Proxyscrape.com free proxy
Stable Proxies:
IP address | Port | Country | Protocol | Verified |
---|---|---|---|---|
88.202.230.103 | 7392 | United Kingdom | socks | 12 hours ago |
94.23.220.136 | 56714 | France | socks | 22 hours ago |
5.75.206.99 | 80 | Germany | http | 22 hours ago |
95.111.227.164 | 53152 | Germany | socks | 1 day ago |
195.35.29.39 | 80 | Germany | http | 1 day ago |
134.209.67.109 | 8086 | United States | http | 1 day ago |
5.228.229.33 | 80 | Russian Federation | http | 1 day ago |
84.247.128.92 | 3128 | Germany | http | 1 day ago |
51.89.73.162 | 80 | United Kingdom | http | 1 day ago |
185.196.178.152 | 4145 | Austria | socks | 1 day ago |
5.180.45.235 | 80 | Cyprus | http | 1 day ago |
65.21.49.83 | 8080 | Finland | http | 1 day ago |
51.210.19.141 | 80 | France | http | 1 day ago |
155.133.57.22 | 4153 | Poland | socks | 2 day ago |
178.208.10.237 | 1080 | France | socks | 2 day ago |
82.65.220.152 | 80 | France | http | 2 day ago |
173.212.237.43 | 27004 | Germany | socks | 2 day ago |
91.235.69.164 | 8382 | Ukraine | http | 2 day ago |
91.202.25.154 | 9090 | Russian Federation | http | 2 day ago |
164.92.167.4 | 1194 | Germany | http | 2 day ago |
156.200.116.72 | 1976 | Egypt | http | 3 day ago |
130.180.208.145 | 80 | France | http | 3 day ago |
185.152.113.67 | 9999 | Slovakia | http | 4 day ago |
217.65.2.14 | 3333 | Russian Federation | http | 4 day ago |
194.26.229.46 | 20000 | Russian Federation | socks | 4 day ago |
172.64.165.79 | 80 | United States | http | 4 day ago |
172.67.72.188 | 80 | United States | http | 4 day ago |
194.26.229.46 | 20002 | Russian Federation | socks | 5 days ago |
194.26.229.46 | 20009 | Russian Federation | socks | 6 days ago |
161.123.116.54 | 22332 | Canada | http | 6 days ago |
37.186.66.36 | 3629 | Armenia | socks | 6 days ago |
85.21.233.231 | 1337 | Russian Federation | socks | 6 days ago |
172.67.160.84 | 80 | United States | http | 6 days ago |
172.67.165.174 | 80 | United States | http | 6 days ago |
172.66.0.96 | 80 | United States | http | 6 days ago |
91.107.143.161 | 3128 | Germany | http | 1 week ago |
91.201.119.198 | 1337 | Russian Federation | socks | 1 week ago |
89.133.137.45 | 4145 | Hungary | socks | 1 week ago |
77.68.77.181 | 80 | United Kingdom | http | 1 week ago |
92.205.110.118 | 35153 | France | socks | 1 week ago |
83.17.171.252 | 36099 | Poland | socks | 1 week ago |
173.212.223.23 | 28083 | Germany | socks | 1 week ago |
213.219.198.69 | 80 | Russian Federation | http | 1 week ago |
81.19.137.61 | 80 | France | socks | 1 week ago |
193.233.232.116 | 80 | Austria | socks | 1 week ago |
172.104.143.136 | 80 | Germany | http | 1 week ago |
78.30.191.213 | 8080 | Serbia | http | 1 week ago |
109.202.160.2 | 8888 | Russian Federation | http | 1 week ago |
13.51.234.32 | 8096 | Sweden | http | 1 week ago |
172.64.154.143 | 80 | United States | http | 1 week ago |
62.109.0.18 | 24101 | Russian Federation | socks | 1 week ago |
65.21.159.49 | 80 | Finland | http | 1 week ago |
77.232.128.191 | 80 | Russian Federation | http | 1 week ago |
46.105.44.110 | 80 | France | http | 1 week ago |
152.53.3.82 | 7891 | Austria | socks | 1 week ago |
152.53.3.82 | 7890 | Austria | https | 1 week ago |
172.64.144.179 | 80 | Netherlands | http | 1 week ago |
213.21.59.134 | 4153 | Russian Federation | socks | 1 week ago |
95.220.110.1 | 80 | Russian Federation | socks | 1 week ago |
93.177.67.178 | 80 | Germany | http | 1 week ago |
93.171.103.125 | 8080 | Russian Federation | http | 1 week ago |
104.244.76.38 | 80 | Luxembourg | http | 1 week ago |
34.175.101.255 | 80 | Spain | http | 1 week ago |
91.65.102.12 | 80 | Germany | http | 1 week ago |
178.252.170.222 | 3128 | Iran | http | 1 week ago |
195.231.69.203 | 80 | Italy | http | 1 week ago |
172.67.75.35 | 80 | United States | http | 1 week ago |
172.67.128.150 | 80 | United States | http | 1 week ago |
172.66.40.144 | 80 | United States | http | 1 week ago |
172.64.145.59 | 80 | United States | http | 1 week ago |
172.67.165.123 | 80 | United States | http | 1 week ago |
172.67.162.240 | 80 | United States | http | 1 week ago |
79.121.102.227 | 8080 | Hungary | http | 1 week ago |
37.140.31.63 | 80 | Russian Federation | http | 1 week ago |
159.69.206.198 | 80 | Germany | http | 1 week ago |
185.146.173.59 | 80 | Sweden | http | 1 week ago |
103.130.145.169 | 80 | Turkey | http | 1 week ago |
79.174.12.190 | 80 | Russian Federation | http | 1 week ago |
41.65.236.53 | 1976 | Egypt | http | 1 week ago |
172.64.145.223 | 80 | United States | http | 1 week ago |
91.148.127.60 | 8080 | Serbia | http | 1 week ago |
178.254.42.248 | 80 | Germany | http | 1 week ago |
172.67.182.221 | 80 | United States | http | 1 week ago |
172.67.68.245 | 80 | Netherlands | http | 1 week ago |
172.64.194.218 | 80 | United States | http | 1 week ago |
45.81.232.17 | 8191 | Germany | socks | 2 weeks ago |
66.235.200.122 | 80 | United States | http | 2 weeks ago |
172.67.163.136 | 80 | United States | http | 2 weeks ago |
77.221.136.210 | 80 | Sweden | http | 2 weeks ago |
79.124.77.148 | 3128 | Bulgaria | http | 2 weeks ago |
176.10.111.23 | 80 | Switzerland | http | 2 weeks ago |
185.244.210.185 | 80 | France | http | 2 weeks ago |
95.110.227.85 | 3128 | Italy | http | 2 weeks ago |
147.45.104.252 | 80 | Russian Federation | http | 2 weeks ago |
206.189.12.206 | 80 | Netherlands | http | 2 weeks ago |
77.221.140.172 | 5556 | Sweden | socks | 2 weeks ago |
89.109.53.26 | 8123 | Russian Federation | http | 2 weeks ago |
217.160.99.39 | 80 | Germany | http | 2 weeks ago |
172.67.146.14 | 80 | United States | http | 2 weeks ago |
45.81.232.17 | 25306 | Germany | socks | 2 weeks ago |
Fast and stable proxies
Russia |
100 IP
19$
|
500 IP
69$
|
1 000 IP
99$
|
3 000 IP
249$
|
5 000 IP
399$
|
USA |
100 IP
25$
|
500 IP
69$
|
1 000 IP
99$
|
3 000 IP
249$
|
5 000 IP
399$
|
Great Britain |
100 IP
19$
|
500 IP
69$
|
1 000 IP
99$
|
3 000 IP
249$
|
5 000 IP
399$
|
Germany |
100 IP
19$
|
500 IP
69$
|
1 000 IP
99$
|
3 000 IP
249$
|
5 000 IP
499$
|
Europe |
100 IP
19$
|
500 IP
69$
|
1 000 IP
99$
|
3 000 IP
229$
|
5 000 IP
379$
|
China |
100 IP
19$
|
500 IP
69$
|
1 000 IP
99$
|
3 000 IP
249$
|
5 000 IP
Sold out
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Ukraine |
100 IP
19$
|
500 IP
69$
|
1 000 IP
99$
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3 000 IP
Sold out
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5 000 IP
Sold out
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Australia |
100 IP
19$
|
500 IP
69$
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1 000 IP
99$
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3 000 IP
Sold out
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5 000 IP
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Canada |
100 IP
19$
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500 IP
69$
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1 000 IP
99$
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3 000 IP
Sold out
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5 000 IP
Sold out
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Netherlands |
100 IP
19$
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500 IP
69$
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1 000 IP
99$
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3 000 IP
Sold out
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5 000 IP
Sold out
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France |
100 IP
19$
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500 IP
69$
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1 000 IP
99$
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3 000 IP
Sold out
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5 000 IP
Sold out
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Turkey |
100 IP
19$
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500 IP
69$
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1 000 IP
99$
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3 000 IP
Sold out
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5 000 IP
Sold out
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India |
100 IP
19$
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500 IP
69$
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1 000 IP
99$
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3 000 IP
Sold out
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5 000 IP
Sold out
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Poland |
100 IP
19$
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500 IP
69$
|
1 000 IP
99$
|
3 000 IP
Sold out
|
5 000 IP
Sold out
|
Spain |
100 IP
19$
|
500 IP
69$
|
1 000 IP
99$
|
3 000 IP
Sold out
|
5 000 IP
Sold out
|
Mix of countries |
1 000 IP
99$
|
3 000 IP
229$
|
5 000 IP
379$
|
10 000 IP
729$
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15 000 IP
1049$
|
FAQ
What are residential proxies and when you should use them?
Residential proxies are proxy servers that use real IP addresses assigned to physical devices, such as computers or routers. They are often used to hide the IP address of the user and to bypass internet restrictions, such as blocking or filtering of websites.
Residential proxies are generally more anonymous than datacenter proxies, as they use real IP addresses that are assigned to physical devices. They are also generally more expensive and less stable than datacenter proxies.
Residential proxies are commonly used for tasks such as web scraping, online marketing, and search engine optimization (SEO). They are also often used to bypass geolocation restrictions and to access blocked websites or content.
In summary, residential proxies are proxy servers that use real IP addresses assigned to physical devices. They are generally more anonymous than datacenter proxies, but are also generally more expensive and less stable. They are commonly used for tasks such as web scraping, online marketing, and SEO, and are also often used to bypass geolocation restrictions and access blocked websites or content.
Why would you scrape Instagram followers’ emails?
There are several reasons why someone might want to scrape the emails of Instagram followers:
1. Marketing: Businesses and marketers can use email addresses to send targeted marketing campaigns to Instagram followers.
2. Lead Generation: Email addresses can be used to generate leads for sales purposes. By scraping the emails of Instagram followers, businesses can reach out to potential customers directly and offer their products or services.
3. Research: Researchers can use email addresses to conduct surveys or other types of research on Instagram users.
4. Social Media Analytics: Email addresses can be used to analyze social media activity and understand the behavior of Instagram users.
It's important to note that scraping emails from Instagram followers without their consent is illegal and unethical. It's always recommended to obtain permission before collecting and using someone's personal information, such as their email address.
Ways to scrape YouTube comments?
There are several ways to scrape comments from YouTube videos, including the following:
1. YouTube API: You can use the YouTube API to retrieve comments from YouTube videos. However, you will need an API key and some programming skills to use the API.
2. Web Scraping: You can scrape comments from YouTube videos by writing a script to extract the comments from the HTML source code of the video page. This requires some programming skills, but it is a flexible method that can be done in any programming language.
3. Third-party tools: There are also third-party tools that can scrape comments from YouTube videos for you. Some of these tools are free and some are paid, and they often require little to no technical knowledge to use.
Regardless of the method you choose, it is important to be aware of the terms of service for YouTube, which prohibit the use of automated means to scrape comments from the site. It is also important to respect users' privacy and only use this information for legitimate purposes.
Why Scrape Instagram Followers?
People or organizations might scrape Instagram followers for a variety of reasons. Some of the reasons for scraping Instagram followers include:
1. Market research: Understanding the demographics and interests of a target audience can help companies improve their marketing strategies. Scraping Instagram followers can give insight into the age, location, gender, and interests of a user's followers.
2. Competitive analysis: By scraping the followers of competitors, businesses can see what works for their competitors and what doesn't. This information can be used to develop better marketing strategies and improve their own social media presence.
3. Influencer marketing: Scraping Instagram followers can help businesses identify potential influencers to partner with. By analyzing the followers of influencers, businesses can get a better understanding of the target audience they are reaching through influencer marketing.
4. Data analysis: Scraping Instagram followers can provide a large amount of data that can be analyzed to uncover patterns and trends. This information can be used to make data-driven decisions about marketing and business strategy.
It's worth noting that scraping Instagram followers is not officially supported by Instagram, and the platform has taken steps to prevent scraping in the past. Additionally, the use of automated tools to scrape data from the platform may violate Instagram's terms of service. Before scraping Instagram followers, it's important to carefully review and understand Instagram's policies to ensure that you're not breaking any rules.
Structured vs. unstructured data
Structured and unstructured data refer to the way that data is organized and stored.
Structured data is organized in a well-defined format, such as a database table, CSV file, or spreadsheet. It typically consists of rows and columns, with each column representing a specific type of information and each row representing a single record. Structured data is easy to search, sort, and analyze because it has a clear structure and is easily accessible by software applications.
Unstructured data, on the other hand, has no specific structure or format. It can take the form of images, audio files, videos, text documents, emails, or any other type of digital content that cannot be easily organized into a structured format. Unstructured data is often difficult to manage, process, and analyze because it does not have a clear structure or organization.
In practice, many data sets contain a mix of structured and unstructured data. For example, a database may store structured data about customer orders, while also storing unstructured data such as customer feedback in the form of text documents or email messages.
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