Best Proxies for Web Scraping in 2026

The scraper works fine. It always works fine on the first few hundred requests. Then the responses start coming back empty. The HTML is there, the status code says 200, the page loads normally in a browser. Your scraper is pulling back nothing, or worse, it’s pulling back a CAPTCHA page that looks nothing like the data you expected.

This is the point where most developers realize they have a proxy problem. The website’s anti-bot system has fingerprinted something about their traffic and decided to stop cooperating. The right proxy can fix this. The wrong one will burn through your budget while still getting blocked.

This guide breaks down which proxies actually work for web scraping in 2026, why they work, and how to configure them so you’re not wasting money on requests that never succeed.

Why Scrapers Get Blocked: How Anti-Bot Systems Actually Work

Before choosing a proxy, you need to understand what you’re up against. Anti-bot systems don’t rely on a single detection method. They layer multiple signals together and score each incoming request on a spectrum from “definitely human” to “definitely a bot.” Understanding these layers tells you which proxy features actually matter and which are marketing fluff.

IP Reputation

This is the first and fastest check. Before a website examines anything else about your request, it looks at where the request is coming from. Every IP address carries a reputation based on its history and origin.

Datacenter IPs are publicly registered to hosting companies. Anti-bot systems maintain databases of these ranges and flag traffic from them immediately. The IP doesn’t need to have done anything wrong. Its origin alone is enough to trigger elevated scrutiny.

Residential IPs belong to home internet connections assigned by ISPs. These match the profile of normal web traffic, which is why they pass initial reputation checks more easily.

Mobile IPs take this further. Mobile carriers use a technology called CGNAT (Carrier-Grade NAT) that routes hundreds or even thousands of legitimate mobile users through a single public IP address. This mechanism, formalized in RFC 6598, means that when a website sees traffic from a mobile IP, it knows that blocking that address could cut off real customers. This makes mobile IPs the hardest type for anti-bot systems to act against.

Behavioral Fingerprinting

IP reputation only gets you through the front door. Once you’re in, the site starts watching how you behave.

Real users scroll. They pause. They move their mouse in imperfect curves and click on things in no particular order. A scraper, even one running in a headless browser, tends to do none of these things. It loads a page, extracts data, and moves to the next URL in under a second.

Browser fingerprinting adds another dimension. Every browser exposes dozens of signals: canvas rendering, WebGL output, installed fonts, the navigator.webdriver property, and more. A 2016 study by Laperdrix et al. found that browser fingerprints were unique for approximately 1 in 286,777 browsers tested. Anti-bot systems collect these signals and use them to identify scrapers even when the IP looks clean.

Request Pattern Analysis

Humans don’t request 50 pages per second, all from the same URL path, at perfectly even intervals.

Anti-bot systems track request frequency, URL patterns, and timing. If your scraper is hammering /product/ pages at a consistent 200ms interval, the pattern alone triggers a block regardless of what IP you’re using. This is why proxy rotation alone isn’t enough. You also need to vary your request timing and access patterns to resemble genuine browsing.

Header and TLS Fingerprinting

Every HTTP request carries headers that describe the client making it. The User-Agent, Accept-Language, Accept-Encoding, and other headers form a signature. When these don’t match what a real browser would send, or when they’re inconsistent with the browser claimed in the User-Agent string, the request gets flagged.

TLS fingerprinting goes deeper. When your client establishes an HTTPS connection, the TLS handshake reveals specific cipher suites, extensions, and elliptic curves that are unique to different HTTP clients. A Python requests library has a completely different TLS fingerprint than Chrome. If your User-Agent says Chrome and your TLS handshake says Python, the mismatch is a dead giveaway. This technique, commonly known as JA3 fingerprinting, is increasingly standard among commercial anti-bot services. Its successor, JA4, was designed by the same creator to handle modern browsers that now randomize TLS extension order to resist the original JA3 method.

Cloudflare’s bot management system, which sits in front of a significant share of all web traffic, combines machine learning, heuristics, behavioral anomaly detection, and JavaScript challenges into a single scoring pipeline. No single proxy feature defeats all of these layers. IP reputation is handled by proxy type. Behavioral fingerprinting requires browser automation. Request patterns need throttling and randomization. Header and TLS fingerprinting require client-level configuration. A good proxy solves the first problem and makes the rest solvable.

The Three Proxy Types, Explained

Every proxy server sources its IP from somewhere, and that origin determines how effective it is for scraping. Three categories matter for this discussion.

Datacenter Proxies

Datacenter proxies source their IPs from cloud hosting providers and data centers. These IPs are registered to companies, not individuals, and the ranges are public knowledge.

Speed is their strength. Datacenter infrastructure is built for throughput, and these proxies typically deliver the lowest latency and highest bandwidth of any proxy type. They’re also the cheapest, often 1/10th the price of residential proxies per request.

The trade-off is visibility. Because datacenter IP ranges are publicly known, anti-bot systems identify them instantly. This doesn’t mean every request gets blocked. Plenty of websites don’t run aggressive bot detection. For scraping public APIs, government data, or sites without Cloudflare or similar protection, datacenter proxies remain the most cost-effective option.

Residential Proxies

Residential proxies source their IPs from real home internet connections assigned by ISPs. This is the type of traffic websites expect from normal visitors.

The IP pools are large. Major providers offer access to millions of IPs across 190+ countries, with targeting down to the city level. This scale, combined with the inherent trust residential IPs carry, makes them the standard choice for scraping operations that need to bypass bot detection.

Cost is per gigabyte, typically starting around $1/GB for larger plans. This pricing model means bandwidth management directly impacts your budget, a topic we cover later in this article.

Mobile Proxies (4G/5G)

Mobile proxies source their IPs from mobile carriers via SIM cards connected to cellular networks. They are the most effective proxy type for scraping protected targets.

The reason comes down to CGNAT. Mobile carriers assign a single public IP address to many devices simultaneously. A website that sees traffic from a mobile IP can’t block that address without risking collateral damage to legitimate mobile users on the same carrier. Websites know this. As a result, mobile IPs face fewer blocks, fewer CAPTCHAs, and less aggressive rate limiting than any other proxy type.

Mobile proxies cost more, starting around $2/GB. That higher per-GB price is offset by higher success rates. When 95% of your requests succeed on mobile versus 70% on residential, the cost per successful request can actually end up lower. We’ll do the math on this in the measurement section.

For a detailed breakdown of how these proxy types compare across other criteria like geotargeting and scalability, see 5 Criteria for Choosing the Best Proxy Server.

Which Proxy Type Fits Your Scraping Target

The generic comparison table you see in every proxy article lists speed, cost, and pool size in a grid. That table doesn’t help you make a decision. What you actually need to know is which proxy type to start with for your specific target and when to switch if it stops working.

Match Your Target to a Proxy Type

A note on those cost estimates: they assume you’re running the bandwidth optimization techniques covered later in this article. Without them, multiply the JavaScript-rendered and e-commerce rows by 3-5x.

When to Escalate: Reading Your Scrape Logs

The table above tells you where to start. This one tells you when to change course based on what you’re actually seeing in production.

Mixing Proxy Types: The Tiered Approach

Running every request through mobile proxies would maximize success rates. It would also maximize your costs. The smarter approach is to tier your proxy usage based on target difficulty and route each domain through the cheapest proxy type that can handle it.

Start with datacenter proxies for targets that have no meaningful bot detection. Public data feeds, open APIs, government databases, and small sites without commercial anti-bot protection will return data reliably on the cheapest proxy type.

Use residential proxies as your default for everything else. Most commercial websites fall into this middle ground: enough bot detection to block datacenter IPs, not enough to require mobile.

Reserve mobile proxies for the hardest targets only. Major e-commerce platforms, social media sites, and any domain that consistently defeats residential IPs deserve the higher per-GB cost. Mobile should be an escalation, not a starting point.

This works in practice because most scraping operations target a mix of sites. If you’re monitoring prices across 50 e-commerce stores, maybe 5 of them are aggressive enough to need mobile proxies. The other 45 work fine on residential. Running all 50 through mobile would cost 2-3x more with no benefit on the majority of your targets.

To implement this, you need a routing layer in your scraping infrastructure that assigns proxy types per domain. Proxidize allows you to configure multiple proxy ports with different IP types and rotation settings, so your scraper can select the appropriate one per request. Our Twitter scraper guide walks through a real example of this, where mobile proxies with automatic rotation are configured specifically because the platform’s detection is aggressive enough to warrant them.

How to Set Up a Proxy for Web Scraping

The configuration itself is straightforward. Here’s how to route your scraping traffic through a proxy in the most common tools.

Python (Requests Library)

The requests library is the standard for simple HTTP scraping in Python. Proxy configuration takes a few lines:

import requests

username = "customer-USER"

password = "PASS"

proxy = "pg.proxi.es:20000"

proxies = {

    "http":  f"http://{username}:{password}@{proxy}",

    "https": f"http://{username}:{password}@{proxy}"

}

response = requests.get("https://httpbin.org/ip", proxies=proxies)

print(response.text)

Replace the username, password, and proxy address with your actual credentials. The same proxy dictionary works with any requests.get() or requests.post() call. The Requests library documentation covers additional proxy options including SOCKS support and environment variable configuration. For a deeper walkthrough of Python scraping fundamentals, our guide to web scraping with Beautiful Soup covers the full process from environment setup to data extraction.

Scrapy

Scrapy handles proxies through the request meta parameter:

yield scrapy.Request(

    url,

    callback=self.parse,

    meta={"proxy": "http://customer-USER:[email protected]:20000"}

)

For projects with multiple spiders, a custom downloader middleware is cleaner. It lets you define the proxy once and apply it to every outgoing request automatically, without repeating the meta parameter in each spider. The Scrapy Playwright integration extends this to handle JavaScript-rendered pages through the same framework.

cURL

For quick testing or shell scripts, cURL supports proxies with the -x flag:

curl -x http://customer-USER:PASS@pg.proxi.es:20000 

https://httpbin.org/ip

This is useful for verifying that a proxy is working before integrating it into your scraping pipeline. If the response returns an IP different from your own, the proxy is routing correctly.

If you’re working with Selenium or other browser automation frameworks, proxy setup varies slightly per tool. The Python libraries for web scraping guide compares the major options and their proxy integration approaches.

How to Rotate Proxies Without Burning Through Your Pool

Rotation sounds simple. Get a new IP, send the request, repeat. In practice, the rotation interval matters more than the size of the IP pool. Rotate too aggressively and you trigger rate-limit defenses designed to catch distributed bots. Rotate too slowly and a single IP accumulates enough request history to get flagged.

Three rotation modes exist, and each fits a different scraping pattern.

Per-request rotation assigns a fresh IP to every single HTTP request. This is the default for stateless jobs like pulling search results or scraping product listings where each page is independent. No session continuity means no cookies carry over, no login state persists, and the target site sees each request as a completely new visitor. For most large-scale collection jobs, this is what you want.

Timed rotation holds the same IP for a set window, typically between 1 and 10 minutes, then swaps it. Use this when you’re paginating through results and the site tracks your page position server-side. If your IP changes between page 3 and page 4, some sites reset your result set or serve duplicate content. A 3-5 minute window usually covers a full pagination cycle without triggering per-IP request limits.

Sticky sessions lock an IP for 10 minutes to several hours. These are non-negotiable for any scrape that involves authentication. If you’re logging into an account, navigating through a dashboard, or completing a multi-step form, the target site ties your session token to your IP. Change the IP mid-session and the site invalidates the token, forcing a re-login that wastes time and draws attention.

Proxidize supports all three modes. You set the rotation behavior per port, which means you can run per-request rotation on one port for product scraping and a 30-minute sticky session on another port for authenticated access, simultaneously. Mobile proxies can rotate every request when billed per GB, or at a set interval (down to every minute) when purchased per proxy.

For authenticated scraping workflows in particular, session handling is critical. Our guide on scraping websites with login pages covers how to pair sticky sessions with Python’s requests.Session() to maintain login state across multiple page requests.

Handling the Rotation Gap

The transition between IPs is a vulnerability. For a brief window during rotation, your next request either waits for a new IP assignment or fails outright. The proxy server guide notes that some rotating endpoints can take up to 60 seconds to reestablish a connection after rotation. At scale, these gaps compound. A thousand concurrent scrapers each losing 200-500ms per rotation event adds up to meaningful job completion delays.

Two ways to mitigate this. First, pre-warm your next IP by requesting it before releasing the current one. Most proxy APIs support this through session pre-allocation. Second, build retry logic that distinguishes between a rotation gap and a block. A rotation gap usually manifests as a connection timeout. A block returns a 403 or a soft-block page with a 200 status. Treating both the same way, by rotating and retrying, wastes IPs on temporary gaps that would resolve on their own with a brief wait.

Dealing with Pre-Flagged IPs

Not every IP in a proxy pool is clean. Some residential and mobile IPs have been used by other customers, flagged by target sites, and recycled back into the pool before their reputation recovered. You’ll know you’ve drawn a pre-flagged IP when your very first request returns a CAPTCHA or a 403.

Build a validation step into your rotation logic. Before sending a fresh IP against your actual target, test it against a lightweight endpoint like httpbin.org/ip or the target’s robots.txt. If the test request fails or returns unexpected content, discard that IP and draw another. This adds a few milliseconds of latency per rotation. It prevents wasting time and bandwidth on requests that were never going to succeed.

Cut Your Proxy Bill by Not Downloading What You Don’t Need

Residential and mobile proxies charge by the gigabyte. Every image, stylesheet, font file, and tracking script that loads alongside your target data is bandwidth you’re paying for and throwing away.

A typical e-commerce product page weighs 2-3MB when fully loaded. The 2025 Web Almanac puts the median desktop page at 2.9MB, with images alone accounting for over 1MB on average. The actual data you want, the product name, price, description, and availability, fits in maybe 15-30KB of HTML. That means over 98% of your bandwidth spend on that page is waste.

Disable Images, CSS, and Fonts in Headless Browsers

If you’re using Puppeteer or Playwright to render JavaScript-heavy pages, intercept network requests and abort anything that isn’t HTML, XHR, or fetch:

await page.route('**/*', (route) => {

  const type = route.request().resourceType();

  if (['image', 'stylesheet', 'font', 'media'].includes(type)) {

    route.abort();

  } else {

    route.continue();

  }

});

This single block typically reduces page load bandwidth by 60-80%. On a 10,000-page scrape through residential proxies at $5/GB, that’s the difference between a $100 proxy bill and a $20 one.

Target What You Need with Selectors

Full-page parsing is the second source of waste. Loading an entire DOM into memory only to extract three fields is like downloading a whole database to read one row.

Once the page loads, extract only the elements you need:

const price = await page.$eval('.price-current', el => el.textContent);

const title = await page.$eval('h1.product-title', el => el.textContent);

const stock = await page.$eval('.stock-status', el => el.textContent);

This doesn’t save proxy bandwidth directly since the page already loaded over the wire. What it saves is processing time and memory, which matters when you’re running hundreds of concurrent browser instances. Lower memory per page means more concurrent pages per machine, which means faster job completion without scaling your infrastructure.

Prefer APIs and XHR Endpoints Over Full Page Loads

Many sites load product data through internal API calls after the initial page render. Open your browser’s Network tab, filter by XHR/Fetch, and look for JSON responses that contain the data you need. If you find one, you can call that endpoint directly with a simple HTTP request instead of launching a full headless browser.

A JSON API response for a product listing might be 5-10KB. The same data loaded through a full browser render costs 2-3MB. That is a 200-300x bandwidth reduction. For scraping operations running through residential proxies billed per gigabyte, this is the single highest-impact optimization available.

Not every site exposes clean API endpoints. Some protect them with tokens generated during the initial page load. When that happens, you’re back to headless rendering with request interception. The key is checking for the API shortcut first before defaulting to the heavier approach.

Measuring What Actually Works

You can’t improve what you don’t track. Three metrics tell you whether your proxy setup is working or silently wasting money.

Success Rate

The percentage of requests that return the data you expected. Not the percentage that return a 200 status code.

This distinction matters because soft blocks return a 200 with empty or decoy content. Your monitoring needs to validate that the response contains the actual data fields your scraper targets. A raw HTTP status check will miss these completely.

Track this per domain and per proxy type. A 90% aggregate success rate might mask the fact that one high-value target sits at 40% while everything else runs at 98%. Per-domain tracking tells you exactly where to escalate.

Cost Per Successful Request

Divide your total proxy spend by the number of requests that returned usable data. This is the metric that reveals whether a “cheaper” proxy is actually cheaper.

Consider this example. Residential proxies cost $5/GB and achieve a 75% success rate on a target. Mobile proxies cost $10/GB and achieve 95% on the same target. If each page is 50KB after bandwidth optimization:

• Residential: 20,000 pages per GB, 15,000 successful. Cost per success: $0.00033
• Mobile: 20,000 pages per GB, 19,000 successful. Cost per success: $0.00053

Residential wins in this scenario. Now shift the success rates to 60% residential and 95% mobile:

• Residential: 12,000 successful. Cost per success: $0.00042
• Mobile: 19,000 successful. Cost per success: $0.00053

Still residential, barely. Drop residential to 50%:

• Residential: 10,000 successful. Cost per success: $0.00050
• Mobile: 19,000 successful. Cost per success: $0.00053

Nearly identical. Below 50% residential success, mobile becomes the cheaper option per successful request, and the gap widens fast. The only way to find the crossover point for your specific targets is to measure both.

Job Completion Time

How long does a full scraping job take from start to finish? This captures everything the other metrics miss: rotation gaps, retry queues, rate limiting delays, and infrastructure bottlenecks.

If your 100,000-page job takes 4 hours on residential proxies with a 75% success rate (because 25% of requests need retries) and 2.5 hours on mobile at 95%, the time savings might justify the higher per-GB cost. This is especially relevant for time-sensitive operations like price monitoring or stock availability checks, where stale data has a real business cost.

Frequently Asked Questions