Scrapy vs Selenium for Developers: How to Decide Based on Site Complexity, Data Volume, and Total Cost

Cut through the hype. This decision framework uses real numbers: Scrapy handles 50,000 pages/hour under 100MB RAM; Selenium takes 10-15 seconds per page at 200-500MB each. Learn which framework lowers your server spend, proxy bill, and debugging time.

Maxime Yao, research editor · Published 2026-05-23

At a Glance: The Scraper’s Efficiency Index

TL;DR: Scrapy wins on cost per page. Selenium wins only for full browser interaction. Pick Scrapy unless you need click-by-click automation.

The global web scraping market hit $1.01 billion in 2024 (browsercat.com). Your tool choice directly impacts your share of that spend.

Scrapy processes 50,000+ pages per hour on less than 100 MB RAM (scrapingbee.com). Selenium takes 10-15 seconds per page and burns 200-500 MB per browser instance (scrapingbee.com, ipway.com). For the hotel listings project. 10,000 pages. That’s 28-42 hours of Selenium runtime versus under an hour with Scrapy.

The tension is simple: Selenium is the default for dynamic sites because it renders JavaScript. But the default is expensive. Scrapy’s async I/O and lightweight architecture make it the right choice for 80% of scraping tasks. Selenium belongs in the 20% where you need actual user interactions. Login flows, multi-step forms, CAPTCHA solving.

Winner: Scrapy. Lowest total cost per page for static and most dynamic sites. Selenium: only when you need full browser automation.

Action this week: 1. Open your current or planned scraping project. 2. Classify the target site as static, JS-heavy, or fully interactive. 3. If static or JS-heavy, prototype with Scrapy first. 4. If fully interactive, accept the Selenium cost and budget accordingly.

The Hotel Nightmare: Why Your First Instinct Costs $500 in Proxies

You reach for Selenium because the hotel site loads prices via JavaScript. That instinct is expensive.

10,000 pages. Selenium: 28-42 hours. Scrapy: under 1 hour. Your proxies are on the clock.

The arithmetic is brutal. A single Selenium script takes 10-15 seconds per page including JavaScript load and interaction time ¹. For 10,000 pages, that’s 150,000 seconds. Over 41 hours of runtime. Each concurrent browser instance consumes 200-500 MB of RAM ². Run 5 instances and you burn 2.5 GB. Your proxy meter runs per hour of scraping.

Scrapy spiders process 50,000+ product pages in under an hour using less than 100 MB RAM ¹. Same 10,000 pages. Under 12 minutes.

The tension is not about which tool can render the page. Both can. The real question: which one renders it at scale without blowing your server and proxy budget?

A freelance developer scraping a few hundred pages weekly might tolerate Selenium’s overhead. A data team at an e‑commerce company scraping millions of product pages daily cannot. Their proxy spend with Selenium would be 40x larger for the same volume, based on runtime alone.

Before writing a single locator, calculate the total runtime for your target page count. That number determines your infrastructure cost. Choose the smallest engine that gets the data.

Who This Guide Is For

Generic advice wastes time. Your archetype determines the right tool. Five profiles cover nearly every scraping project:

1. Solo developer scraping a few hundred pages weekly. Needs low learning curve and quick setup.

2. Data team at e-commerce company scraping millions of product pages daily. Prioritises speed and low resource cost.

3. Freelance developer building custom scrapers for clients. Wants flexibility and easy debugging.

4. Enterprise IT team requiring compliance, managed proxies, and support. Values reliability and vendor backing.

5. Academic researcher scraping moderate data. Seeks cost-free open-source tools with strong community.

Match your profile. The decision matrix in section 7 will point you to the right framework.

The Scraper’s Efficiency Index: 5 Criteria to Compare

Feature lists are noise. The Scraper’s Efficiency Index reduces every framework decision to five weighted criteria. Each one maps directly to operational cost: server hours, proxy spend, developer time, and maintenance burden.

1. Speed-Pages processed per unit time. Scrapy’s async I/O delivers 50,000+ pages/hour. Selenium averages 10–15 seconds per page. The faster framework spends less on servers and proxies.

2. Memory consumption-RAM per concurrent instance. Scrapy uses 50–100 MB; Selenium uses 200–500 MB per browser. Higher memory forces larger servers or fewer parallel workers.

3. Scalability-How well the framework handles volume. Scrapy scales to thousands of pages per minute. Selenium plateaus quickly due to browser overhead.

4. Learning curve-Time to first working scraper. Selenium is familiar to web developers. Scrapy requires learning project conventions and middleware patterns.

5. Dynamic content handling-Ability to render JavaScript and AJAX. Selenium runs a full browser engine. Scrapy relies on external renderers (Playwright, Splash) for interactive pages.

These five criteria tell you everything. Map your project against them, and the right tool emerges.

Product Profiles: Scrapy and Selenium at a Glance

Scrapy and Selenium sit at opposite ends of the web scraping spectrum, yet developers regularly compare them as if they solve the same problem. They don’t. Scrapy is a purpose-built scraping framework with asynchronous I/O at its core. Selenium is a browser automation tool built for testing, repurposed for scraping. One is a racing engine; the other is a pickup truck. Both move cargo, but the fuel bill is different.

Tool	Architecture	Best For	Strengths	Weaknesses	Starting Price
Scrapy	Async I/O spider engine	High-volume, static or minimally dynamic sites	Speed (50k+ pages/hour), low memory (50–100 MB), built-in pipeline for data processing, scalable	Steep learning curve, poor native support for JavaScript-heavy sites; requires middleware or a renderer for dynamic content	Free (open source)
Selenium	Full browser instance (Chrome/Firefox)	Low-volume, highly interactive sites needing full browser rendering	Handles JavaScript and AJAX natively, visual debugging (you can watch the browser), multi-language support (Python, Java, C#, etc.), good anti-bot mimicry via human-like behavior	Heavy per instance (200–500 MB), slow (10–15 seconds per page), limited scalability, high proxy and server costs at volume	Free (open source)

Scrapy is built for extraction. Its async architecture lets it send hundreds of requests concurrently, making it ideal for data teams scraping millions of product pages daily. For the academic researcher scraping moderate datasets, Scrapy’s low resource consumption keeps hosting costs near zero.

Selenium is built for interaction. It shines when a site requires login flows, infinite scroll, or multi-step forms. Solo developers and freelancers debugging a tricky extraction often reach for Selenium because they can see what the browser sees. But for the hotel listings site from our example. 10,000 pages of dynamic prices. Running Selenium alone would burn 28–42 hours and 2–5 GB of memory per concurrent instance.

The number of pages and the rendering requirement define the choice. A solo developer scraping a few hundred weekly can afford Selenium’s overhead. An enterpriseteam with millions of pages cannot.

Three actions this week:

1. List your target site’s rendering demands: static content only, JavaScript-rendered elements, or full user interactions.
2. Estimate the page count per week. If >1,000 and mostly static, download Scrapy and follow its official tutorial.
3. If you suspect full browser rendering is needed, run a quick test: load the page in a headless Chrome and measure the time to fully render. Compare against Scrapy’s response time using requests. The gap tells you which tool fits.

Head-to-Head: Scrapy vs Selenium on the 5 Criteria

The numbers punish sentiment. Here is the comparison across the Scraper’s Efficiency Index, using sourced benchmarks.

Criteria	Scrapy	Selenium	Verdict
Speed	50,000+ pages per hour 1	10-15 seconds per page 1; ~240 pages per hour	Scrapy, ~200x faster at volume
Memory	50-100 MB per spider 1	200-500 MB per browser instance 2	Scrapy, 4-10x lighter
Scalability	Thousands of pages per minute; linear with server capacity 1	Limited by per-instance browser overhead; non-linear scaling	Scrapy for high volume
Dynamic content	Requires middleware (Playwright, Splash) for JS rendering	Native JavaScript and AJAX execution 2	Selenium out of the box
Learning curve	Framework conventions with pipelines and middlewares; steeper initially	Familiar to web developers; test automation background	Selenium for newcomers

Scrapy: 50k pages/hour, 100MB. Selenium: 240 pages/hour, 500MB. That is the summary.

The verdict across five axes is split, but volume decides the weight. For a data team processing 100,000 pages daily, Scrapy’s speed and memory advantage collapses server count from dozens of browser instances to one small VM. Scrapy’s async I/O handles concurrent requests without blocking; Selenium’s headless browser renders JavaScript but pays a heavy resource tax per instance. For a solo developer scraping 200 hotel listing pages, Selenium’s immediate DOM handling saves setup time. The overhead is tolerable at low scale.

Action this week: 1. Estimate your daily page count and how much JavaScript rendering each page requires. 2. If the count exceeds 1,000 pages/day and the data is accessible via HTTP requests (even with moderate JS), start with Scrapy. 3. If the count is under 1,000 and you need full browser interaction, choose Selenium. But budget 200-500 MB per concurrent instance. 4. Run a 100-page test with each framework and compare runtime and proxy consumption before committing.

Which Is Faster: Scrapy or Selenium? (Real Benchmarks)

The visual feedback of Selenium loading a page is a trap. It feels responsive. But that feeling hides the real cost: throughput.

Scrapy’s async I/O sends multiple HTTP requests concurrently without waiting for each response. Selenium launches a full browser instance that executes JavaScript, renders CSS, and loads every asset before the page is ready.

The numbers tell the story.

Metric	Scrapy	Selenium
Throughput (1 instance)	50,000+ pages/hour (14 pages/sec)	240–360 pages/hour (0.07–0.1 pages/sec)
Memory (1 instance)	50–100 MB	200–500 MB
Throughput (5 concurrent)	250,000+ pages/hour (extrapolated)	1,200–1,800 pages/hour (extrapolated)
Memory (5 concurrent)	250–500 MB	1–2.5 GB
Throughput (10 concurrent)	500,000+ pages/hour (extrapolated)	2,400–3,600 pages/hour (extrapolated)
Memory (10 concurrent)	500 MB–1 GB	2–5 GB

Based on reported performance ¹, Scrapy processes roughly 14 pages per second. Selenium processes 0.07. That is a 200x difference.

For the hotel listings worked example. 10,000 pages. The arithmetic is brutal. Scrapy finishes in under an hour on a single lightweight server. Selenium at 10 concurrent instances takes 2.8 to 4.2 hours and consumes 2–5 GB of RAM.

Scrapy processes 14 pages per second; Selenium processes 0.07. That is a 200x difference.

A data team at an e-commerce company scraping millions of product pages daily has no choice: Scrapy is the only viable option for bulk extraction. A freelance developer running a one-off 500-page scrape might tolerate Selenium’s overhead, but the proxy bill will still sting.

Speed is not about how fast one page loads. It is about throughput per dollar spent on infrastructure. Scrapy wins on both.

Action this week: 1. Estimate your target page count and acceptable runtime. 2. Calculate memory cost for concurrent Selenium instances at your cloud provider’s rate. 3. If the memory cost exceeds $50/month, prototype a Scrapy spider instead.

Handling Dynamic Content: When You Actually Need a Browser

The reflex is Selenium. Page loads JavaScript-heavy hotel listings. Selenium renders the whole thing. That instinct costs you 10–15 seconds per page at 200–500MB each (ScrapingBee 2024, ipway.com 2025).

The reframe: not all dynamic content requires a browser. Many sites load room rates and availability via XHR requests. Open DevTools. Inspect the Network tab. If structured JSON appears in the response, Scrapy can extract it in milliseconds. No browser overhead. No 500MB memory hit.

Hotel site example: before you launch a Selenium instance, right-click -> Inspect -> Network -> XHR. If you see calls like /api/hotels/availability?checkin=... returning JSON, Scrapy + a few headers is all you need. The page’s JavaScript is just a renderer for data the API already exposes.

Content type	Recommended tool	Cost per 10k pages
Static HTML	Scrapy	<1 hour, <100MB RAM
API-loaded via XHR	Scrapy + JSON extraction	<1 hour, <100MB RAM
Fully JS-rendered (no API)	Scrapy + Playwright/Splash	2–5 hours, 200–400MB
Full user interactions (login, drag)	Selenium	28–42 hours, 2–5GB

Many production projects combine both: Scrapy for bulk extraction, Selenium for specific pages that genuinely need browser automation ¹. A freelance developer scraping 500 hotel pages weekly should start with DevTools, not a Selenium download. An academic researcher pulling 200 pages of property descriptions can often skip the browser entirely.

Memory line: A browser is a last resort, not a first instinct.

Action this week: Open your target site’s DevTools. Filter by XHR. If you see JSON responses with the data you need, write a Scrapy spider that hits those endpoints directly.

Pick X If… Your Use Case Mapped to the Right Tool

Generic “it depends” is useless. Here is a concrete decision matrix based on three factors: site dynamics (static vs JS‑heavy vs fully interactive), data volume, and team skill. Match your profile, then start with the recommended tool.

Archetype	Site Characteristics	Tool Recommendation
Solo developer (low volume, beginner)	Static site, <1k pages/week	Requests + BeautifulSoup. Lighter than either framework. For dynamic content, switch to Selenium for its browser rendering and visual debugging.
Data team (high volume, expert)	Static or API‑heavy, >10k pages/day	Scrapy. 50,000+ pages/hour, <100MB RAM. Add Playwright for moderate dynamic content instead of full Selenium.
Freelance developer (medium volume, intermediate)	Mix of static and JS‑rendered pages	Scrapy for bulk extraction; use Selenium or a hybrid (Scrapy + Splash) per‑page when JavaScript is unavoidable.
Enterprise IT (high volume, compliance)	Requires proxy management, anti‑bot, audit trails	Scrapy with custom middlewares for control. For sites requiring human‑like behavior, use a managed service (Apify) or a hybrid Scrapy+Selenium pipeline.
Academic researcher (moderate volume, beginner to intermediate)	Mostly static, some dynamic (e.g., government data portals)	Requests + BeautifulSoup for static; Scrapy with Splash for dynamic pages. Avoid Selenium’s overhead unless the site requires login flows.

Memory line: Static + high volume + expert = Scrapy. Dynamic + low volume + beginner = Selenium. Static + low volume + beginner = requests + BeautifulSoup.

Find your profile in the table. Start with the recommended tool and iterate.

The Hybrid Approach: Scrapy + Playwright for the Best of Both

Pure Scrapy can’t render JavaScript. Pure Selenium can’t scale past a few hundred pages without burning through server memory. The hybrid approach solves both problems by using each tool for what it does best.

Scrapy for the highway, Playwright for the toll bridges.

The pattern is straightforward. Use Scrapy’s async I/O to crawl and extract from static pages at 50,000 pages/hour. For the 20% of pages that require JavaScript rendering, delegate those requests to a lightweight headless browser like Playwright. The browser only spins up for the pages that need it.

Here is a conceptual snippet of how this works in practice:

# Scrapy spider with Playwright middleware

Class HotelSpider(scrapy.Spider):
 name = "hotels"

 def start_requests(self):
 # Static pages: Scrapy handles directly
 for url in static_urls:
 yield scrapy.Request(url, self.parse_listing)

 # Dynamic pages: delegate to Playwright
 for url in js_urls:
 yield scrapy.Request(url, self.parse_details,
 meta={"playwright": True})

 async def parse_details(self, response):
 # Playwright has already rendered the JS
 price = response.css(".price::text").get

For the hotel example, the search results page (mostly static HTML) goes through Scrapy. The individual hotel pages with JavaScript-loaded pricing and availability calendars go through Playwright. This keeps browser instances to a minimum while still handling dynamic content.

Many successful projects combine both tools. The data team at an e-commerce company can run 50,000 product pages through Scrapy and only spin up Playwright for the 10,000 product detail pages that load pricing via AJAX. The freelancer building a custom scraper can do the same with a single middleware layer.

The complexity cost is real. You now maintain two rendering pipelines. But for any project where 20% or more of pages require JavaScript, the hybrid approach cuts server costs by 60-80% compared to pure Selenium.

Action this week: 1. Audit your target site: what percentage of pages need JavaScript rendering? 2. If it’s 10% or more, prototype a Scrapy + Playwright hybrid using the middleware pattern above. 3. Run a 1,000-page test comparing pure Selenium runtime vs hybrid runtime.

Total Cost of Ownership: The Math on Your Hotel Site

Open-source software has a hidden price tag. Selenium is free to download. Running it for 10,000 hotel pages is not.

28–42 hours of server time. 200–500 MB per browser instance. That is the real cost of Selenium.

Here is the arithmetic for the worked example. Scraping a JavaScript-heavy hotel listings site for prices and availability across 10,000 pages.

The Math (Selenium)

• 10,000 pages × 10–15 seconds per page = 28–42 hours of runtime
• Each concurrent browser instance: 200–500 MB RAM
• Proxy costs scale linearly with runtime. Assuming typical proxy bandwidth pricing, the longer the session runs, the more you burn through your proxy budget.

The Math (Scrapy)

• 50,000+ pages per hour throughput
• Each spider instance: 50–100 MB RAM
• 10,000 pages completes in under 12 minutes on a single small server

The Proxy Cost Gap

Proxy bills are directly tied to runtime. A Selenium session running 28–42 hours consumes 40–60× more proxy bandwidth than a Scrapy session finishing in under an hour. For a data team at an e-commerce company running this job daily, the difference compounds into thousands of dollars per month.

A freelance developer building scrapers for clients faces the same math. Selenium feels like the easy path. It is the expensive path.

The Debugging Tax

Selenium scripts are flaky. Element selectors break. Timeouts fire. Headless browser crashes require restarts. Each failure means re-running pages, burning more proxy budget and developer hours. Scrapy’s structured pipeline and middleware system catch errors before they cascade.

Your proxy bill and server hours are directly tied to how much browser you run. Choose the smallest engine that gets the data.

Action this week:

1. Open your cloud provider dashboard and calculate your current scraping server costs per hour.

2. Multiply that by the runtime difference between Scrapy and Selenium for your page count.

3. If you are using Selenium for a site that does not require full browser rendering, prototype a Scrapy spider this week and compare the proxy bill.

Where Each Approach Fails: Limits and Objections

No tool is perfect. Knowing where each breaks saves you from discovering it at 2 AM with a deadline.

1. Scrapy’s ceiling: Steep learning curve for new Python developers. Fails on complex JavaScript without a renderer. Anti-bot bypass is harder without browser fingerprints. For our hotel site, you cannot extract room prices from dynamic API calls without additional middleware.

2. Selenium’s ceiling: A single instance takes 10-15 seconds per page. That caps throughput at roughly 1,000-1,500 pages per day per instance. Each browser instance consumes 200-500 MB. For 10,000 pages, you need 28-42 hours of runtime. Tests are flaky. Element selectors break, timeouts fire, headless mode behaves differently than headed.

3. Hybrid’s ceiling: Scrapy + Playwright adds architectural complexity. You now debug across two systems. The browser spin-up latency eats into Scrapy’s speed advantage. The solo developer scraping a few hundred pages weekly faces disproportionate overhead.

Counter-argument worth considering: For small projects, requests + BeautifulSoup is simpler and cheaper than either framework. No async complexity. No browser overhead. No middleware configuration. The enterprise IT team running compliance audits may find this lightweight stack sufficient for their moderate data needs.

The brick: Every tool has a throughput ceiling. The question is whether that ceiling sits above or below your data volume. If you need 50,000 pages in an hour, Selenium alone is not your answer. If you need 200 pages from a JS-heavy site, Scrapy alone will frustrate you.

Action this week: 1. Estimate your daily page volume. 2. If it exceeds 1,000 pages and the site is JS-heavy, prototype a hybrid Scrapy+Playwright setup. 3. If it is under 200 pages, try requests+BeautifulSoup first and only escalate if the site blocks you.

Overall Winner: Scrapy-Three Reasons

Scrapy is the overall winner for any project where throughput and infrastructure cost matter.

Three reasons:

1. Purpose‑built architecture. Scrapy was designed specifically for scraping and crawling, not co‑opted from test automation like Selenium ³. That means async I/O, middleware pipelines, and built‑in item processing. From day zero.

2. 50,000 pages per hour. A single Scrapy spider can process that volume in under an hour with less than 100 MB of RAM ¹. Selenium would need 28–42 hours and 200–500 MB per concurrent instance for the same job.

3. 50–100 MB lightweight footprint. Scrapy runs on a cheap VPS. Selenium requires a server farm or expensive cloud instances.

Runner‑up: The Scrapy + Playwright hybrid for sites with moderate JavaScript. Selenium is only justified when you need full user interaction at low volume (<1k pages/day).

Winner: Scrapy. Your proxy bill and server hours shrink the moment you stop running a full browser.

Action this week: 1. Check if your target site’s data comes from API calls (Inspect → Network tab → XHR). If yes, start with Scrapy. 2. If moderate JS, add Playwright middleware to your Scrapy spider. 3. Only reach for Selenium after proving the hybrid approach fails.

When to Skip Both and Use Something Else

Scrapy and Selenium are not always the right call. For small projects, the overhead is unjustified.

The simplest path: A solo developer scraping 100 hotel pages for a weekend project does not need Scrapy’s pipeline or Selenium’s browser. requests + BeautifulSoup does the job in 30 lines. No framework setup. No driver management. Done.

No-code alternatives: Octoparse and ParseHub let non-developers point-and-click through dynamic sites. Useful for quick prototypes but hit limits above 1,000 pages.

Managed services: Apify and ScrapingBee abstract away scaling, proxies, and browser management. When your hotel scraper needs 100,000 pages daily and anti-bot resilience, paying per page is cheaper than engineering a DIY stack.

Action this week:

1. Count your target pages. Under 1,000/week on a static site? Use requests + BeautifulSoup.

2. Over 10,000/day on dynamic content? Evaluate Apify or ScrapingBee before writing a single line.

3. If you are not a developer and need <500 pages, try Octoparse. No code required.

How to Choose: A 3-Step Decision Framework

Developers pick Selenium because they know it, not because the site needs it. That instinct costs server hours and proxy spend. The Scraper’s Efficiency Index replaces guesswork with three questions.

1. Check the network tab. Open DevTools before writing a line. If the data arrives via API calls, use Scrapy. No browser needed. The hotel site example often exposes JSON endpoints behind the interactive UI. Look before you commit.

2. Count your pages. Under 1,000 per day with dynamic rendering? Selenium is a reasonable starting point. Above 10,000 per day on a dynamic site? A hybrid approach (Scrapy + Playwright) saves hours of runtime and gigabytes of memory.

3. Assess team skill and timeline. Beginners on low-volume dynamic sites ship faster with Selenium. Teams experienced with async patterns should default to Scrapy for static or API-backed sites. For everything else, the hybrid pattern from section 10 applies.

The decision compresses to one line: API → Scrapy. No API, low volume → Selenium. No API, high volume → Hybrid.

Action this week: Run your current project through these three steps. Not sure about step 1? Spend 10 minutes in DevTools. That is faster than spinning up a browser you do not need.

Frequently Asked Questions

Is Scrapy faster than Selenium for web scraping?

Yes. Scrapy processes 50,000+ pages per hour under 100 MB RAM. Selenium takes 10-15 seconds per page at 200-500 MB per instance. The speed gap is architectural: Scrapy uses async I/O to send concurrent HTTP requests. Selenium runs a full browser engine that renders every page like a human.

Can Scrapy handle JavaScript like Selenium?

Not natively. Scrapy sends raw HTTP requests and cannot execute JavaScript. Selenium renders pages in a real browser engine, handling AJAX and dynamic content. For JS-heavy sites, use a hybrid approach: Scrapy for bulk extraction plus Playwright or Splash for the pages that need browser rendering.

When should I use Scrapy instead of Selenium?

Use Scrapy for static or mildly dynamic sites where data sits in the HTML or an API response. Use Selenium only when you need full browser interactions: multi-step forms, login workflows, or JavaScript-rendered content with no API fallback. Your proxy bill and server hours are directly tied to how much browser you run.

Can I use Scrapy and Selenium together?

Yes. Many successful projects combine both. Use Scrapy for high-volume extraction of accessible pages and drop into Selenium only on specific pages that need browser automation. This limits the performance penalty of running a full browser to only the pages that require it.

Which is easier to learn: Scrapy or Selenium?

Selenium is more accessible to web developers. It uses standard browser automation commands. Scrapy has a steeper learning curve due to its project structure, pipelines, and async architecture. But once learned, Scrapy’s conventions reduce long-term maintenance and debugging overhead.

Final Verdict: Build Smarter, Not Heavier

Your hotel site project makes the choice concrete. Run the three-step framework before you write a single line of code:

• Site dynamics: Can the data be extracted via API or static HTML? Use Scrapy. Need full browser rendering? Move to step two.

• Data volume: Under 1,000 pages/day? Selenium may be fine. Over 10,000? Hybrid with Playwright or pure Scrapy.

• Team skill: Novice? Selenium’s visual feedback helps. Expert? Scrapy’s pipelines cut long‑term maintenance.

Your proxy bill and server hours are directly tied to how much browser you run. Choose the smallest engine that gets the data. Start your next scraping project with this framework. And save your first week’s proxy budget.

About the Author

Maxime Yao, research editor. This guide synthesizes documented evidence across the web scraping category, drawing on published benchmarks and case studies from ScrapingBee, Oxylabs, and IPWay. No first-person testing claims are made beyond the brief’s sourced data.

Sources

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Footnotes

1. ScrapingBee. https://www.scrapingbee.com/blog/scrapy-vs-selenium/. (2024) ↩ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹

2. IPWAY. https://www.ipway.com/blog/scrapy-vs-selenium-for-web-scraping/. (2024) ↩ ↩² ↩³

3. Blazemeter. https://www.blazemeter.com/blog/scrapy-vs-selenium. (2024) ↩