Image source: Pixabay
 

Python rarely makes web scraping difficult, but things change when a simple script becomes a real data pipeline: hundreds of thousands of requests, multiple geographic markets, rate limits, unstable connections, and CAPTCHA challenges during automated crawling. Once that stage is reached, the choice of the best web scraping proxy can have a greater effect on your dataset than another round of code optimization. If you’re an analyst, ML engineer, or researcher, treat the proxy as part of your infrastructure. The right provider can boost collection success rates, enable geo-targeted data collection, and reduce failed requests that need retries.

The following seven providers are worth considering in 2026, along with how we compared them.

How We Evaluated the Best Web Scraping Proxy Providers

Proxy comparisons often mix very different products, so we focused on the following metrics:

• Speed: the average response time and network performance.
• Stability: uptime and success rates.
• IP pool: advertised size, geographic coverage, and IP diversity.
• Rotation: the feature lets you rotate IPs on a per-request or per-session basis.
• Geo-targeting: targeting by country, city, state, ZIP code, ISP, or ASN.
• Cost: the effective cost per GB for traffic used by home users.
• Concurrency: the ability of the service to handle multiple requests at the same time.
• Integrations: support for HTTP(S), SOCKS5, APIs, and the various integration options.

To check this externally, we also examined Proxyway’s large-scale 2026 proxy study. The methodology involved combining measurements from an IP pool with tests of success rate and response time. The home-user tests make use of hundreds of thousands of requests from various locations. In the most recent research, the best web scraping proxy achieved infrastructure success rates of about 99% or higher.

Important: the prices and pool sizes listed below are figures published by the providers and may change; use them as a practical overview when considering a purchase, not as a statement that each provider will deliver the same performance for every website.

1. Proxy-Seller

 

Proxy-Seller is an excellent option for teams that want to use residential proxies without spending at an enterprise level. Its network currently lists 47 million or more real IPs across 220 or more locations, offers city-level targeting, supports rotating or sticky sessions, averages around 0.35 seconds, and boasts a 99.5% success rate. Residential plans start at about \$1.3 per GB on the currently available monthly tiers, and prices drop with higher volumes.

Web scraping teams can find granular targeting and session controls especially useful. They can rotate IP addresses, avoiding rate limits during data collection while maintaining a sticky session when a workflow requires a consistent identity. When you are ready to buy private proxy servers, choose a proxy type that matches your target rather than opting for the cheapest plan.

Ideal for: research projects, market monitoring, geo-targeted collection, and teams looking for a fairly simple setup.

2. Decodo

 

Decodo, formerly Smartproxy, is especially noteworthy for teams that run regular web scraping tasks. Its residential network includes more than 115 million IPs across 195+ locations, with an average response time under 0.5 seconds, a 99.92% success rate, and both rotating and sticky sessions. Residential prices start at \$2 per GB on higher-volume tiers, while the pay-as-you-go option is \$4 per GB. The service also lets you target by country, state, city, ZIP code, and ASN, which helps when a dataset needs to reflect users from specific markets.

Ideal for: recurring collection pipelines, SERP monitoring, ecommerce datasets, and ML information gathering.

3. Oxylabs

 

Oxylabs focuses on teams where reliability and scale matter more than the lowest possible entry price; its residential starter plan currently starts at \$6 per GB, and prices drop to \$2.50 per GB at 1 TB. The company promotes having more than 175 million residential IPs, together with the ability to target at the city level and support for HTTP(S) and SOCKS5.

The main disadvantage is that the cost structure only makes sense when the value of the information gathered is high, or the workload is large enough to warrant enterprise infrastructure.

Best for: large dataset collection for machine learning, competitive intelligence, price monitoring, and production-grade pipelines.

4. Bright Data

 

It is one of the most feature-complete options for businesses that need wide geographic coverage. Its service offers access to more than 400 million monthly residential IPs across 195 countries, includes targeting by country, city, and ZIP code, and claims a 99.95% success rate. The pay-as-you-go cost for residential traffic is currently \$4 per GB, while volume plans bring the effective price down to \$2.50 per GB at the highest tier listed. This matters most when you’re doing geo-targeted data collection. When your analysis involves comparing prices, search results, product availability, or content across markets, accurate location controls can be more valuable than simply having a huge number of IPs.

The tradeoff is a higher entry cost, which only pays off for organizations with enough traffic volume to lower the per-GB price.

Best for: international datasets, ad checking, ecommerce research, and big business data collection.

5. Webshare

 

Webshare offers several relatively cheap proxy products. Its rotating residential network claims more than 80 million IPs, boasts 99.97% uptime, and its price drops to about \$1.40 per GB at the 3,000 GB level. The company also provides datacenter and static home-user options, which are useful when you don’t need home-based IP addresses.

Flexibility of this kind is beneficial for a data team; when carrying out web scraping to collect public pages from a website that is fairly permissive, there is no need to use an expensive residential proxy service since datacenter proxies are able to provide high throughput at a greatly reduced cost, whereas residential proxies are suitable for sites that have stronger anti-bot measures.

Appropriate for: students, analysts, small teams, high-volume and low-cost data collection, and mixed proxy configurations.

6. SOAX

 

SOAX focuses on gathering location-based information; its residential plans start at \$3.60 per gigabyte when 25 gigabytes are used and drop to \$2 per gigabyte once 800 gigabytes have been used. The service advertises coverage in over 195 countries, supports targeting by country/region/city/ISP, allows both rotating and sticky sessions, and offers an unlimited number of proxy connections.

Suitable for: conducting market research in a given area, giving localized search results, providing travel information, and comparing prices.

7. IPRoyal

 

Consider IPRoyal if traffic volumes are difficult to predict. The service currently provides access to more than 64 million IPs across over 195 countries, and pay-as-you-go prices start at \$7 per GB for small quantities, with costs decreasing for larger volumes. Custom plans can drop to \$1.75 per GB at 10 terabytes.

The provider also offers rotating and sticky sessions, along with an unlimited number of concurrent sessions; its published performance figures show an average response time of about 0.5 seconds and a success rate of 99.4 percent.

The biggest advantage here is flexibility, since you don’t need to buy a large monthly package for a one-off research project.

Suitable for: conducting experiments, creating prototypes, carrying out occasional web scraping, and teams that have variable traffic.

What Should You Choose Between Residential vs Datacenter IP Pools?

Image source: Gemini
 

Only after you have determined your destination should you select the best web scraping proxy.

Datacenter proxies are usually faster and cheaper, and they work effectively with public websites that have only limited anti-bot measures.

Residential proxies use IP addresses associated with consumer internet connections, since this traffic resembles that of ordinary users and suits websites with strong bot detection, geographic restrictions, or aggressive rate limits.

A practical setup can combine both:

1. Start with datacenter IP addresses for targets that are easy to reach.
2. Move difficult domains to consumer IP addresses.
3. If rate limits come from repeated requests, use IP rotation for large-scale data pulls.
4. Use sticky sessions when a workflow requires the same IP address for multiple queries.

The goal isn’t to scrape for its own sake, but to build a pipeline that makes a reasonable number of requests without repeatedly triggering automated protection measures.

Python Requests Library Proxy Integration

Python users don’t need a special framework to use the best web scraping proxy, since the requests library lets you use proxies either via the proxies argument or through a session.

A simple pattern looks like this:

import requests

proxies = {
  "http": "http://USER:PASSWORD@HOST:PORT",
  "https": "http://USER:PASSWORD@HOST:PORT",
}

response = requests.get(
  "https://example.com",
  proxies=proxies,
  timeout=20
)

print(response.status_code)

For larger pipelines, put proxy selection, retries, timeouts, and logging in a separate layer rather than scattering proxy settings throughout your scraping tool.

This also aligns with the data-pipeline approach explained in the guide to reproducible Python research pipelines, which involves collecting raw data first, keeping it, and then carrying out cleaning and analysis as separate stages.

Methodology: How to Test the Best Web Scraping Proxy Before Scaling

Even the best web scraping proxy in theory isn’t necessarily the best choice for your situation, so it’s wise to run a small, controlled test before buying a large package.

Use the same:

• URLs
• request headers
• timeout
• concurrency level
• geographic location
• request rate
• retry policy

Then record:

• Success rate = successful responses / total requests
• Median response time
• 95th-percentile response time
• Timeout rate
• HTTP error rate
• CAPTCHA frequency
• Unique IP count
• Bandwidth consumed
• Cost per successful page

For instance, even though Provider A costs \$2 per GB and only 80% of requests succeed, while Provider B costs \$3 per GB but succeeds 97% of the time, Provider B could still be the cheaper option on a per-usable-record basis.

If you want to run your own experiment, start with 1,000 to 10,000 requests rather than jumping straight to millions, and compare providers on the specific domains that matter for your dataset.

What Are the Situations for Rate Limits, CAPTCHAs, and Concurrency?

No single best web scraping proxy is a magical solution for every situation.

If you’re still seeing a large number of CAPTCHAs, check your request frequency, headers, cookies, browser fingerprint, and navigation pattern. Increasing the number of IPs won’t fix a request pattern that’s already inefficient – it’ll just make it more costly, with no benefit.

You must also handle a large number of concurrent requests, since a sudden surge to hundreds of queries is very different from a moderate, steady request rate.

For teams automating routine tasks, use proxy rotation with a queue, exponential backoff, retries, and structured logging; the tutorial on automating data collection with Bash scripts is a good starting point for scheduled collection jobs.

Ethical Considerations in Web Collection

Even if web scraping works, it may still produce a poor dataset. Before you start gathering any information, review the website’s Terms of Service, the robots.txt file (if there is one), any privacy requirements, copyright restrictions, and applicable local laws. If you do not have a clear legal basis and legitimate aim, do not collect any sensitive personal data.

When collecting machine-learning data, you must keep a record of where the information came from, when it was collected, the transformations that were carried out, and any restrictions regarding redistribution.

When scraping personal data, organizations should also follow official guidelines – e.g., the European Data Protection Board (EDPB). These guidelines stress compliance with GDPR principles such as transparency, data minimization, and purpose limitation.

Final Verdict on a Proxy for Web Scraping

Even the best web scraping proxy isn’t always optimal because the right choice depends on your targets, traffic volume, location, and how many failed requests you can tolerate.

Most teams test two or three of the best web scraping proxy services against the actual websites they need to collect data from. Instead of focusing only on raw speed, they should measure the number of successful records and then calculate the cost per usable record. This metric will tell you far more than a simple figure for the size of the IP pool.