Supacrawler vs Firecrawl

Both Supacrawler and Firecrawl offer web scraping APIs designed for LLM and AI applications, but they differ in pricing, features, and performance. Here's a comprehensive comparison.

Pricing Comparison

Monthly costs for equivalent functionality:

Usage Level	Supacrawler	Firecrawl	Savings
3K requests		$19
5K requests	$15		27%+
100K requests	$65	$99	34%
500K requests	$285	$399	29%

API Comparison

Basic Web Scraping

Firecrawl Approach:

from firecrawl import FirecrawlApp

def scrape_with_firecrawl(url):
    app = FirecrawlApp(api_key="fc-YOUR_API_KEY")
    
    # Scrape a single URL
    scrape_result = app.scrape_url(
        url=url,
        params={
            'pageOptions': {
                'onlyMainContent': True,
                'includeHtml': False,
                'screenshot': False
            },
            'extractorOptions': {
                'mode': 'llm-extraction'
            }
        }
    )
    
    if scrape_result['success']:
        return {
            'content': scrape_result['data']['markdown'],
            'title': scrape_result['data']['metadata'].get('title'),
            'success': True
        }
    else:
        return {
            'error': scrape_result.get('error', 'Unknown error'),
            'success': False
        }

Supacrawler Approach:

from supacrawler import SupacrawlerClient

client = SupacrawlerClient(api_key="your-api-key")

def scrape_with_supacrawler(url):
    result = client.scrape(url, format="markdown", render_js=True)
    return {
        'content': result.markdown,
        'title': result.metadata.title if result.metadata else None,
        'success': True
    }

Website Crawling

Firecrawl Approach:

from firecrawl import FirecrawlApp
import time

def crawl_with_firecrawl(start_url, max_pages=10):
    app = FirecrawlApp(api_key="fc-YOUR_API_KEY")
    
    # Start crawl job
    crawl_result = app.crawl_url(
        url=start_url,
        params={
            'crawlerOptions': {
                'includes': [],
                'excludes': [],
                'maxDepth': 2,
                'limit': max_pages
            },
            'pageOptions': {
                'onlyMainContent': True,
                'includeHtml': False
            }
        },
        wait_until_done=True,
        timeout=300
    )
    
    if crawl_result['success']:
        return [
            {
                'url': item['metadata']['sourceURL'],
                'title': item['metadata'].get('title'),
                'content': item['markdown']
            }
            for item in crawl_result['data']
        ]
    else:
        return {'error': crawl_result.get('error'), 'success': False}

Supacrawler Approach:

from supacrawler import SupacrawlerClient

client = SupacrawlerClient(api_key="your-api-key")

def crawl_with_supacrawler(start_url, max_pages=10):
    # Single API call handles everything
    job = client.create_crawl_job(
        url=start_url,
        link_limit=max_pages,
        depth=2,
        format="markdown"
    )
    
    # Wait for completion
    result = client.wait_for_crawl(job.job_id)
    
    return [
        {
            'url': url,
            'title': data.get('metadata', {}).get('title'),
            'content': data.get('markdown')
        }
        for url, data in result.data.crawl_data.items()
    ]

Feature Comparison

Feature	Supacrawler	Firecrawl
Output Formats	HTML, Markdown, JSON	Markdown, JSON
JavaScript Rendering	Advanced (5s+ wait)	Basic (2s max)
LLM Extraction	AI-powered parsing	LLM-based extraction
Screenshot Capture	Full screenshots API	Basic screenshots
Content Monitoring	Watch API	Not available
Proxy Rotation	Automatic	Basic
Error Handling	Comprehensive retries	Basic error responses
Rate Limits	Generous	Strict limits
Documentation	Interactive examples	Basic API docs

Performance Benchmarks

Crawling 100 Documentation Pages:

Metric	Supacrawler	Firecrawl
Average Response	1.2s	2.8s
Success Rate	98.9%	92.4%

LLM Integration

RAG Pipeline Integration

Firecrawl Approach:

from firecrawl import FirecrawlApp
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

def build_rag_with_firecrawl(urls):
    app = FirecrawlApp(api_key="fc-YOUR_API_KEY")
    
    # Scrape all URLs
    documents = []
    for url in urls:
        result = app.scrape_url(
            url=url,
            params={
                'pageOptions': {
                    'onlyMainContent': True
                },
                'extractorOptions': {
                    'mode': 'llm-extraction'
                }
            }
        )
        
        if result['success']:
            documents.append({
                'content': result['data']['markdown'],
                'metadata': {'source': url}
            })
    
    # Split and embed
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200
    )
    
    splits = text_splitter.split_documents(documents)
    embeddings = OpenAIEmbeddings()
    vectorstore = Chroma.from_documents(splits, embeddings)
    
    return vectorstore

Supacrawler Approach:

from supacrawler import SupacrawlerClient
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

def build_rag_with_supacrawler(start_url):
    client = SupacrawlerClient(api_key="your-api-key")
    
    # Crawl entire site in one call
    job = client.create_crawl_job(
        url=start_url,
        link_limit=100,
        depth=3,
        format="markdown"
    )
    
    result = client.wait_for_crawl(job.job_id)
    
    # Convert to documents
    documents = [
        {
            'content': data['markdown'],
            'metadata': {'source': url}
        }
        for url, data in result.data.crawl_data.items()
        if data.get('markdown')
    ]
    
    # Split and embed (same as above)
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200
    )
    
    splits = text_splitter.split_documents(documents)
    embeddings = OpenAIEmbeddings()
    vectorstore = Chroma.from_documents(splits, embeddings)
    
    return vectorstore

Content Quality Comparison

Documentation Extraction

Input URL: https://docs.example.com/getting-started

Firecrawl Output:

# Getting Started

This is the main content extracted from the page.

Some navigation elements might still be present.

Footer content may be included.

## Installation
...

Supacrawler Output:

# Getting Started

Clean, focused content with perfect extraction.

Navigation and footers automatically removed.

## Installation

Step-by-step installation guide with proper formatting.

### Prerequisites
- Node.js 16+
- npm or yarn

### Install the package
```bash
npm install example-package

Usage

Complete usage examples with proper code blocks.


## Error Handling

**Firecrawl Error Response:**
```json
{
  "success": false,
  "error": "Failed to crawl the URL"
}

Supacrawler Error Response:

{
  "success": false,
  "url": "https://example.com",
  "error": "Navigation timeout exceeded",
  "metadata": {
    "status_code": 500,
    "retry_count": 3,
    "error_type": "timeout"
  }
}

Advanced Features

Custom Data Extraction

Firecrawl Approach:

from firecrawl import FirecrawlApp

def extract_structured_data_firecrawl(url):
    app = FirecrawlApp(api_key="fc-YOUR_API_KEY")
    
    extraction_result = app.scrape_url(
        url=url,
        params={
            'extractorOptions': {
                'mode': 'llm-extraction',
                'extractionPrompt': 'Extract product name, price, and description',
                'extractionSchema': {
                    'type': 'object',
                    'properties': {
                        'product_name': {'type': 'string'},
                        'price': {'type': 'string'},
                        'description': {'type': 'string'}
                    }
                }
            }
        }
    )
    
    return extraction_result

Supacrawler Approach:

from supacrawler import SupacrawlerClient
import google.generativeai as genai

client = SupacrawlerClient(api_key="your-api-key")
genai.configure(api_key="your-gemini-key")

def extract_structured_data_supacrawler(url):
    # Scrape with Supacrawler
    result = client.scrape(url, format="markdown")
    
    # Extract with Gemini
    model = genai.GenerativeModel('gemini-pro')
    prompt = f"""
    Extract product information from this content:
    {result.markdown}
    
    Return JSON with: product_name, price, description
    """
    
    response = model.generate_content(prompt)
    return json.loads(response.text)

Cost-Benefit Analysis

For 25,000 monthly scrapes:

Provider	Base Cost	LLM Extraction	Screenshots	Monitoring	Total
Firecrawl	$199	Included	+$49	N/A	$248
Supacrawler	$89	DIY	Included	Included	$89
Savings	-	-	-	-	64%

When to Choose What

Choose Firecrawl When:

LLM-First workflow: Need built-in LLM extraction
Simple scraping needs: Basic content extraction only
Existing integration: Already using successfully

Choose Supacrawler When:

Cost efficiency matters: Need 65%+ cost savings
Comprehensive solution: Scraping + screenshots + monitoring
Better performance: Faster response times
Advanced features: Watch API, crawling, better error handling

Migration Guide

From Firecrawl to Supacrawler:

# Before (Firecrawl)
from firecrawl import FirecrawlApp

def old_scraper(url):
    app = FirecrawlApp(api_key="fc-YOUR_API_KEY")
    result = app.scrape_url(url, params={'pageOptions': {'onlyMainContent': True}})
    return result['data']['markdown']

# After (Supacrawler)
from supacrawler import SupacrawlerClient

client = SupacrawlerClient(api_key="your-key")

def new_scraper(url):
    result = client.scrape(url, format="markdown")
    return result.markdown

Getting Started

Ready to save 65%+ on your scraping costs?

Compare directly: Firecrawl to Supacrawler calculator
Start free: Get your API key
Migrate easily: Migration guide

Continue with Firecrawl?

Check our Firecrawl integration examples
See hybrid approach strategies

Summary: While Firecrawl offers built-in LLM extraction, Supacrawler provides better performance, more features, and 65% cost savings with flexible AI integration options.