Residential vs Datacenter Proxies: Which Is Better for Scraping?
Choosing between residential and datacenter proxies is one of the most consequential decisions in any scraping project. The wrong choice costs you money, time, and data quality. Residential proxies use IPs assigned by ISPs to real households, while datacenter proxies originate from servers in commercial data centers. Each has distinct strengths for scraping, and the best choice depends on your targets, budget, and scale.
This article provides a head-to-head comparison specifically for scraping use cases, with real success rate data, cost analysis, and a decision framework you can apply to your project.
For a broader comparison including mobile proxies, see our Residential vs Datacenter vs Mobile Proxies guide. For foundational proxy concepts, start with the Complete Guide to Web Scraping Proxies.
How Anti-Bot Systems See Each Proxy Type
The fundamental difference comes down to IP reputation. Anti-bot systems maintain databases of IP ranges and their associated ASN (Autonomous System Number) types:
| Property | Residential Proxies | Datacenter Proxies |
|---|---|---|
| IP Source | ISP-assigned to homes | Cloud/hosting providers |
| ASN Type | ISP (residential) | Hosting/business |
| Trust Level | High — looks like real users | Low — known proxy ranges |
| Detection Difficulty | Hard to detect | Easy to fingerprint |
| IP Pool Size | Millions globally | Thousands to tens of thousands |
When a website sees a request from a residential IP, it looks identical to a regular person browsing from home. A datacenter IP, on the other hand, immediately signals automated access because no real user browses from an AWS or Hetzner server.
Success Rates by Target Type
Success rates vary dramatically depending on the target site's sophistication. Here is what you can expect in practice:
| Target | Residential Success Rate | Datacenter Success Rate | Gap |
|---|---|---|---|
| Static blogs / news sites | 99%+ | 95-98% | Small |
| E-commerce (Shopify, small) | 97-99% | 80-90% | Moderate |
| Amazon / Walmart | 92-97% | 30-60% | Large |
| Google SERP | 90-96% | 20-50% | Very large |
| Social media (LinkedIn) | 85-93% | 10-30% | Extreme |
| Ticketing / sneaker sites | 80-90% | 5-15% | Extreme |
The harder the target, the wider the gap between residential and datacenter success rates. For protected targets, residential proxies are not just better — they are often the only viable option.
Cost Analysis
Residential proxies cost more per GB, but higher success rates often make them cheaper per successful request. Let us run the numbers:
Scenario: 100,000 Product Pages from Amazon
| Metric | Residential | Datacenter |
|---|---|---|
| Price per GB | $3-8 | $0.50-2 |
| Success rate | 95% | 40% |
| Requests needed for 100K pages | ~105,000 | ~250,000 |
| Avg page size | 200 KB | 200 KB |
| Total bandwidth | ~21 GB | ~50 GB |
| Estimated cost | $63-168 | $25-100 |
| Time to complete (10 RPM) | ~175 min | ~417 min |
| Cost per successful page | $0.0006-0.0017 | $0.00025-0.001 |
While datacenter proxies look cheaper at first glance, the actual cost depends heavily on the target. For easy targets, datacenter proxies save money. For hard targets like Amazon or Google, the extra retries and failures narrow or eliminate the cost advantage.
Hidden Costs of Low Success Rates
- Time waste: Failed requests still take time. A 40% success rate means 2.5x the crawl duration.
- IP burns: Blocked datacenter IPs often stay blocked for days, shrinking your usable pool.
- Monitoring overhead: More failures means more error handling, retry logic, and monitoring infrastructure.
- Data freshness: Slower completion means less fresh data — critical for price monitoring and SEO tracking.
Speed and Performance
Datacenter proxies generally have lower latency and higher throughput because they connect directly through high-speed infrastructure. Residential proxies route through consumer networks, which can add 50-200ms of latency.
| Metric | Residential | Datacenter |
|---|---|---|
| Average latency | 200-800ms | 50-200ms |
| Throughput per connection | 1-10 Mbps | 100+ Mbps |
| Connection stability | Variable | Very stable |
| Concurrent connections | Hundreds+ | Thousands+ |
For raw speed on unprotected targets, datacenter proxies win. But for protected targets, the time saved by lower latency is overshadowed by the time lost to failures and retries.
Implementation: Testing Both Types
Before committing to one type, benchmark both against your actual targets. Here is a testing framework:
Python Benchmark
import requests
import time
from dataclasses import dataclass
@dataclass
class BenchmarkResult:
proxy_type: str
total_requests: int
successful: int
failed: int
avg_latency_ms: float
total_bandwidth_mb: float
@property
def success_rate(self) -> float:
return self.successful / self.total_requests if self.total_requests else 0
def benchmark_proxy(proxy_url: str, target_urls: list[str], proxy_type: str) -> BenchmarkResult:
"""Benchmark a proxy type against target URLs."""
successful = 0
failed = 0
latencies = []
total_bytes = 0
for url in target_urls:
start = time.time()
try:
resp = requests.get(
url,
proxies={"http": proxy_url, "https": proxy_url},
timeout=30
)
latency = (time.time() - start) * 1000
latencies.append(latency)
if resp.status_code == 200:
successful += 1
total_bytes += len(resp.content)
else:
failed += 1
except Exception:
failed += 1
return BenchmarkResult(
proxy_type=proxy_type,
total_requests=len(target_urls),
successful=successful,
failed=failed,
avg_latency_ms=sum(latencies) / len(latencies) if latencies else 0,
total_bandwidth_mb=total_bytes / (1024 * 1024),
)
# Test against your actual targets
test_urls = ["https://example.com/page/" + str(i) for i in range(100)]
residential = benchmark_proxy(
"http://USERNAME:PASSWORD@gate.proxyhat.com:8080",
test_urls,
"residential"
)
print(f"Residential: {residential.success_rate:.1%} success, "
f"{residential.avg_latency_ms:.0f}ms avg latency")
print(f" {residential.successful}/{residential.total_requests} succeeded, "
f"{residential.total_bandwidth_mb:.1f} MB transferred")Node.js Benchmark
const HttpsProxyAgent = require('https-proxy-agent');
const fetch = require('node-fetch');
async function benchmarkProxy(proxyUrl, targetUrls, proxyType) {
let successful = 0, failed = 0;
const latencies = [];
let totalBytes = 0;
for (const url of targetUrls) {
const agent = new HttpsProxyAgent(proxyUrl);
const start = Date.now();
try {
const res = await fetch(url, { agent, timeout: 30000 });
latencies.push(Date.now() - start);
if (res.ok) {
successful++;
const buf = await res.buffer();
totalBytes += buf.length;
} else {
failed++;
}
} catch {
failed++;
}
}
return {
proxyType,
total: targetUrls.length,
successful,
failed,
successRate: successful / targetUrls.length,
avgLatencyMs: latencies.reduce((a, b) => a + b, 0) / latencies.length || 0,
totalMB: totalBytes / (1024 * 1024),
};
}
// Test residential proxies
const result = await benchmarkProxy(
'http://USERNAME:PASSWORD@gate.proxyhat.com:8080',
testUrls,
'residential'
);
console.log(`${result.proxyType}: ${(result.successRate * 100).toFixed(1)}% success`);Decision Framework
Use this decision tree to choose the right proxy type for your scraping project:
Choose Residential Proxies When:
- Targeting sites with strong anti-bot protection (Amazon, Google, social media)
- Success rate matters more than cost per GB
- You need geo-targeted IPs for localized data
- The project requires high data accuracy and completeness
- You are scraping at moderate scale (up to millions of pages per day)
Choose Datacenter Proxies When:
- Targeting sites with minimal protection (blogs, public APIs, open data)
- Raw speed and throughput are the priority
- Budget is very tight and targets are not aggressive
- You need massive concurrent connections (thousands)
- The data is not time-sensitive (you can retry failed requests overnight)
Consider a Hybrid Approach When:
- You scrape both easy and hard targets
- Some pages are critical (product details) while others are non-critical (images, static assets)
- You want to optimize cost without sacrificing reliability
Why ProxyHat Focuses on Residential
ProxyHat provides residential rotating proxies because they deliver the highest success rates across the broadest range of targets. With a pool of millions of residential IPs across 190+ countries, you get:
- 95%+ success rates on most targets including major platforms
- Automatic IP rotation through the gateway at
gate.proxyhat.com:8080 - Sticky sessions when you need session persistence
- Geo-targeting by country, state, or city
- Pay-per-GB pricing that scales with your usage
Explore ProxyHat pricing to find the right plan, or read the documentation to get started.
For language-specific setup guides, see Using Proxies in Python, Using Proxies in Node.js, or Using Proxies in Go.
Frequently Asked Questions
Are residential proxies always better than datacenter for scraping?
Not always. For targets with minimal protection (blogs, public APIs, open data portals), datacenter proxies offer better speed and lower cost. Residential proxies are superior when targeting protected sites like Amazon, Google, or social media platforms.
Can I mix residential and datacenter proxies in one project?
Yes. Many teams use datacenter proxies for non-critical requests (images, CSS, public APIs) and residential proxies for protected pages (product data, SERP results). This hybrid approach optimizes both cost and success rate.
Why are residential proxies more expensive?
Residential IPs are sourced from real ISP customers who opt into proxy networks. The supply is limited and the infrastructure to route traffic through consumer connections is complex. However, the higher success rate often makes residential proxies cheaper per successful request on hard targets.
How do I know which proxy type my target requires?
Run a benchmark. Send 100 requests through each proxy type and compare success rates. If datacenter success drops below 80%, residential is the better choice. For most commercial sites with any level of bot protection, residential proxies will outperform.
