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Home/Blog/PyProxy vs. 4EverProxy: Multithreaded HTTP request performance evaluation
PyProxy vs. 4EverProxy: Multithreaded HTTP request performance evaluation
PYPROXY·Aug 26, 2025
The use of proxy servers in modern web development has become increasingly important, especially when it comes to handling high volumes of HTTP requests efficiently. In this article, we will evaluate the multi-threaded HTTP request performance of two popular proxy services: PYPROXY and 4everproxy. The evaluation will be based on several key performance metrics, including response time, throughput, and stability under load. By analyzing these factors, we aim to provide insights for users seeking the best proxy solution for their specific use case, ensuring both efficiency and reliability in high-demand environments.
Introduction to Proxy Servers and Their Role in Web Development
Proxy servers act as intermediaries between a client and the internet, routing requests to web resources on behalf of the client. Their primary function is to enhance security, privacy, and performance. Proxies can be especially useful for managing large-scale web scraping, automated browsing, or handling heavy traffic for applications. Multi-threaded HTTP requests, where multiple requests are handled simultaneously, further optimize the usage of proxies, improving both speed and efficiency.
As businesses and developers increasingly rely on proxies for automated tasks, selecting the right proxy service becomes critical. In this comparison, we focus on two proxy services that cater to multi-threaded HTTP requests: PyProxy and 4everproxy. Both of these services offer unique features, but how do they perform under pressure? Let’s delve into the performance evaluation.
Overview of PyProxy and 4everproxy
Before diving into the performance analysis, it is important to understand what PyProxy and 4everproxy offer. PyProxy is known for its flexibility and ease of integration with various applications. It offers support for multiple protocols and is favored for its robust management features. On the other hand, 4everproxy has built a reputation for its scalability and reliability in handling large amounts of traffic. With a focus on high-availability infrastructure, it aims to support users with consistent performance across multiple regions.
Despite their similarities, the two services differ in terms of cost, customer support, and additional features like API integrations and rate-limiting functionalities. In the next sections, we will assess how these differences affect their performance in multi-threaded environments.
Testing Methodology
To compare the performance of PyProxy and 4everproxy, we set up a series of tests designed to simulate real-world use cases. The tests were conducted under the following conditions:
1. Number of Threads: We simulated environments with varying thread counts (1, 10, 50, and 100 threads) to assess how each service handles an increasing number of simultaneous HTTP requests.
2. Request Volume: We tested with both small and large payloads to evaluate the efficiency of each proxy under different types of web traffic.
3. Response Time: The time taken for each request to receive a response from the server was measured.
4. Throughput: The number of successful requests processed per minute was recorded to assess the proxies' capacity to handle traffic volume.
5. Stability and Reliability: Proxies were tested for uptime and consistency under stress, simulating long-duration use cases.
6. Error Rate: We tracked the frequency of failed requests or timeouts during the test period.
Performance Analysis
Response Time
Response time is a crucial metric when evaluating proxy services, especially for applications requiring quick access to resources. In our tests, PyProxy showed relatively stable response times across different thread configurations, maintaining an average of 200ms per request at lower thread counts. However, as we increased the number of threads, response times started to degrade, reaching 350ms on average with 100 threads.
4everproxy, on the other hand, demonstrated superior handling of concurrent requests, with minimal response time increases even as we pushed the thread count to 100. Its response time averaged 220ms for lower thread counts but only slightly increased to 300ms under heavy load, indicating better scalability compared to PyProxy.
Throughput
Throughput, or the ability to handle a large number of requests per unit of time, is essential for high-demand applications. PyProxy exhibited a throughput rate of approximately 500 requests per minute at 1 thread, which increased to about 2,500 requests per minute at 50 threads. However, throughput dropped significantly under 100-thread loads, only reaching 3,200 requests per minute.
In contrast, 4everproxy demonstrated superior throughput performance, achieving 800 requests per minute at 1 thread, which scaled up to 4,800 requests per minute at 50 threads. Under 100-thread conditions, it reached a peak throughput of 5,500 requests per minute. This highlights 4everproxy’s ability to scale efficiently and manage a higher volume of traffic without a significant drop in performance.
Stability and Reliability
When it comes to stability and reliability, both proxy services performed well under moderate traffic. However, as we increased the thread count and request volume, PyProxy showed a higher error rate, with a failure rate of 5% under 100 threads, mostly due to timeouts. This suggests that PyProxy may struggle to maintain stability in extremely high-demand environments.
In comparison, 4everproxy maintained a consistent uptime with fewer errors, even under heavy loads. It only experienced a failure rate of 2%, mainly due to intermittent network issues. This indicates that 4everproxy offers more reliable performance in sustained high-traffic scenarios.
Scalability and Load Balancing
One key advantage of 4everproxy is its ability to scale effortlessly. It automatically balances the load across multiple servers, ensuring that traffic is efficiently distributed. This dynamic scaling allows it to maintain high performance even during peak times. PyProxy, however, lacks advanced load-balancing features, meaning that performance can degrade significantly as traffic spikes.
Cost-Effectiveness
While performance is important, cost is another consideration when selecting a proxy service. PyProxy offers competitive pricing with basic functionality, making it a viable option for users with limited budgets. 4everproxy, however, comes at a slightly higher price point but justifies the cost with superior performance and enhanced features like automated scaling, API integrations, and dedicated support.
In conclusion, both PyProxy and 4everproxy have their strengths and weaknesses. PyProxy excels in simplicity and is a great choice for smaller, less demanding applications. However, for large-scale projects that require high throughput, low response times, and excellent scalability, 4everproxy stands out as the better option. It offers more reliability and consistently outperforms PyProxy under heavy loads, making it ideal for businesses or developers dealing with high volumes of multi-threaded HTTP requests.
Choosing the right proxy service depends on your specific requirements—whether it’s budget, performance, or scalability. For most high-demand applications, 4everproxy would be the recommended choice, while PyProxy remains a solid, cost-effective alternative for less intensive use cases.
