How to Choose Infrastructure for Large-Scale Web Research

How to Choose Infrastructure for Large-Scale Web Research

A scraper that pulls 500 pages a day runs beautifully on a laptop and a cron job. Push the same script to 500,000 pages and it collapses, usually inside the first hour.

The failure almost never comes from parsing logic. It comes from the plumbing: how requests get routed, how often they repeat, and what the target server sees arriving on the other end.

Getting those decisions right at the start saves weeks of rework.

Profile the Target Before Buying Anything

Infrastructure requirements come from the sites being studied, not from a vendor's feature comparison chart. A government statistics portal and a sneaker retailer sit at opposite ends of the difficulty scale, and they need completely different setups.

So the first job is reconnaissance. Pull 200 pages manually, log every response code, and watch for the point where challenges start appearing. That figure (requests per hour before friction) is the most useful number in the entire project.

Teams that skip this step overbuy. They end up paying premium rates for routing that a static documentation site would never have challenged in the first place.

IP Origin Drives Everything Downstream

Once difficulty is mapped, the routing layer becomes the main decision. Datacenter IPs originate from hosting companies like AWS, DigitalOcean, and Hetzner, and detection vendors maintain published range lists for all of them.

Residential IPs come from real consumer connections verified by an ISP, which is why they clear checks that datacenter ranges fail outright. They cost more, and they're usually billed per gigabyte rather than per IP. Teams weighing that trade-off can discover IPRoyal, your residential proxy provider, then compare pool coverage against what the target sites actually demand.

But residential routing isn't automatically the right call. Datacenter proxies still win decisively on speed and price for public records, open API endpoints, and anything that doesn't run bot detection at all.

Rate Limits Are the Real Constraint

Every serious site caps how many requests a single source can make inside a time window. Cloudflare's breakdown of rate limiting describes the mechanic plainly: exceed the threshold and traffic gets blocked, challenged, or throttled.

The fix is boring and it works. Start at one request per second per IP, read the response headers, and cut speed by half the moment a 429 shows up.

Concurrency is the other lever, and it gets conflated with speed constantly. Fifty threads against one domain hits the same ceiling as a single thread running fifty times faster.

And rotation timing matters far more than raw pool size. An IP firing 300 requests in 60 seconds looks nothing like a person, no matter how residential its origin.

Build the Compliance Layer First, Not Last

Legal review belongs in the design phase. Robots.txt directives were formally standardized in RFC 9309, and while the spec is explicit that these rules aren't a form of access authorization, ignoring them is difficult to defend afterward.

What's actually permissible shifts by jurisdiction. The web scraping case record in the US and EU includes rulings that landed on both sides, and collecting anything resembling personal data pulls GDPR obligations into scope.

Log the details: which URLs were fetched, at what time, from which region, under what terms of service. Auditability costs almost nothing upfront and is close to impossible to retrofit.

Storage, Orchestration, and the Parts Nobody Budgets For

Request volume gets all the attention, but storage design usually breaks first. A crawl producing 40 million HTML documents needs a plan for raw retention, parsed output, and deduplication, and object storage handles that far better than a Postgres table someone spun up on day one.

Queue architecture deserves the same scrutiny. Celery, Sidekiq, and Temporal all handle retry logic, though the important question is what happens to a job that fails at 3 a.m. on request 2.4 million.

Monitoring closes the loop. Track block rate per domain, average latency per region, and cost per thousand successful responses, because those three numbers reveal a degrading pipeline long before the dataset looks obviously wrong.

Where This Goes Next

Detection keeps improving, and the gap between a naive scraper and a properly provisioned research pipeline widens every year. Sites now score behavioral signals: TLS fingerprints, header ordering, timing between page interactions.

Which means the stack chosen this quarter will need revisiting before long. Research teams that hold up are the ones treating infrastructure as something tuned continuously, not purchased once and forgotten.