IP blocking is a security measure websites use to restrict access from specific Internet Protocol (IP) addresses. When a site detects suspicious activity or too many requests from one IP address, it can blacklist that address and prevent any further access from that source.
For anyone doing web scraping, IP blocking is one of the most common obstacles you will encounter. Understanding how it works helps you build more reliable data collection workflows.
How websites detect and block IP addresses
Websites use sophisticated tracking systems to identify automated traffic. These systems create a trust score for every connection by analyzing your IP address and its metadata.
The key factors that affect your trust score include:
- Autonomous System Number (ASN): Every registered IP owner has a unique identifier. If multiple IPs from the same ASN behave suspiciously, the trust score for all IPs in that network drops.
- Address type: Datacenter IPs get lower trust scores than residential IPs because bots commonly use datacenter addresses.
- Geographic location: Requests from the same country as the website typically receive higher trust scores.
- Subnet information: IP addresses come in blocks of 256 addresses. When one address in a block shows suspicious activity, it can hurt the reputation of the entire block.
Common triggers for IP blocks
Your IP address can get blocked when websites detect:
- Too many requests: Sending hundreds of requests per minute from a single IP looks like bot activity or a potential attack.
- Non-human patterns: Clicking through pages faster than any person could, or accessing pages in an unnatural sequence.
- Terms of service violations: Accessing restricted content or ignoring the rules in a site's robots.txt file.
- Repeated failed requests: Multiple attempts to access non-existent pages or submit invalid forms.
Rate limiting vs. IP blocking
Before blocking you completely, many websites first use rate limiting. This means they slow down your requests instead of cutting off access entirely. You might see slower response times or temporary errors asking you to wait before trying again.
Think of rate limiting as a warning. If you keep pushing after hitting rate limits, a full IP block usually follows.
How to avoid IP blocks when scraping
The most effective strategies focus on making your scraping activity look like normal human traffic:
Rotate your IP addresses: Using different IP addresses for each request prevents pattern detection. This makes your requests appear to come from multiple users instead of one bot.
Use residential proxies: Residential IPs from actual internet service providers get higher trust scores than datacenter IPs. They cost more but trigger far fewer blocks.
Add delays between requests: Spacing out your requests by a few seconds mimics human browsing behavior. Rapid-fire requests are a dead giveaway.
Match your location: Use IP addresses from the same country as your target website. A request to a French website from a French IP looks more legitimate than one from a random datacenter in another continent.
Rotate user agents: Change your browser identifier with each request so you do not look like the same visitor hitting the site repeatedly.
What to do if you get blocked
If your IP is already blocked, you have several options:
- Switch to a different IP address through a proxy service or VPN
- Wait it out, as some blocks are temporary and lift after a few hours or days
- Contact the website directly if you have a legitimate use case for accessing their data
How Browse AI handles IP blocking for you
Managing proxies, rotating IPs, and handling blocks requires significant technical work. Browse AI takes care of all this automatically. The platform manages IP rotation and proxy infrastructure behind the scenes, so you can focus on extracting the data you need without worrying about getting blocked.
Browse AI's no-code approach means you do not need to set up proxy pools or write scripts to handle rate limiting. You simply point the tool at the data you want, and it handles the technical challenges of reliable data extraction.
