Why Building a Web Scraper is Harder Than You Think

You want to scrape some data from the web. How hard can it be? A few HTTP requests, parse some HTML, done.

Then reality hits. Your IP gets blocked after 100 requests. Cloudflare throws a challenge page. The site requires login. Your "simple scraper" needs infrastructure you didn't plan for.

This post breaks down what production web scraping actually requires. Not an ad for any service - just an honest look at the problem space.

1. The Proxy Problem

The moment you send more than a handful of requests to any serious website, you'll get blocked. Your IP address is your identity, and websites track it.

The IP Hierarchy

Not all proxies are created equal. There's a clear hierarchy:

Proxy Type	Description	Detection Risk	Cost
Data Center IPs	IPs from cloud providers (AWS, GCP, etc.)	Very High	$
Residential IPs	IPs from real ISP customers	Medium	$
ISP/Static Residential	Static IPs from ISPs, residential-like	Low	$$
Mobile IPs	IPs from cellular networks (4G/5G)	Very Low	$$

Data Center IPs are cheap but essentially useless for any serious scraping. Websites maintain blocklists of known data center IP ranges. One request and you're flagged.

Residential IPs come from real home internet connections. They look like normal users. The catch: they're shared across many scrapers, rotate frequently, and quality varies wildly.

ISP/Static Residential are the sweet spot for many use cases. Static, legitimate residential addresses. Expensive but reliable.

Mobile IPs are the gold standard. Cellular carriers use CGNAT (Carrier-Grade NAT), meaning thousands of legitimate users share the same IP. Blocking a mobile IP means blocking real customers. Sites are extremely hesitant to do this.

The Pool Problem

You don't just need one proxy - you need thousands. A proxy pool with:

• Geographic distribution (many sites geo-restrict)
• Rotation logic (avoid hitting same IP twice in a row)
• Health monitoring (dead proxies need removal)
• Session stickiness (some sites require consistent IP during a session)

Building this yourself means:

• Sourcing proxy providers (multiple, for redundancy)
• Building rotation infrastructure
• Monitoring and replacing dead proxies
• Handling authentication and bandwidth billing

Most teams underestimate this. It's not a one-time setup - it's ongoing operations.

2. The Anti-Bot Arms Race

Having good proxies is just the entry ticket. Modern websites deploy sophisticated anti-bot systems.

Cloudflare and Friends

Cloudflare, Akamai, PerimeterX, DataDome - these services protect millions of websites. They analyze:

• TLS fingerprints: Your HTTP client's TLS handshake reveals what software you're using
• JavaScript challenges: Browser automation? They can tell
• Behavioral analysis: Mouse movements, scroll patterns, timing
• Cookie/session tracking: Inconsistent sessions get flagged
• Request patterns: Too fast? Too regular? Blocked

CAPTCHA Hell

When behavioral detection fails, CAPTCHAs appear:

• reCAPTCHA v2: The classic checkbox
• reCAPTCHA v3: Invisible, score-based
• hCaptcha: reCAPTCHA alternative
• Custom challenges: Image selection, puzzle solving

Solving CAPTCHAs at scale requires either:

• Human solving services (slow, expensive)
• Machine learning models (unreliable, arms race)
• Avoiding detection in the first place (best option)

The Unblocker Concept

This is where "unblocker" services come in. They handle:

• TLS fingerprint spoofing
• JavaScript rendering
• CAPTCHA solving
• Cookie management
• Retry logic with different configurations

You send a URL, they return the content. Simple API, complex infrastructure behind it.

3. Hosted Scrapers: The Lambda Model

Even with proxies and anti-bot bypass solved, you still need:

• Servers to run your scraper code
• Job queuing and scheduling
• Result storage
• Error handling and retries
• Monitoring and alerting

The Traditional Approach

Run EC2 instances, deploy your scraper, manage a queue (SQS, Redis), store results in PostgreSQL or S3. You're now maintaining:

• Server infrastructure
• Database operations
• Job orchestration
• Scraper code updates
• Scaling logic

The Hosted Alternative

Services like Brightdata, Apify, and others offer a "serverless scraper" model:

1. Write your scraper logic (JavaScript/Python)
2. Upload to their platform
3. Trigger on-demand or schedule
4. Results delivered to your storage

Similar to AWS Lambda - you write the function, they handle execution. Benefits:

• No infrastructure to maintain
• Pay per execution
• Built-in proxy integration
• Automatic retries and error handling

Trade-off: vendor lock-in and less control over execution environment.

4. The Login Problem

Public data is one thing. But many valuable data sources require authentication:

• Social media (Facebook, X/Twitter, LinkedIn)
• E-commerce (order history, pricing after login)
• SaaS platforms (dashboards, reports)

Why Login Scraping is Hard

1. Session management: Maintain cookies across requests
2. 2FA/MFA: Many sites require additional verification
3. Rate limiting per account: Even with valid login, aggressive scraping gets accounts banned
4. Terms of Service: Often explicitly prohibits automation

The Dataset Approach

Some scraping services maintain pre-crawled datasets. For example, Brightdata claims 45M+ records for Facebook data.

How does this work? A few possibilities:

• They've already crawled with authenticated accounts
• They aggregate data from multiple sources
• They use APIs where available (official or unofficial)

When you query their "Facebook dataset," you're not scraping Facebook in real-time. You're querying their cached database.

This raises questions:

• How fresh is the data?
• What's the coverage?
• Is this compliant with the source site's ToS?

For well-known sites, this approach often makes more sense than building custom scrapers. The data already exists - why reinvent the wheel?

The Edge Case: Login-Required Non-Famous Sites

Here's where things get complicated. If you need to scrape a niche B2B platform or internal tool that:

• Requires login
• Has no pre-existing datasets
• Has custom anti-bot measures

You're back to building custom infrastructure:

• Secure credential management
• Session handling
• Custom anti-detection logic
• Monitoring for account bans

No SaaS solution covers every site. For truly custom needs, you still need engineering work.

5. Compliance: The Invisible Constraint

Technical capability isn't everything. Legal and ethical constraints matter.

What You Can't Scrape

Most scraping services explicitly exclude certain sites:

• Apple.com (strict legal enforcement)
• Government sites (legal restrictions)
• Sites with explicit scraping bans in ToS
• Personal data without consent (GDPR/CCPA)

Brightdata, for example, maintains a blocklist. Try to scrape apple.com and you'll get rejected.

The Risk Calculus

Even if technically possible, consider:

• Legal risk: CFAA in the US, similar laws elsewhere
• Business risk: Cease and desist letters, lawsuits
• Ethical risk: Scraping personal data at scale has consequences

The hiQ vs LinkedIn case established some legal precedent for scraping public data, but the law remains murky. When in doubt, consult legal counsel.

6. Build vs Buy: The Real Calculus

So should you build scraping infrastructure or buy from a SaaS provider?

Build When:

• You have a small, specific target (one or two sites)
• Sites have minimal anti-bot protection
• You need full control over execution
• Cost sensitivity at very high scale
• Compliance requires owning the infrastructure

Buy When:

• You're targeting multiple sites
• Sites have sophisticated anti-bot measures
• You need to move fast
• Infrastructure isn't your core competency
• Pre-existing datasets cover your needs

The Hybrid Approach

Many teams end up with a hybrid:

• SaaS for difficult sites (Cloudflare-protected, login-required)
• Custom scrapers for simple targets
• Pre-crawled datasets for common data sources

7. Conclusion

Web scraping looks simple until you try to do it at scale. The real infrastructure includes:

Layer	DIY Complexity	SaaS Solution
Proxy Pool	High - sourcing, rotation, monitoring	Included
Anti-Bot Bypass	Very High - TLS, JS, CAPTCHAs	Unblocker API
Execution Environment	Medium - servers, queues, storage	Hosted Scraper
Login Handling	High - sessions, 2FA, account management	Datasets (partial)
Compliance	Ongoing - legal review, blocklists	Built-in restrictions

The complexity multiplies across layers. Each solved problem reveals the next.

SaaS scraping services exist because this problem is genuinely hard. They've invested years in infrastructure most teams can't justify building. Whether their pricing makes sense depends on your scale and needs.

For most teams, the answer isn't "build everything" or "buy everything" - it's understanding which layers need custom solutions and which can be outsourced.

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The web scraping landscape keeps evolving. Anti-bot systems get smarter. Scrapers adapt. The arms race continues. What works today may not work tomorrow - factor that into your build vs buy decision.