Your pricing agent runs perfectly in testing. You deploy globally. Twelve countries work fine. Three return mysterious session failures. At 3am you discover that what looked like authentication timeouts are actually how Singapore's CDN handles bot detection differently than Tokyo's. Same code, same provider, completely different regional behavior.

Fabien Vauchelles has spent a decade in this territory. As creator of Scrapoxy, an open-source proxy aggregator, and Anti-Ban Expert at Wiremind—a revenue management company processing millions of transportation prices daily—he's built infrastructure that makes a non-obvious truth visible: "global scraping" requires architectural thinking from the start.

What Scrapoxy's Architecture Reveals

Scrapoxy orchestrates proxies across multiple providers and regions, managing instance startup and shutdown to rotate IP addresses while displaying geographic coverage. Why build this complexity?

Regional web behavior shapes your architecture from day one, not something you patch after launch.

Scrapoxy's multi-provider architecture exposes what happens when you assume the web behaves consistently. A proxy that works reliably in Frankfurt starts throwing connection resets in Singapore—the CDN's bot detection calibrates differently for Southeast Asian traffic patterns. Selectors work fine. Authentication succeeds. But the regional CDN has decided you're suspicious based on TLS fingerprinting that varies by geography.

The support for multiple datacenter providers—AWS, Azure, GCP—addresses a production reality: global web automation requires regional infrastructure. Data residency requirements, regional CDN behavior, the need to deploy proxies where operations actually run.

At Wiremind, where Vauchelles processes millions of transportation prices daily, this complexity compounds. Geo-specific content makes selectors non-transferable across locales. An e-commerce site hides product listings in one region, reorders them in another. What looks stable in QA collapses under production load when regional rendering variations expose assumptions about page structure. Extraction logic either handles these variations or your dataset becomes corrupted by sampling bias toward regions that load fastest.

The TinyFish Recognition

We've encountered this operational reality building web agent infrastructure at scale. When we built systems for Google's Japan hotel inventory, "global" meant solving distinct regional problems: different CDN behaviors, localized rate limits, page structures that made selectors non-transferable across locales. Geographic distribution shapes your core architecture.

Vauchelles' approach—using parameters like country and OS type to create proxy diversity across providers—matches what you learn in production. You need regional infrastructure that matches regional complexity. Single-architecture approaches break because they assume consistency the web doesn't provide.

Vauchelles has spoken at 100+ conferences across 15 countries, carrying this message: scraping across borders requires infrastructure that treats each region as a distinct operational domain. Most teams learn this through production failures. Scrapoxy's architecture anticipates them.

The web outgrew the assumption that one architecture could handle all geographies. When your pricing agent fails at 3am because Singapore's CDN behaves differently than Tokyo's, you're discovering that "global" automation means solving 195 regional problems, each with its own infrastructure requirements. Scrapoxy's architecture reflects what you learn after that discovery.