, low-volume data extraction as part of broader automation workflows
• Web scraping on Make.com is limited to 1,000 operations on the free plan and has a 5-minute execution cap
• Dedicated scraping APIs like SearchHive handle JavaScript rendering, anti-bot bypass, and structured data extraction out of the box
• At scale (10K+ pages/month), SearchHive costs 5-10x less than Make.com for equivalent scraping throughput
• Make.com's HTTP module lacks proxy rotation, retries, and parsing tools that scraping APIs provide natively

Comparison Table: Make.com vs SearchHive for Web Scraping

Make.com Web Scraping: What Actually Works

Make.com offers several modules that can be used for web scraping:

HTTP Module - The primary tool for fetching web pages. Supports GET/POST requests, custom headers, and basic authentication. However, it returns raw HTML that you need to parse yourself.

HTML Extract - A built-in tool for parsing HTML with CSS selectors. Useful for pulling specific elements from pages, but limited compared to dedicated scraping tools.

Iterators and Aggregators - For processing multiple pages in sequence. You can loop through URLs, extract data, and aggregate results into arrays.

The typical Make.com scraping scenario looks like this:

1. Trigger on a schedule or webhook
2. Use HTTP module to fetch a URL
3. Parse HTML with the HTML Extract module
4. Map extracted fields to your data structure
5. Push results to Google Sheets, Airtable, or a database

This works fine for extracting a few hundred pages per month from simple, static websites. The problems start when you need to handle JavaScript-rendered content, anti-bot protections, or scale beyond Make.com's operation limits.

Where Make.com Falls Short for Scraping

No JavaScript rendering. Make.com's HTTP module fetches the raw HTML response. If a page relies on JavaScript to render content (which most modern sites do), you'll get empty containers or incomplete data. There is no built-in headless browser.

No proxy rotation. Scraping the same domain repeatedly from a single IP gets you blocked quickly. Make.com offers no proxy rotation -- you'd need to integrate a third-party proxy service, adding cost and complexity.

Operation limits are tight. The free plan gives you 1,000 operations per month. Each HTTP request counts as one operation, and a typical scraping scenario uses 3-5 operations per page (fetch, parse, map, store). That's roughly 200-300 pages per month on the free tier.

5-minute execution cap on free plans. If your scenario takes more than 5 minutes to complete, it gets killed. Paid plans raise this to 40 minutes, but scraping thousands of pages still requires splitting work across multiple scheduled runs.

No built-in retries or error handling for scraping. When a request fails or returns a CAPTCHA page, Make.com doesn't automatically retry with different parameters. You need to build custom error-handling logic.

SearchHive ScrapeForge: The Developer Alternative

SearchHive's ScrapeForge API is built specifically for web scraping, handling the challenges that Make.com leaves to you:

• Headless Chrome rendering for JavaScript-heavy pages
• Automatic proxy rotation across residential IPs
• Structured data extraction returning clean JSON or Markdown
• Automatic retries with exponential backoff
• Concurrent requests for high-throughput scraping

Here's how the same scraping task looks with SearchHive:

import requests

response = requests.post(
    "https://api.searchhive.dev/v1/scrape",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "url": "https://example.com/products",
        "format": "markdown",
        "render_js": True
    }
)

data = response.json()
print(data["content"]["markdown"])

For batch scraping, use the Python SDK:

from searchhive import ScrapeForge

client = ScrapeForge(api_key="YOUR_API_KEY")

urls = [
    "https://example.com/products/page-1",
    "https://example.com/products/page-2",
    "https://example.com/products/page-3",
]

results = client.scrape_many(urls, format="markdown")
for url, result in results:
    print(f"{url}: {len(result.content)} chars extracted")

Pricing Deep Dive

Make.com Pricing (for scraping workloads)

Each scraping scenario typically consumes 3-5 operations per page (HTTP request + HTML parse + field mapping + storage write). The paid plans all offer the same 10,000 operations -- the price difference covers team features, not more capacity.

SearchHive Pricing (for scraping workloads)

SearchHive charges 1 credit per ScrapeForge request. At 100K pages/month, that's $0.00049 per page compared to Make.com's $0.0245 per page (at $49 Builder vs $10.59 Core for 2,000 pages). SearchHive is roughly 50x cheaper per page at the Builder tier.

When to Use Each

Choose Make.com when:

• You're already using Make.com for other automations and need to scrape a few hundred static pages
• Your team is non-technical and needs a visual builder
• Scraping is a small part of a larger workflow (e.g., scrape a page, then create a task in Asana)

Choose SearchHive when:

• You need to scrape thousands of pages reliably
• Target sites use JavaScript rendering
• You need proxy rotation to avoid getting blocked
• You want structured data output without manual parsing
• Cost efficiency matters at scale

Code Example: Migrating from Make.com to SearchHive

If you're currently scraping with Make.com and hitting limitations, here's how to replicate a typical scenario in Python with SearchHive:

from searchhive import ScrapeForge
import json
import csv

client = ScrapeForge(api_key="YOUR_API_KEY")

# Scrape product listing pages
pages = [
    "https://store.example.com/collections/electronics",
    "https://store.example.com/collections/electronics?page=2",
    "https://store.example.com/collections/electronics?page=3",
]

results = client.scrape_many(pages, format="markdown")

# Save results (in Make.com you'd route to Google Sheets)
with open("products.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(["Page", "Content Length", "Extracted"])
    for url, result in zip(pages, results):
        content = result.content if hasattr(result, "content") else str(result)
        writer.writerow([url, len(content), content[:200]])

print(f"Scraped {len(results)} pages successfully")

For more complex extraction, combine ScrapeForge with SearchHive's DeepDive API for AI-powered data structuring:

from searchhive import ScrapeForge, DeepDive

scrape = ScrapeForge(api_key="YOUR_API_KEY")
deep = DeepDive(api_key="YOUR_API_KEY")

# Scrape raw content
raw = scrape.scrape("https://example.com/products", format="markdown")

# Extract structured data with AI
structured = deep.extract(
    content=raw.content,
    schema={
        "type": "array",
        "items": {
            "type": "object",
            "properties": {
                "name": {"type": "string"},
                "price": {"type": "string"},
                "rating": {"type": "string"}
            }
        }
    }
)

for product in structured.data:
    print(f"{product['name']}: {product['price']}")

Verdict

Make.com is a solid no-code automation platform, but web scraping is not its strength. The lack of JavaScript rendering, proxy rotation, and scraping-specific features means you'll hit walls quickly if data extraction is a core part of your workflow.

SearchHive's ScrapeForge API handles the hard parts of web scraping -- rendering, proxies, retries, and parsing -- so you can focus on using the data rather than fighting to collect it. At scale, SearchHive costs a fraction of what Make.com charges for equivalent throughput, and the Python SDK makes integration straightforward.

For teams already invested in Make.com, the HTTP module works for light scraping tasks. But if you're building a data pipeline, monitoring competitor prices, or training ML models on web data, a dedicated scraping API is the better tool for the job.

Start scraping with SearchHive's free tier -- 500 credits, no credit card required. Check out the Python SDK docs to get started in under 5 minutes.

For a deeper look at no-code scraping tools, see /compare/makecom-vs-searchhive-for-web-scraping-full-comparison and /blog/n8n-web-scraping-workflows-automate-data-collection.