Web scraping has evolved far beyond single-threaded scripts. At scale, the biggest technical challenge is no longer how to extract data — it’s how to extract it without getting blocked, throttled, or fed false content. One of the most overlooked but critical pieces of this puzzle is request concurrency — the number of simultaneous connections a scraper makes to a target server. Managed poorly, concurrency results in detection and bans. Managed well, it becomes the core of a resilient and scalable scraping operation.
Why Concurrency Is a Scraper’s Hidden Bottleneck
Modern websites — especially e-commerce, travel, and social platforms — implement increasingly aggressive bot protection. According to a study by DataDome, nearly 40% of all internet traffic in 2023 was generated by bots, with malicious scrapers accounting for a significant share. To defend themselves, websites use behavioral fingerprinting, rate limiting, CAPTCHA walls, and honeypot traps.
Concurrency mismanagement is often the trigger. Flooding a server with too many requests from the same IP (or even subnet) within a short window is a telltale sign of automation. Even if requests are technically well-formed, their timing gives the game away.
The real goal of concurrency management isn’t speed — it’s mimicry. How close can you get to human browsing behavior while still scaling up extraction?
How to Calculate “Safe” Concurrency Levels
There’s no universal concurrency limit that fits every site. Instead, experienced scrapers rely on empirical backoff algorithms. One proven strategy is to use exponential backoff — gradually increasing request volume and monitoring the target server’s responses for changes (e.g., 429 errors, CAPTCHA challenges, or subtle content variations).
According to engineering data published by Zyte (formerly Scrapinghub), on average:
- For lightly protected sites, safe concurrency ranges between 8 to 15 requests per second per domain.
- For heavily guarded sites (e.g., LinkedIn or Google), you may need to drop below 1 request per second with aggressive delay randomization.
Your stack matters too. Async frameworks like aiohttp or trio can handle higher concurrent connections without blocking the event loop. However, concurrency on the code level must still respect real-world limits on the target server’s side.
Distributed IPs Are Not Optional — They’re Foundational
No amount of concurrency throttling can bypass the fact that a single IP address has a short lifespan when scraping. Distributed IP usage is mandatory. That’s where residential proxies shine.
Unlike data center proxies, residential proxies use IPs assigned by real internet service providers, routing traffic through consumer devices. This makes them significantly more resilient to detection.
In practical terms, residential proxies allow scrapers to spread out concurrent requests across multiple IPs, drastically reducing detection risk. If you’re building a scraper that hits dozens of pages per second across multiple domains, it’s not just a best practice — it’s infrastructure.
You can buy residential proxy packages tailored to your concurrency requirements. The better providers offer location targeting, session control, and sticky IP options, which are crucial for certain scraping tasks like multi-step form submissions or logged-in sessions.
Respecting Target Sites Still Matters
Concurrency tuning isn’t just a technical problem — it’s also an ethical one. If you hit a public site with hundreds of simultaneous requests without regard for server load, you’re not just risking bans — you’re causing real performance issues for legitimate users.
Responsible scrapers:
- Obey robots.txt rules where applicable.
- Set polite delays and randomized intervals.
- Monitor HTTP status codes and back off when signs of rate limiting appear.
It’s also good practice to avoid scraping during peak traffic hours for the target site, especially for smaller businesses without robust infrastructure.
Final Thoughts
Concurrency is both an enabler and a limiter. Scrapers that ignore it inevitably run into roadblocks — not just technical ones, but legal and ethical too. On the flip side, teams that treat concurrency management as a core engineering challenge unlock scraping at scale with fewer interruptions and higher data fidelity.
And the foundation of that scalability? Distributed IP infrastructure. For serious scraping setups, especially those involving login-protected content or dynamic rendering, it pays to buy residential proxy solutions that give you the IP diversity and stability you need to operate without disruption.