Web scraping can seem like a dark art to some – a mix of HTTP requests, parsers, browser automation, and anti-bot countermeasures. But for many developers, analysts, and companies, it’s just a very practical way to collect data from across the internet. On Reddit and similar forums, one of the most common questions is: “What do people use for web scraping?” The answers vary widely depending on the scale, complexity, and budget of the project.
In this blog, we’ll take a comprehensive look at how folks are scraping in 2025, what tools and services they lean on, and what trade‑offs come with each choice. This isn’t a sales pitch – more like a field guide.
1. The DIY Route: Building Your Own Scraper
One of the most common strategies is to write your own scraper using code. This gives you maximum flexibility, but also demands more effort. Here’s how people typically approach it.
1.1. Popular Libraries and Frameworks
BeautifulSoup + Requests
- BeautifulSoup is a widely used Python library for parsing HTML and XML. It’s easy to pick up, very readable, and ideal for smaller or simpler scraping tasks.
- Usually paired with requests (or a similar HTTP client), which handles fetching the webpage content.
Strengths:
- Low barrier to entry, especially for beginners.
- Lightweight, works well when the HTML structure is straightforward.
- Easy to tweak, inspect, and debug.
Limitations:
- Not ideal for large-scale scraping since requests are synchronous.
- Doesn’t handle JavaScript-heavy sites naturally; if the data only shows up after JS executes, you’ll need additional tools.
Scrapy
- Scrapy is a full-fledged Python web-crawling framework. It’s asynchronous, built for scalability, and has a modular architecture.
- You define “spiders” that crawl pages, extract data, follow links, and pipeline the results.
Strengths:
- Highly efficient for large projects due to concurrent requests.
- Organized project structure: pipelines, middlewares, item loaders, etc.
- Easy to scale, and integrate with data storage or other services.
Limitations:
- Steeper learning curve than BeautifulSoup.
- Doesn’t natively render JavaScript. If you need JS rendering, you often integrate with something like Splash, Selenium, or Puppeteer.
- Requires more boilerplate and setup.
Selenium (or Playwright, Puppeteer, etc.)
- Selenium automates real browsers (Chrome, Firefox, etc.). It’s great when you need to mimic a real user or deal with dynamic, JavaScript-heavy content.
- Alternatives like Playwright or Puppeteer are also common today, though Reddit discussions often mention Selenium due to its maturity and broad support.
Strengths:
- Can handle sites where content is loaded after page load, or where data only appears when you click or scroll.
- Very flexible: you can simulate user interactions (clicks, form submissions, hover, etc.)
Limitations:
- Resource-heavy – running real browser instances is expensive in CPU/memory.
- Slower than pure HTTP-based scraping.
- Maintenance overhead: browser drivers, version mismatches, headless detection, etc.
1.2. Real-World Developer Views (from Reddit)
Redditors often emphasize that there’s no one “best” tool: it depends heavily on the use case.
“There is no best, each has its use. Selenium is the most powerful, but … for a lot of cases it’s overkill. I tend to use BeautifulSoup for simple things, and Selenium for more complex stuff.”
“Once you know your way around the [Scrapy] framework you’ll write a lot less and more robust code … compared to requests+parser.”
These anecdotal insights align with how many practitioners actually build scrapers: choose the right tool for the problem, not just what seems “coolest.”
1.3. When DIY Makes Sense (and When It Doesn’t)
Good reasons to build your own scraper:
- You need very specific logic or data structure.
- You want full control over the crawling flow, retry logic, and error handling.
- You’re comfortable with programming and want to minimize third-party dependencies.
- You’re working on a one-off or internal project where manageability is more important than scale.
When DIY can become a pain:
- Handling anti-bot measures (CAPTCHAs, IP bans) can be a major headache.
- Managing proxies, rotations, and maintaining infrastructure (especially for scaling) is non-trivial.
- When data needs change often, maintaining scrapers can become a long-term burden.
2. Managed Web Scraping Services
Many people – especially non-technical users or teams without scraping expertise – turn to managed services or APIs to do the heavy lifting.
These services abstract away a lot of complexity: IP rotation, browser rendering, scheduling, and retry logic. You just tell them what you want, and they return structured data.
Here are some of the widely used ones, and how they compare.
2.1. Grepsr (as a Service, Not a Library)
- Grepsr is a full-service web scraping company. It’s not a library or open-source tool – they handle data extraction end-to-end. According to community discussions, Grepsr is used for lead generation, competitive intelligence, and other data collection needs.
- Pricing for Grepsr tends to start at a per‑source basis, which makes it more appealing for business use rather than DIY developers.
Pros:
- Minimal technical setup required.
- Scalable: professionals handle proxy management, data cleaning, scheduling.
- Good for business users who just want clean, ready-to-use data.
Cons:
- Less flexible than writing your own scraper.
- Recurring costs can become significant as volume scales.
- You’re dependent on the service provider for changes or maintenance.
2.2. Other Popular Scraping / API-Based Services
Zyte (formerly Scrapinghub)
- Zyte is one of the most recognizable names in scraping. They support Scrapy (their roots) and offer a hosted scraping API + cloud infrastructure.
- They provide features like auto‑extraction, proxy rotation, and AI-powered components to simplify data extraction.
Strengths:
- Deep integration with Scrapy; you can migrate existing scrapers easily.
- Robust infrastructure to handle large-scale crawling.
- Compliance focus: proxy pools, rate limiting, and structure to avoid IP bans.
Trade-offs:
- More expensive than DIY in the long run.
- Some lock-in: relying on their API means you must work within their constraints.
Bright Data (formerly Luminati)
- Known for its massive proxy network. Bright Data offers scraping APIs, proxy infrastructure, and enterprise-level features.
- Useful when you need to rotate across residential, mobile, or ISP proxies.
Strengths:
- High reliability on difficult-to-scrape sites.
- Large IP pool, which helps with avoiding detection or getting blocked.
Trade-offs:
- Premium pricing.
- Requires thoughtful configuration to avoid inefficient or wasteful usage.
ScrapingBee, ScraperAPI, Others
- ScrapingBee offers a scraping API with built-in headless browser support, proxy rotation and simple integration. It’s favored by smaller teams or individual developers.
- ScraperAPI is another widely used API that abstracts away proxies and anti-bot handling. It’s popular in developer communities for its simplicity and effectiveness.
Strengths (for API-style services):
- Very little maintenance required.
- Quick to integrate into existing systems (just call an endpoint).
- Often comes with good documentation and SDKs.
Trade-offs:
- Limited control: you rely on the provider to decide how to render pages, rotate IPs, and manage retries.
- Cost can get high if usage scales.
- You may hit rate or volume limits depending on your tier.
3. Hybrid Approaches: Best of Both Worlds
It’s common to see hybrid strategies that combine DIY scraping with managed services. Some real-world patterns:
- Build custom scrapers for core workflows, and offload occasional, tricky sites to a scraping API.
- Suppose you have a well-defined set of e-commerce sites you need to pull data from regularly: write Scrapy spiders for them.
- For ad-hoc or irregular sites (especially JavaScript-heavy or anti-bot protected), use a service like Grepsr or ScrapingBee.
- Suppose you have a well-defined set of e-commerce sites you need to pull data from regularly: write Scrapy spiders for them.
- Use managed services for proxies, but keep scraping logic in-house.
- You build the scraper (requests + BeautifulSoup or Scrapy), but route your traffic through a proxy API (e.g., Bright Data or Zyte) rather than managing proxy servers yourself.
- This reduces infrastructure headaches while preserving flexibility in your extraction logic.
- You build the scraper (requests + BeautifulSoup or Scrapy), but route your traffic through a proxy API (e.g., Bright Data or Zyte) rather than managing proxy servers yourself.
- Scheduler + infrastructure from a service, scraper code on your own servers.
- Scheduled scraping via a cloud provider (or via the service) but the parsing, cleaning, and post-processing remain in your codebase.
- This is effective when your data pipeline is complex and you want control over how data is processed or stored after extraction.
- Scheduled scraping via a cloud provider (or via the service) but the parsing, cleaning, and post-processing remain in your codebase.
4. Choosing the Right Approach: Decision Factors
Here are some key criteria that drive which tool or method people pick – based on themes that often come up in Reddit threads and professional discussions.
4.1. Scale of the Project
- Small / one-off scrapes → DIY with BeautifulSoup + requests is often sufficient.
- Large-scale, recurring scrapes → Scrapy or a managed service (or hybrid) is likely more sustainable.
- Massive scraping with many domains / IPs → Services like Bright Data or Zyte, or a custom infrastructure with distributed spiders + proxy management.
4.2. Technical Expertise
- Teams comfortable with Python and frameworks will learn DIY.
- Non-technical or lean teams often prefer API-based or managed services.
4.3. Anti‑Scraping Measures
- If the target sites are aggressive (CAPTCHAs, IP rate-limiting, bot detection), managed APIs or services shine because they often have built-in mechanisms to handle those.
- For more benign sites, custom scrapers may suffice, especially when you control headers, delays, and retry logic.
4.4. Maintenance Costs
- DIY scrapers: more upfront effort + ongoing maintenance when sites change.
- Services: less maintenance overhead, but recurring costs.
4.5. Data Ownership & Compliance
- With DIY, you own the entire pipeline.
- With third-party services, you need to trust the provider with potentially sensitive scraping tasks – ensure they comply with legal and ethical standards.
5. Risks, Ethics & Best Practices
Scraping is powerful, but not risk-free. Here are some lessons and best practices gathered from across the scraping community:
- Respect robots.txt (but don’t rely on it).
Some scrapers bypass robots.txt. While compliance is ethical, not all scrapers do – and some bots selectively ignore it. - Rate-limit your requests.
Be polite. Make sure your scraper doesn’t hammer a server too aggressively. Implement delays, exponential back-off, and parallelism responsibly. - Use rotating proxies.
To avoid IP bans, many scrapers leverage proxy pools. Whether you manage your own or use a provider, rotating IPs is often essential for scalability and reliability. - Monitor for changes.
Scraping is brittle. Websites change layouts, class names, or structure. Build monitoring for “failed scrapes” and unexpected results, and set up alerts. - Legal & compliance awareness.
Just because data is publicly accessible doesn’t mean it’s legal to scrape without restrictions. Know the terms of service and relevant laws (e.g., GDPR, data use policies). - Data quality & cleaning.
Raw scraped data is rarely clean. Deduplicate, validate, and normalize data after extraction to ensure it’s usable.
6. Future Trends in Web Scraping
Looking ahead, certain patterns are emerging in how people talk about and build scrapers:
- Headless browser APIs, like Playwright Cloud, are getting more use: they make JS rendering easier and more scalable.
- Serverless scraping is growing: running scraping jobs as serverless functions allows for cost-effective scaling.
- AI-driven data extraction: tools/services are using machine learning to identify and extract structured data from pages with minimal configuration.
- Ethical scraping practices are gaining more attention: as companies rely on large-scale scraping, compliance and data-use ethics are more central than ever.
Choosing Your Web Scraping Path
So – how are people doing web scraping these days? The short answer: it depends.
- Many developers still build custom scrapers using Python libraries like BeautifulSoup, Scrapy, or browser automation tools like Selenium – especially when they need full control, custom logic, or aren’t dealing with very large scales.
- But there’s a growing shift toward managed services and APIs (Grepsr, Zyte, Bright Data, ScrapingBee, etc.) for teams who want to skip infrastructure headaches and focus on the data.
- Hybrid models are also common: combining in-house scraping logic with service-based proxy infrastructure or JS rendering.
The right choice depends on your use case, scale, technical resources, and how much maintenance you’re willing to do. Whatever route you pick, it’s smart to think ahead: plan for IP rotation, error handling, and monitoring so your scraper doesn’t break or cause unintended strain.
