Proxy Rotation Efficacy Report: Per-Request vs. Session vs. Sticky

Empirical data on three rotation strategies tested across 100K requests per configuration. Includes success rate curves, session correlation risks, and optimal rotation intervals by target type.

Methodology & test setup

This study ran 300,000 requests per proxy type across 500+ unique target domains over a 90-day period from January to March 2025. Targets were segmented into three tiers based on their anti-bot sophistication: Tier-1 (no protection), Tier-2 (basic fingerprinting), and Tier-3 (advanced fingerprinting + behavioral analysis).

All requests were made using a standardized headless Chromium instance with playwright-stealth patches applied. The only variable changed between test runs was the proxy configuration — type, rotation strategy, and pool size. Infrastructure was identical across all test groups.

# Test harness — simplified
import asyncio
import random
from dataclasses import dataclass, field
from playwright.async_api import async_playwright
from playwright_stealth import stealth_async

@dataclass
class TestConfig:
    proxy_type: str          # "mobile" | "residential" | "datacenter" | "isp"
    rotation:   str          # "per_request" | "session" | "sticky_5min"
    pool_size:  int
    targets:    list[str] = field(default_factory=list)

async def run_test(config: TestConfig, n_requests: int = 1000) -> dict:
    results = {"success": 0, "blocked": 0, "error": 0}
    pool = get_proxy_pool(config.proxy_type, config.pool_size)

    async with async_playwright() as p:
        for _ in range(n_requests):
            proxy = get_next_proxy(pool, config.rotation)
            target = random.choice(config.targets)
            try:
                browser = await p.chromium.launch(proxy=proxy)
                page = await (await browser.new_context()).new_page()
                await stealth_async(page)
                resp = await page.goto(target, wait_until="networkidle")
                results["success" if resp.ok else "blocked"] += 1
                await browser.close()
            except Exception:
                results["error"] += 1
    return results

Key findings

The data reveals a clear hierarchy: mobile carrier IPs consistently outperform every other proxy type across all target tiers. The gap widens dramatically at Tier-3 targets — where mobile proxies achieve a 99.4% success rate versus 62.1% for datacenter proxies.

Proxy type breakdown

Not all proxy types are equal. The chart below shows average success rates by proxy type across Tier-3 targets — the most demanding category. Mobile and residential proxies dominate; datacenter proxies fail on most sophisticated targets.

Target tier analysis

Tier classification matters enormously. At Tier-1 targets (no anti-bot), even datacenter proxies achieve 98%+ success. But as target sophistication increases, the gap between mobile and datacenter proxies grows from ~1% to over 37 percentage points.

• Tier-1: Static HTML, no bot detection. All proxy types achieve 95%+ success rates. Cost optimization is the primary driver here.
• Tier-2: Basic fingerprinting — JA3/JA4, User-Agent matching. Residential and mobile proxies maintain high success; datacenter begins to falter (~78%).
• Tier-3: Full behavioral analysis, TLS fingerprinting, and IP reputation scoring. Only mobile and residential proxies maintain acceptable success rates.

Cost-per-success analysis

Raw success rate tells only part of the story. When you factor in price-per-GB and normalize for successful requests only, mobile proxies are often cheaper than residential on Tier-3 targets — because fewer requests are wasted on blocks and retries.

For teams scraping Tier-3 targets at scale, switching from residential to mobile proxies reduced their effective cost-per-successful-request by 22% despite a higher nominal price-per-GB.

Recommendations

Based on our data, here is the recommended proxy strategy by use case:

• E-commerce scraping: Mobile proxies with per-request rotation. Most major retailers are now Tier-3 targets.
• Price monitoring: Residential with session rotation (5–10 req). Balances cost and success at Tier-2 targets.
• LLM training data: Residential at scale with per-request rotation. Volume matters more than individual success rate.
• Brand protection / SERP: Mobile proxies. Search engines are the strictest Tier-3 targets in our dataset.

Conclusion

The proxy landscape in 2025 is more stratified than ever. As anti-bot systems grow more sophisticated, the gap between proxy types has widened — making proxy selection a first-order infrastructure decision, not an afterthought.

Mobile carrier IPs are the clear leaders for demanding targets. For teams scaling web data collection, the shift to mobile proxies with per-request rotation delivers the highest success rates and, counterintuitively, a lower effective cost-per-successful-request at Tier-3 targets.