3 posts tagged with "web scraping apis"

From the outside, a scraping API looks simple. You send a URL. You get a page back.

Behind that one response, the Scrape.do team describes a stack of decisions, and several of them run against what most developers would assume.

The company reports that more than 99% of its requests work without a browser. Yet its engineers still open individual difficult targets and tune configurations for them by hand. And the failure they find hardest to catch is not the obvious block. It is the response that comes back successfully with part of the data missing.

That raises a question worth asking even if you only scrape a handful of sites: what does an operation handling 10 billion+ pages a month know about getting reliable data that still applies at your scale?

As Scrape.do's founder Batuhan Özyön put it during our recent Reddit AMA:

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At a small scale, scraping is mostly a technical problem. At our scale, it becomes a continuous systems and operations problem.

Batuhan has worked in web scraping and reverse engineering for many years and has been building Scrape.do since 2020. He was joined in the thread by Lead Software Engineer Mert B., R&D engineers Raif Tekin and Muhammet Derviş Aygan, and Selman Gokce, who leads marketing and SERP product marketing. The traffic volumes, percentages and internal systems described below are the team's own reported figures, not independent measurements.

A disclosure: ScrapeOps routes a significant amount of traffic through Scrape.do, and we know the team well. That is exactly why we wanted to get past the feature list and into the operation behind it.

In our tenth r/WebScrapingInsider AMA, we asked the Scrape.do team when browsers are actually necessary, how sessions and site-specific configurations decide the hard cases, and what it takes to keep data flowing after the first request succeeds.

Here are the nine biggest insights from the discussion.

Most proxy problems get diagnosed the same way.

A target starts blocking you, so you rotate the IP. Traffic slows down, so you buy more capacity. The response comes back 200, so you assume the scrape worked.

None of that holds up once a scraper runs at real, sustained volume.

In Geonode's AMA with r/WebScrapingInsider, the harder questions weren't really about whether "unlimited" proxies are good or bad. They exposed a system underneath that label: a pool can look healthy in aggregate while quietly failing against one target, the same IPs can behave differently once the client's browser identity changes, and a scraping API has to manage sessions and validate responses long after the proxy request itself succeeded.

The proxy turns out to be one variable inside a larger control system. The real problem is knowing which variable actually broke, and what to check next.

As Geonode's CEO and co-founder Jean-Patrick Bisson put it during our recent Reddit AMA:

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Reputation is per target, not one number, so an address burned on one retailer is fine everywhere else.

Jean-Patrick is CEO and co-founder of Geonode, a residential proxy and scraping API provider that operates its own network rather than reselling third-party supply. He answered questions in the thread alongside his team, posting under the company's account.

In our ninth r/WebScrapingInsider AMA, we asked Jean-Patrick how target-level reputation actually gets managed, what "unlimited" pricing depends on economically, how Geonode's scraping API handles sessions and browser identity, and where response validation stops being the provider's job.

Here are the six biggest insights from the discussion.

Every few weeks someone asks me the same question: which proxy provider is the best?

It is a reasonable question. Every comparison post on Google promises an answer. Every provider's homepage claims to be it.

And every developer who has burned a weekend switching providers wants a name they can stop thinking about.

There isn't one.

At ScrapeOps we currently route billions of requests a month through more than 50 proxy providers and scraping APIs. The same provider can be the cheapest, fastest option on one e-commerce site and one of the worst on another.

The winner can change between a product page and a search page on the same domain. It changes again when volume goes from 10,000 pages to 1 million.

What you are actually buying is not "a proxy". You are buying how efficiently a particular provider has learned to solve a particular target.

That efficiency is uneven, commercially motivated, and hidden behind headline prices that rarely survive contact with a real URL.

For our eighth r/WebScrapingInsider AMA I sat in the guest seat myself and opened up four years of provider benchmarking data to the community. If I had to compress the whole thread into two sentences, it would be these:

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There is no best proxy provider. There is only the best provider for your websites, workload and volume.

For context on where this comes from: I am the CEO and Co-Founder of ScrapeOps, a proxy aggregator that routes traffic across the proxy provider market and benchmarks providers against each other in production, across roughly 60,000 domains.

Before ScrapeOps I worked at ScraperAPI, one of the companies that established the modern web scraping API model. So I have seen this industry from both the provider side and the buyer side.

In the AMA, the community asked how to actually compare providers, why two providers can charge 20x apart for the same page, what success rate hides, and where the economics of anti-scraping are heading.

Here are the nine biggest insights from the discussion.