Best Web Scraping Tools in 2026

Web scraping in 2026 requires multiple tools working together. There's no single "best" tool because different tools solve different problems in the scraping pipeline.

Most "best web scraping tools" articles make a critical mistake: they compare incompatible tools. Comparing Beautiful Soup to Scrapfly is like comparing a JSON parser to AWS they operate at completely different layers of the stack.

This guide organizes tools by what problem they actually solve. If you're evaluating tools for production scraping, understanding the pipeline is essential.

Quick Answer for the Impatient:

• Simple scraping: Scrapfly (fetch) + BeautifulSoup/Cheerio (parse)
• Large-scale crawling: Scrapy (orchestrate) + Scrapfly middleware (fetch)
• Complex interactions: Playwright (control browser) + proxies (avoid detection)

Now let's break down why these combinations work and how to choose the right tools for your use case.

Understanding the Web Scraping Pipeline

The scraping pipeline has four distinct stages. Most real-world projects use 2-3 different tools, each handling a specific stage:

Each stage requires different evaluation criteria. API services are measured by anti-bot success and cost per request. Parsers are measured by speed and selector support. Frameworks are measured by scheduling efficiency and middleware quality.

Comparing tools across categories produces meaningless results. "Which is better: Scrapfly or Beautiful Soup?" is like asking "Which is better: Postgres or React?" They don't compete they complement.

The Four Pipeline Stages

Stage 1: Fetch/Unblock/Render
Getting HTML from websites while handling anti-bot protection (Cloudflare, DataDome, PerimeterX) and rendering JavaScript for SPAs. This is where most projects fail.

Tools: Scrapfly (managed API), or DIY with Playwright + residential proxies + stealth plugins

Stage 2: Parse
Extracting structured data from HTML using CSS selectors or XPath. Parsers don't fetch anything. They process HTML you already have.

Tools: BeautifulSoup (Python), Cheerio (Node.js), lxml (Python)

Stage 3: Orchestrate
Managing crawl queues, retries, rate limiting, and data pipelines across thousands of URLs.

Tools: Scrapy (Python framework), custom code

Stage 4: Store/Export
Saving extracted data to JSON, CSV, databases, or APIs.

Tools: Usually handled by your framework or custom code

Let's examine the best tools for each stage, starting with the hardest: fetching protected pages.

Stage 1: Fetching & Unblocking Tools

Getting past modern anti-bot systems is where most scraping projects die. Protected sites analyze TLS fingerprints, browser fingerprints, behavioral patterns, and IP reputation. A single mismatch triggers a block.

Developers consistently underestimate how complex this has become.

Scrapfly - Managed Fetch/Unblock/Render API

Scrapfly is an HTTP API that handles the entire Stage 1 pipeline: fetching pages, bypassing anti-bot systems, and rendering JavaScript. You send a URL; you get back HTML. No proxy management, no anti-bot research, no browser fingerprinting.

Where It Fits:

Your Code → Scrapfly API → HTML → Your Parser (BeautifulSoup/Cheerio)

Scrapfly replaces the infrastructure you'd otherwise build yourself: proxy rotation, browser pool management, CAPTCHA solving, anti-bot evasion, and rendering engine maintenance.

Key Features

• Anti-bot bypass: Handles Cloudflare, DataDome, PerimeterX, Kasada automatically via ASP (Anti-Scraping Protection)
• JavaScript rendering: Headless browsers included, no need to manage browser pools
• Proxy rotation: Residential and datacenter IPs across 100+ countries
• Session management: Cookies persist across requests for login flows
• Debugging tools: Screenshots, HAR files, request logs
• SDKs: Python, Node.js, Go

API Service Evaluation Metrics

Code Example - Python

from scrapfly import ScrapflyClient, ScrapeConfig

client = ScrapflyClient(key='YOUR_API_KEY')

# Scrapfly handles: proxies, anti-bot bypass, JS rendering
result = client.scrape(ScrapeConfig(
    url='https://protected-site.com/products',
    asp=True,  # Anti-Scraping Protection
    render_js=True,
    country='US'
))

# You get clean HTML back - parse it with your preferred library
html = result.content

# Parse with BeautifulSoup
from bs4 import BeautifulSoup
soup = BeautifulSoup(html, 'lxml')
products = soup.select('div.product')

for product in products:
    title = product.select_one('h2').text
    price = product.select_one('.price').text
    print(f"{title}: {price}")

When to Use Scrapfly

Scrapfly is built for production scraping where reliability matters. It excels at handling sites with bot detection (Cloudflare, DataDome, PerimeterX, Kasada), eliminating the need to build and maintain your own proxy infrastructure. The SLA-backed uptime and automatic anti-bot updates mean you can focus on extracting data instead of fighting detection systems.

The platform is particularly valuable when time-to-market is critical. Instead of spending weeks building anti-bot infrastructure, you can start scraping protected sites immediately. Teams without dedicated anti-bot expertise can achieve results that would otherwise require months of research and development.

Pricing Considerations

Scrapfly uses consumption-based pricing:

• Free tier: 1,000 API credits for testing
• Paid plans: Start at $30/month for 200,000 credits (Discovery), up to $500/month for 5.5M credits (Enterprise)
• Credits consumed: Varies by features (ASP, rendering, session length)

For production use, estimate costs based on:

• Simple fetch (no ASP, no JS): ~1 credit per request
• Anti-bot bypass (ASP enabled): ~5-10 credits per request
• JS rendering + ASP: ~10-20 credits per request

Compare this to DIY costs:

• Residential proxies: $5-15 per GB
• Server infrastructure: $50-500/month
• Development time: Weeks to months
• Maintenance: Ongoing as anti-bot systems evolve

For most teams, managed services are cheaper when you factor in engineering time.

DIY Alternative: Playwright + Proxies

When to Build Your Own:

• You have specific requirements that APIs can't handle
• Extremely high volume makes infrastructure economics favorable
• You need full control over browser behavior
• Your team has anti-bot expertise

What You'll Need:

• Playwright or Puppeteer for browser control
• Residential proxy service ($5-15/GB)
• Stealth plugins (playwright-stealth, puppeteer-extra-plugin-stealth)
• CAPTCHA solving service (optional, $1-3 per 1,000 solves)
• Server infrastructure for browser pool

Realistic DIY Stack:

from playwright.sync_api import sync_playwright
from playwright_stealth import stealth_sync

# You'll need to configure proxies separately
PROXY_URL = "http://user:pass@proxy-provider.com:port"

with sync_playwright() as p:
    browser = p.chromium.launch(
        headless=True,
        proxy={"server": PROXY_URL}
    )

    page = browser.new_page()
    stealth_sync(page)  # Apply stealth evasion

    page.goto('https://protected-site.com/products')
    page.wait_for_selector('.product')

    html = page.content()
    # Now parse with BeautifulSoup/Cheerio
    browser.close()

This example is simplified. Production DIY scraping requires proxy rotation logic, browser fingerprint randomization beyond stealth plugins, CAPTCHA detection and solving, rate limiting, retry strategies, and infrastructure scaling.

Building this properly takes weeks to months. Maintaining it as anti-bot systems evolve is ongoing work.

Stage 2: HTML Parsing Tools

Once you have HTML (from Scrapfly, requests, or a browser), you need to extract data. Parsers process HTML you already have. They don't fetch anything.

Parsers have no "success rate." They parse whatever HTML you give them. Success depends on Stage 1 (fetching).

BeautifulSoup - Python HTML Parser

BeautifulSoup parses HTML and provides a Pythonic API for data extraction using CSS selectors, tree navigation, or search methods. It doesn't fetch pages. Pair it with Scrapfly or requests for that.

Parser Evaluation Metrics:

Code Example - With Scrapfly

from scrapfly import ScrapflyClient, ScrapeConfig
from bs4 import BeautifulSoup

client = ScrapflyClient(key='YOUR_API_KEY')

# Stage 1: Scrapfly fetches and handles anti-bot
result = client.scrape(ScrapeConfig(
    url='https://protected-site.com/products',
    asp=True
))

# Stage 2: BeautifulSoup parses
soup = BeautifulSoup(result.content, 'lxml')  # Use lxml backend for speed

# Extract data using CSS selectors
products = soup.select('div.product')

for product in products:
    title = product.select_one('h2.title').text.strip()
    price = product.select_one('span.price').text.strip()
    url = product.select_one('a')['href']

    # Handle missing data gracefully
    rating = product.select_one('span.rating')
    rating_value = rating.text.strip() if rating else 'N/A'

    print(f"{title}: {price} ({rating_value})")

When to Use BeautifulSoup

BeautifulSoup is great for Python projects, especially if you're learning web scraping (gentle learning curve) or dealing with malformed HTML. The flexible navigation (parent/sibling traversal) is handy when CSS selectors alone won't cut it.

If speed is critical (lxml is 2-3x faster), you're comfortable with XPath, and the HTML is well-formed, use lxml directly instead.

Always use BeautifulSoup(html, 'lxml') for production. The lxml backend is significantly faster than html.parser and handles most real-world HTML correctly.

Cheerio - Node.js HTML Parser

Cheerio brings jQuery-like syntax to Node.js for HTML parsing. Fast and memory-efficient because it doesn't run a browser. It's pure DOM parsing.

Parser Evaluation Metrics:

Code Example - With Scrapfly

const { ScrapflyClient } = require('scrapfly-sdk');
const cheerio = require('cheerio');

const client = new ScrapflyClient({ key: 'YOUR_API_KEY' });

async function scrapeProducts() {
    // Stage 1: Scrapfly fetches
    const result = await client.scrape({
        url: 'https://protected-site.com/products',
        asp: true
    });

    // Stage 2: Cheerio parses
    const $ = cheerio.load(result.content);

    $('.product').each((i, el) => {
        const title = $(el).find('h2.title').text().trim();
        const price = $(el).find('span.price').text().trim();
        const url = $(el).find('a').attr('href');

        console.log(`${title}: ${price}`);
    });
}

scrapeProducts();

When to Use Cheerio

Cheerio is ideal for Node.js/TypeScript projects, especially high-volume parsing where speed and memory efficiency matter. If you're familiar with jQuery syntax, you'll be productive immediately. It's significantly faster than running a headless browser. If you only need to parse HTML (not interact with the page), Cheerio + Scrapfly's rendering is much more efficient than Puppeteer.

Stage 3: Browser Automation Tools

Sometimes you need to control a browser directly for complex interactions, login flows with 2FA, or when you're building your own Stage 1 infrastructure. These tools control browsers; they don't bypass anti-bot protection.

Critical Distinction:
Playwright/Puppeteer/Selenium are browser controllers, not unblockers. For protected sites, you need to add proxies + stealth plugins, or use Scrapfly instead.

Playwright - Modern Browser Automation

Playwright controls real browsers (Chromium, Firefox, WebKit) programmatically. Use it when you need custom browser logic that an API can't handle.

Browser Automation Evaluation Metrics:

When to Use Playwright vs. Scrapfly

Use Playwright for custom login flows with 2FA or CAPTCHA, complex multi-step interactions (filling forms, clicking modals), specific mouse/keyboard event sequences, intercepting network requests for API discovery, or building your own Stage 1 infrastructure.

Use Scrapfly for standard page fetching (even with JavaScript rendering), sites with anti-bot protection, or production systems where reliability matters and you don't want to manage browser infrastructure.

If Scrapfly's render_js + js parameter can handle it, use Scrapfly. If you need custom interactions Scrapfly can't do, use Playwright. For protected sites with custom interactions, you're looking at Playwright + proxies + stealth (complex), or contact Scrapfly support about custom scenarios.

Code Example - Complex Interaction

from playwright.sync_api import sync_playwright

def scrape_with_login():
    with sync_playwright() as p:
        browser = p.chromium.launch(headless=True)
        page = browser.new_page()

        # Navigate and handle login
        page.goto('https://example.com/login')
        page.fill('#username', 'user@example.com')
        page.fill('#password', 'secure_password')
        page.click('button[type="submit"]')

        # Wait for navigation
        page.wait_for_selector('.dashboard')

        # Navigate to target page
        page.goto('https://example.com/protected-data')
        page.wait_for_selector('.data-table')

        # Extract data
        html = page.content()
        browser.close()

        # Parse with BeautifulSoup
        from bs4 import BeautifulSoup
        soup = BeautifulSoup(html, 'lxml')
        return soup.select('.data-row')

This example works on unprotected sites. For protected sites, Playwright alone gets blocked. You'd need residential proxy rotation, playwright-stealth plugin, browser fingerprint randomization, and behavioral humanization (random delays, mouse movements).

Or just use Scrapfly with session support for login flows.

Puppeteer - Node.js Headless Chrome

Google's library for controlling Chrome/Chromium. Node.js only, Chrome only.

Browser Automation Evaluation Metrics:

When to Use Puppeteer

Puppeteer makes sense for Node.js projects when you need Chrome-specific features, have existing Puppeteer infrastructure, or want to use puppeteer-extra plugins.

Playwright is generally better for new projects: multi-browser support, better auto-waiting, more languages, actively developed by Microsoft. Puppeteer is still excellent if you're already invested in the Node.js ecosystem and only need Chrome.

Selenium - Cross-Language Browser Automation

The original browser automation framework. Supports most languages and browsers.

Browser Automation Evaluation Metrics:

When to Use Selenium

Selenium shines in polyglot environments where multi-language teams need a common framework, or when you already have Selenium infrastructure. Selenium Grid is useful for distributed scraping, especially when QA testing and scraping share infrastructure.

For new Python or Node.js projects, Playwright is usually the better choice. Selenium is older and heavier. But Selenium remains valuable in polyglot environments and for teams invested in Grid.

Stage 4: Orchestration & Crawling

For large-scale scraping (thousands of URLs), you need orchestration: scheduling, retries, rate limiting, data pipelines.

Scrapy - Python Crawling Framework

Scrapy orchestrates crawling workflows. It handles URL queues, concurrency, retries, throttling, and data pipelines. It doesn't handle anti-bot protection. You pair it with Scrapfly for that.

Framework Evaluation Metrics:

Scrapy + Scrapfly Integration

The power combination for large-scale scraping: Scrapy handles crawl logic, Scrapfly handles fetching protected pages.

# settings.py
DOWNLOADER_MIDDLEWARES = {
    'scrapy_scrapfly.ScrapflyMiddleware': 725,
}

SCRAPFLY_API_KEY = 'YOUR_API_KEY'
SCRAPFLY_ASP = True  # Enable anti-bot protection globally
SCRAPFLY_RENDER_JS = True

# spider.py
import scrapy

class ProductSpider(scrapy.Spider):
    name = 'products'
    start_urls = ['https://protected-site.com/products']

    def parse(self, response):
        # Scrapy handles: crawl queue, retries, throttling
        # Scrapfly handled: fetching, anti-bot, JS rendering

        for product in response.css('div.product'):
            yield {
                'title': product.css('h2::text').get(),
                'price': product.css('.price::text').get(),
                'url': product.css('a::attr(href)').get(),
            }

        # Follow pagination - Scrapy manages the queue
        next_page = response.css('a.next::attr(href)').get()
        if next_page:
            yield response.follow(next_page, self.parse)

When to Use Scrapy

Scrapy is essential for large-scale crawling (1,000+ URLs) where you need scheduling, retries, rate limiting, and complex data pipelines. Works best with Python teams who have framework experience.

Skip it for simple scraping (< 100 URLs), if your team is unfamiliar with Scrapy's architecture, or if you're using Node.js (custom code with Cheerio works better).

The Scrapy + Scrapfly combination is powerful: Scrapy handles orchestration at scale, Scrapfly handles anti-bot and reliability. You don't need to manage proxy rotation in Scrapy, and retry logic works seamlessly (Scrapfly retries internally).

AI-Powered Extraction

Scrapfly AI Extraction

Scrapfly integrates AI-powered data extraction directly into the Web Scraping API, combining fetching and extraction in a single API call. Three extraction methods handle different use cases: precise template-based extraction, natural language LLM prompts, and automatic AI models for common structures.

Key Advantage: Cache + Re-extraction

Scrapfly caches the raw HTML from your scrapes, allowing you to re-extract data with different methods or schemas at much faster speed and lower cost. Scrape once, experiment with extraction multiple times without hitting the target site again.

Method 1: Extraction Templates

Define custom extraction rules using CSS selectors, XPath, and regex for exact, deterministic data extraction. Templates are JSON-based and support nested extraction, formatters, and type casting.

Use for:

• Structured data where precision matters (e-commerce prices, inventory)
• Production systems requiring consistent, reproducible results
• Complex nested data structures
• Any scenario where CSS/XPath works

Evaluation Metrics:

Example - Product Data Extraction:

from scrapfly import ScrapflyClient, ScrapeConfig

client = ScrapflyClient(key='YOUR_API_KEY')

# Define extraction template as JSON
template = {
    "source": "html",
    "selectors": [
        {
            "name": "title",
            "query": "h1.product-title::text",
            "type": "css"
        },
        {
            "name": "price",
            "query": "span.product-price::text",
            "type": "css",
            "extractor": {"name": "price"}
        },
        {
            "name": "reviews",
            "query": "#reviews > div.review",
            "type": "css",
            "multiple": true,
            "nested": [
                {
                    "name": "rating",
                    "query": "count(//svg)",
                    "type": "xpath",
                    "cast": "float"
                },
                {
                    "name": "text",
                    "query": "//p[1]/text()",
                    "type": "xpath"
                }
            ]
        }
    ]
}

# Scrape + extract in one API call
result = client.scrape(ScrapeConfig(
    url='https://example.com/product',
    asp=True,
    extraction_template=template
))

# Access extracted data
data = result.extraction
print(data['title'])
print(data['price'])
print(data['reviews'])

Method 2: LLM Extraction

Use natural language prompts to extract data. Describe what you want in plain English, and the LLM handles the extraction. Works with HTML, JSON, XML, markdown, and RSS.

Use for:

• Sentiment analysis or content summarization
• Flexible schemas where exact structure varies
• Quick prototyping before writing templates
• Content extraction (articles, blog posts)

Evaluation Metrics:

Example - Sentiment Analysis:

from scrapfly import ScrapflyClient, ScrapeConfig

client = ScrapflyClient(key='YOUR_API_KEY')

result = client.scrape(ScrapeConfig(
    url='https://example.com/reviews',
    asp=True,
    extraction_prompt="What is the general sentiment about this product? List the top 3 complaints."
))

# LLM response in extracted_data
sentiment = result.extraction
print(sentiment)

Method 3: AI Automatic Extraction

Pre-trained models extract common web structures (products, reviews, articles, listings) automatically without any configuration. The AI finds all relevant fields for the selected model.

Available Models:

• product - Product pages (title, price, images, variants, specs)
• review - Review pages (rating, text, author, date)
• article - Articles/blog posts (title, author, content, date)
• listing - List pages (items, pagination)

Use for:

• Standard e-commerce product pages
• Review sites
• News articles and blog posts
• Any common web structure

Evaluation Metrics:

Example - Product Auto-Extraction:

from scrapfly import ScrapflyClient, ScrapeConfig

client = ScrapflyClient(key='YOUR_API_KEY')

result = client.scrape(ScrapeConfig(
    url='https://example.com/product',
    asp=True,
    extraction_model='product'
))

# Automatically extracted product data
product = result.extraction
print(product)
# Returns: title, price, images, description, variants, etc.

Cache + Re-extraction Workflow

The cache feature unlocks powerful workflows:

1. Scrape once: cache=True stores the HTML
2. Experiment with extraction: Try different templates, prompts, or models
3. Re-extract from cache: Much faster, lower cost (no re-scraping)

# Initial scrape with cache
result = client.scrape(ScrapeConfig(
    url='https://example.com/product',
    asp=True,
    cache=True  # Cache the HTML
))

# Later: extract with template (uses cached HTML)
result = client.scrape(ScrapeConfig(
    url='https://example.com/product',
    cache=True,
    extraction_template=my_template
))

# Try different extraction without re-scraping
result = client.scrape(ScrapeConfig(
    url='https://example.com/product',
    cache=True,
    extraction_model='product'
))

Production Recommendation

For production scraping:

• Start with templates for structured data (predictable, lowest cost)
• Use auto models for standard structures (product pages, reviews)
• Use LLM extraction for flexible content or exploratory analysis
• Combine with cache to iterate on extraction rules without re-scraping

All three methods are production-ready and deterministic enough for reliable data extraction at scale.

Production Use Case Stacks

Instead of generic "best for" recommendations, here are actual production stacks with cost and complexity estimates:

E-Commerce Price Monitoring

Challenge: Sites use aggressive anti-bot protection (Cloudflare, DataDome)

Recommended Stack:

Scrapfly (fetch + extract with AI)
  →
PostgreSQL (store price history)

Or if you prefer manual parsing:

Scrapfly (fetch protected pages)
  →
BeautifulSoup (parse product data)
  →
PostgreSQL (store price history)

Why this works:

• Scrapfly handles anti-bot protection automatically
• Use Scrapfly's extraction templates or auto-extraction models for consistent, production-ready parsing
• Or pair with BeautifulSoup if you prefer manual CSS selector control
• Simple architecture, easy to maintain

Estimated costs:

• Scrapfly: $30-250/month (Discovery to Startup tier, depending on volume)
• Infrastructure: $10-50/month (database, app server)
• Total: ~$40-300/month

Complexity: Low (weekend project to production)

Large-Scale Crawling (10k+ URLs/day)

Challenge: Need orchestration, scheduling, retries at scale

Recommended Stack:

Scrapy (orchestrate crawls)
  →
Scrapfly middleware (fetch protected pages)
  →
Scrapy pipelines (process & store data)
  →
Database/Data warehouse

Why this works:

• Scrapy handles crawl orchestration efficiently
• Scrapfly handles anti-bot without proxy management
• Scrapy's middleware system integrates seamlessly

Estimated costs:

• Scrapfly: $250-500+/month (Startup to Enterprise tier)
• Infrastructure: $50-200/month (servers, database)
• Total: ~$300-700/month

Complexity: Medium (requires Scrapy expertise)

Custom Login Flows with 2FA

Challenge: Complex authentication requiring browser interactions

Recommended Stack:

Playwright (handle login + 2FA)
  →
Session cookies → Scrapfly (subsequent requests)
  →
BeautifulSoup (parse data)

Or:

Playwright + residential proxies (full DIY)
  →
Cheerio (parse data)

Why this works:

• Playwright handles complex auth flows
• Once authenticated, use Scrapfly for reliable data fetching
• Or go full DIY if you have the expertise

Estimated costs:

• Playwright + Scrapfly: $100-250/month (Pro to Startup tier)
• Full DIY: $100-400/month (proxies + infrastructure)

Complexity: High (custom browser logic + anti-bot evasion)

FAQs

What's the difference between Scrapfly and Scrapy?

Scrapfly is an API service that fetches pages and bypasses anti-bot protection. Scrapy is a Python framework that orchestrates crawls at scale. They're complementary. Use Scrapfly alone for simple scraping or combine them with Scrapy's middleware for large-scale crawling. Scrapy doesn't handle anti-bot protection, and Scrapfly doesn't manage crawl orchestration.

Do I need BeautifulSoup if I use Scrapfly?

Usually yes. Scrapfly returns HTML, so you still need a parser like BeautifulSoup to extract data. However, if you use Scrapfly's extraction API (templates, LLM, or auto models), the parsing is handled for you and you get structured data directly.

Can Playwright bypass anti-bot protection?

Not by itself. Playwright controls browsers but doesn't include anti-bot evasion. Modern anti-bot systems detect browser fingerprints, TLS fingerprints, and behavioral patterns. To bypass protection with Playwright, you need residential proxies, stealth plugins, fingerprint randomization, and behavioral humanization. Scrapfly handles all of this automatically.

Scrapfly vs. DIY proxies + Playwright?

Scrapfly costs $30-500/month depending on volume (Discovery to Enterprise tier), takes hours to implement, requires zero maintenance, and offers SLA-backed reliability. DIY costs $100-1,000/month for proxies plus infrastructure, takes weeks to months to build, needs ongoing maintenance as anti-bot systems evolve, and reliability depends on your team's expertise. For under 1M requests/month, Scrapfly is almost always cheaper when factoring in engineering time. Above 10M requests/month, DIY might be cost-effective if you have an experienced team. Scrapfly wins on time-to-market and reliability; DIY wins on control and customization.

Is AI extraction reliable for production?

Yes, when using Scrapfly's extraction templates or auto models. Extraction templates are completely deterministic using CSS/XPath: same template always produces the same output, perfect for structured data like e-commerce prices or financial data. Auto models are highly consistent for common structures like products, reviews, and articles. LLM extraction is less deterministic but works well for flexible content extraction, sentiment analysis, and summarization. For high-volume extraction, templates are most cost-effective at 1 credit per request. All three methods are production-ready; choose based on your reproducibility requirements and budget.

Summary

Modern web scraping requires combining tools across different pipeline stages. Scrapfly handles fetching and bypassing anti-bot protection, BeautifulSoup (Python) or Cheerio (Node.js) parse the HTML, and Scrapy orchestrates large-scale crawls. For data extraction, Scrapfly's built-in AI extraction (templates, LLM, or auto models) can replace manual parsing entirely.

The simplest production stack is Scrapfly for fetching plus its extraction API for parsing: everything in one API call. Add Scrapy for orchestration when you scale beyond 1,000 URLs. DIY alternatives exist (Playwright + proxies), but managed services win on time-to-market and reliability for most teams.