ScraperAPI vs Scrapingbee vs Scrapingdog: Which Scraping Tool Actually Works?

You're sitting there with a Python script that needs to pull data from Amazon, Walmart, eBay, or run some Google searches. Simple enough, right? Until the blocks start rolling in. IP bans. Captchas. Rate limits. Suddenly your "simple scraping project" turns into a full-time job managing proxies and rotating user agents.

This is where scraping APIs come in—they handle the messy stuff so you can focus on actually using the data. But here's the thing: ScraperAPI, Scrapingbee, and Scrapingdog all claim they'll solve your problems. They can't all be equally good though. So which one actually delivers when you're scraping real websites at scale?

The Real Test: Speed, Success Rate, and Actual Performance

Let's cut through the marketing talk. I ran a straightforward test—10 requests across different scenarios: Amazon product pages, Walmart listings, eBay items, Glassdoor data, and Google search results. The kind of stuff people actually scrape in the real world.

Here's what I tracked:

• Response time per request (because nobody wants to wait 30 seconds for a single page)

• Success rate (if it can't get past the anti-bot measures, what's the point?)

• Consistency across different site types (some APIs work great on e-commerce but choke on job boards)

The code structure was simple—random selection from URL lists, timed requests, error tracking. Nothing fancy, just real-world conditions.

What Actually Happened

Response Times: You'd think they'd all be similar, but no. Some APIs averaged under 3 seconds per request. Others? We're talking 8-10 seconds, sometimes timing out completely. When you're scraping thousands of pages, those seconds add up fast.

Success Rates: This is where things got interesting. One service pulled a 90%+ success rate across all site types. Another couldn't crack 70% on protected sites like Glassdoor. The third sat somewhere in the middle but had weird inconsistencies—great on Amazon, terrible on Google SERPs.

Actual Usability: Beyond the raw numbers, there's the "does this actually work" factor. How often did I need to retry? Did the responses come back clean or full of bot-detection pages? Could it handle different international Amazon domains without choking?

Why Most Scraping Projects Fail (And How These Tools Try to Fix It)

Here's the uncomfortable truth: most DIY scraping setups break within days. You start confident, maybe grab some free proxies, write decent code. Then Amazon updates their bot detection. Walmart changes their HTML structure. Your IP gets blacklisted. Suddenly you're spending more time debugging than actually building.

The three big killers:

1. IP rotation headaches – Free proxies are garbage. Paid ones require constant management.

2. Captcha walls – Modern sites throw challenges faster than you can solve them manually.

3. Maintenance nightmares – Websites change. Your scraper breaks. Repeat weekly.

Premium scraping APIs promise to handle all this automatically. They maintain proxy pools, solve captchas, handle JavaScript rendering, and adapt when sites change their defenses. The question is whether they actually deliver on that promise consistently.

If you're tired of managing these issues yourself, 👉 tools that handle proxy rotation, captcha solving, and JavaScript rendering automatically can save you dozens of hours per month. But only if they actually work reliably.

Breaking Down the Three Contenders

ScraperAPI: The Straightforward Workhorse

What stood out: It just works. Simple API structure, excellent documentation, handles most common scraping scenarios without fuss. The success rate stayed consistently high across e-commerce sites, search engines, and job boards.

The pricing makes sense for scaling—you pay for successful requests, not failed attempts. Their smart proxy rotation actually seems smart, not just random IP switching. JavaScript rendering works smoothly, which matters when you're scraping modern single-page applications.

Best for: Projects where reliability matters more than rock-bottom pricing. If failed requests cost you time or money, the success rate pays for itself.

Scrapingbee: The Feature-Rich Option

Scrapingbee throws in a lot of extras—screenshot capture, webhook support, cloud integrations. Great if you need those specific features. The JavaScript rendering is solid, maybe even slightly better than competitors for highly interactive sites.

The catch? Pricing gets complicated fast, and the response times were noticeably slower in testing. When you're already using premium credits for JS rendering, those extra seconds per request translate to higher costs at scale.

Best for: Specific use cases where you need advanced features like screenshots or webhook callbacks, and speed isn't your primary concern.

Scrapingdog: The Budget-Conscious Choice

Scrapingdog positions itself as the affordable option. The pricing is definitely attractive if you're just starting out or running smaller projects. Basic scraping works fine for less-protected sites.

Where it struggled: Success rates dropped on tougher targets. Glassdoor, Google SERPs with lots of protections, international Amazon domains—these gave it trouble. Response times were inconsistent, sometimes fast, sometimes timing out.

Best for: Learning projects, smaller scraping tasks on easier targets, or situations where you can handle some failed requests in exchange for lower costs.

The Real Cost of "Cheap" Scraping

Here's something nobody talks about enough: failed requests waste more than just API credits.

Let's say you're scraping 10,000 product pages. At a 70% success rate, you're missing 3,000 products. Now you need to:

• Identify which requests failed

• Rerun them (often multiple times)

• Handle partial datasets in your analysis

• Deal with delays in your data pipeline

At a 95% success rate? You're mostly dealing with normal internet hiccups and can move forward confidently.

The math changes dramatically at scale. A service that's 20% more expensive but has 90%+ success rates often costs less overall than cheap options with 70% success rates. You're paying for fewer retries, less debugging time, and cleaner datasets.

What Your Code Actually Needs

Forget the marketing for a second. What does a production scraping setup actually require?

1. Consistent success rates above 90% – Anything less means constant babysitting and retry logic.

2. Response times under 5 seconds – Especially if you're scraping thousands of pages. Slow responses kill throughput.

3. Clean error handling – When requests fail, you need clear feedback on why. "Request failed" isn't helpful. "Blocked by Cloudflare, auto-retry in progress" is.

4. Minimal code changes – Your scraping logic should stay simple. If you're spending hours tweaking parameters for each site, the API isn't doing its job.

5. Predictable costs – Scraping budgets need to be plannable. Hidden fees for JavaScript rendering or premium features make budgeting impossible.

The test code I showed earlier? That structure works great when the API handles the complexity behind the scenes. When it doesn't, you end up adding hundreds of lines of error handling, retry logic, and proxy management—exactly what you paid to avoid.

Which One Should You Actually Use?

After running real tests and dealing with actual scraping challenges, here's my take:

Go with ScraperAPI if: You're running serious scraping operations where downtime costs money. The combination of high success rates, consistent performance, and straightforward pricing makes it the reliable choice for production environments. It handles tough targets like Glassdoor and international sites better than alternatives.

Consider Scrapingbee if: You specifically need advanced features like screenshots, webhook callbacks, or extensive cloud integrations. Just be prepared for higher costs and slightly slower response times.

Try Scrapingdog if: You're experimenting, learning, or scraping easier targets where some failures are acceptable. Good for getting started, less ideal for scaling up.

The Bottom Line

Here's what matters: can you trust the tool to work when you need it?

Scraping isn't just about getting data occasionally—it's about building reliable data pipelines that run daily, weekly, handling thousands of requests without constant supervision. Failed requests don't just cost API credits; they cost your time, delay projects, and introduce gaps in your datasets.

The best scraping API is the one that lets you write straightforward code like the example above and trust that it'll work consistently. No clever workarounds. No constant tweaking. Just reliable data collection that scales with your needs.

After testing all three with real-world scenarios, 👉 ScraperAPI delivered the most consistent results across different site types and complexity levels, which is exactly what you need when scraping moves from side project to critical business function. Sometimes paying a bit more for reliability is the smartest budget decision you'll make.