10 min read
Every website, application, and API has a limit to how much traffic it can handle at one time. When too many requests hit a server within a short period, performance issues and security risks quickly follow. To prevent this, many platforms rely on a technique known as rate limiting. So, what is rate limiting? Let’s find out!
What is rate limiting?Link to heading
Rate limiting is a method used to control the volume of network traffic and prevent users from draining system resources. By capping how many requests a user or client can make within a given timeframe, it becomes significantly harder for malicious actors to overwhelm a system and launch attacks such as Denial of Service (DoS), where an attacker floods a target with requests until it runs out of network capacity, memory, or storage.
For APIs, rate limiting means any client that sends too many requests in a short period can be slowed down or blocked entirely. A throttled client may experience delayed responses for a set duration, or have their requests refused outright. Either way, the goal is the same: ensure legitimate traffic can reach the system and retrieve data without degrading the overall performance of the application.
Why is rate limiting important?Link to heading
Rate limiting plays a central role in any modern cybersecurity strategy. It directly addresses several categories of attack that exploit high volumes of incoming requests. Understanding what is rate limiting and why it matters is the first step toward building a more resilient system.
Distributed Denial of Service (DDoS)Link to heading
A DDoS attack works by bombarding a target system with so much traffic that it becomes unavailable to real users. Rate limiting reduces this risk by preventing any single traffic source from sending an excessive number of requests.
That said, DDoS attacks present a unique challenge, they spread requests across a large number of sources, sometimes millions of different IP addresses, so that no single source triggers the rate limit on its own. An effective security solution must be able to recognize these distributed requests as part of one coordinated attack and treat them as a unified threat.
Credential StuffingLink to heading
When attackers gain access to a compromised database of user credentials, they can deploy bots to systematically insert those stolen username and password combinations into login forms until one set grants access to an account.
These bots are particularly dangerous because they can test hundreds or thousands of credentials in a matter of minutes. Rate limiting helps detect this abnormal login activity and block the bots before they successfully take over user accounts.
Brute Force attacksLink to heading
A brute force attack follows the same logic as credential stuffing, except the attacker does not have a list of real credentials to work from. Instead, a bot generates and submits random credential combinations repeatedly until it finds one that works.
If you want to fully understand what is rate limiting, it is important to recognize how effective it is against brute force attacks. Even when strong password policies are in place, these attacks can still consume significant server resources. Rate limiting helps stop repeated login attempts early and protects overall system performance.
Data Scraping and TheftLink to heading
Malicious actors frequently target websites to extract data they can sell, exploit, or use to gain a competitive edge, such as stealing product pricing from an eCommerce platform. Automated scraper bots can pull large volumes of data from web applications at high speed. Rate limiting detects unusual request patterns consistent with scraping behavior and blocks those bots before significant data can be extracted.
Inventory DenialLink to heading
An inventory denial attack, sometimes called inventory hoarding, involves deploying bots to a web application where they initiate transactions but never complete them. The bots effectively lock up available inventory, making it inaccessible to genuine customers. Rate limiting identifies and stops this kind of abuse, keeping inventory available for legitimate users.
How does rate limiting work?Link to heading
To fully understand what is rate limiting, it helps to look at the mechanics behind it. Rate limiting operates at the application layer, not at the web server level. The core mechanism relies on tracking the IP addresses from which requests originate and measuring the time elapsed between each one. IP addresses serve as the primary identifier, allowing the application to determine who is making each request and how frequently.
A rate-limiting solution continuously monitors two things: the time gap between consecutive requests from a given IP address, and the total number of requests that IP has made within a defined window. When a single IP address exceeds the allowed request count within that window, the solution throttles it, meaning its subsequent requests are denied until the next timeframe begins.
In practical terms, a rate-limited application essentially tells overactive users to slow down, much like a police officer pulling over a driver for speeding, or a parent cutting off a child who has had too much sugar in one sitting.
Types of rate limitsLink to heading
Administrators have several parameters and approaches available when configuring a rate limit. The right method depends on the organization's specific objective and the level of restriction required. Three primary approaches are commonly used:
- User rate limits are the most widely adopted method. The system tracks how many requests a specific user makes, typically by monitoring their IP address or API key. Once a user crosses the defined threshold, the application blocks any further requests until the timeframe resets. Users who need a higher allowance can reach out to the development team to have their limit adjusted.
- Geographic rate limits allow developers to apply region-specific restrictions for added protection. For instance, if users in a particular region are expected to be largely inactive between midnight and 9:00 am, developers can set a lower request ceiling during those hours. This reduces the risk of suspicious traffic patterns going undetected during off-peak periods.
- Server rate limits are set at the infrastructure level when specific servers are assigned to handle particular parts of an application. This approach gives developers greater flexibility, heavily used servers can be allocated a higher request limit, while quieter servers can be given a tighter cap to conserve resources.
What are the algorithms used for rate limiting?Link to heading
A complete explanation of what is rate limiting would not be complete without discussing the algorithms behind it. Different algorithms are designed to balance performance, flexibility, and fairness in different ways.
Fixed-window rate limitingLink to heading
The fixed-window algorithm restricts the number of requests permitted within a set timeframe. For example, a server might allow up to 200 API requests per minute. This window is anchored to a predetermined start time, the server accepts no more than 200 requests between 9:00 and 9:01, after which the window resets and another 200 requests are permitted until 9:02.
This algorithm can be applied at either the user or server level. At the user level, each individual is capped at 200 requests per minute. At the server level, that same cap applies to all users collectively, meaning the entire user base shares the 200-request allowance.
Leaky bucket rate limitingLink to heading
The leaky bucket algorithm takes a different approach by removing fixed timeframes from the equation entirely. Instead, it focuses on maintaining a queue of fixed length, processing requests on a first-come, first-served basis. Each new request joins the back of the queue, and if a request arrives when the queue is already full, it is dropped. The emphasis here is on consistent throughput rather than time-based windows.
Sliding-window rate limitingLink to heading
The sliding-window algorithm functions similarly to the fixed-window model, with one key difference: the timeframe does not start at a predetermined time, it starts the moment a user submits their first request. If a request arrives at 9:00:24 am and the limit is 200 per minute, the server permits up to 200 requests until 9:01:24 am.
This approach resolves some of the edge-case problems that arise with fixed-window rate limiting, and it also addresses the throughput starvation that can occur with the leaky bucket model, offering a more balanced and flexible solution overall.
Common rate limiting mistakes to avoidLink to heading
Incorrectly implementing rate limiting can leave a system both vulnerable to attacks and frustrating for legitimate users. For teams still exploring what is rate limiting and how to apply it correctly, the following are some of the most common mistakes developers and system administrators make.
Setting thresholds too low or too highLink to heading
This is the most fundamental mistake. If the rate limit threshold is set too low, legitimate users may be blocked unfairly, resulting in a poor user experience and a higher bounce rate. On the other hand, if the threshold is too high, rate limiting becomes almost meaningless, as bots and attackers can continue operating freely without significant restrictions.
Only rate limiting by IP addressLink to heading
Many systems rely solely on IP addresses to identify the source of requests. This creates a serious weakness because attackers can easily rotate through hundreds of different IPs using proxies or botnets to bypass restrictions without being detected. In addition, multiple legitimate users may share the same IP address, for example, within a corporate network or behind NAT, which can lead to false positives and unintended blocks.
Applying the same rate limit across all endpointsLink to heading
Not all endpoints have the same sensitivity or usage patterns. Applying a single rate limit across an entire API is a common mistake. A product search endpoint may normally receive thousands of requests per minute, while a login endpoint should only allow a limited number of attempts within a certain timeframe.
Not returning informative error responsesLink to heading
When users or systems are rate-limited, many servers simply return an HTTP 429 response without providing additional details. This leaves client-side developers uncertain about how long they should wait before retrying. As a result, clients may continue sending repeated requests, further worsening the situation.
Failing to account for distributed and coordinated attacksLink to heading
A rate limiting system that only works effectively at the single-server level can become ineffective in distributed environments. If an application runs across multiple servers or geographic regions and each server tracks requests independently, attackers can exploit this by distributing traffic evenly across different nodes, allowing malicious activity to go undetected.
Not testing rate limiting before going liveLink to heading
Many teams deploy rate limiting configurations without thoroughly testing them before production release. This often leads to rules that either fail to work as intended or mistakenly block legitimate users immediately after deployment.
Ignoring rate limiting for internal servicesLink to heading
One commonly overlooked mistake is applying rate limiting only to external traffic while ignoring microservices or internal API calls. If an internal service malfunctions or becomes compromised, it may send uncontrolled requests to other services, triggering a cascading failure that can eventually bring down the entire system.
ConclusionLink to heading
Understanding what is rate limiting is essential for anyone responsible for managing websites, APIs, or online applications. More than just a traffic control mechanism, rate limiting plays a critical role in protecting systems from brute force attacks, credential stuffing, scraping bots, and large-scale DDoS attempts. When configured properly, it helps maintain stable performance, reduces unnecessary server load, and ensures legitimate users can access services without disruption.
For WordPress websites, combining rate limiting with a dedicated firewall is one of the most effective ways to reduce security risks and block malicious traffic. W7SFW is a WordPress firewall designed to protect websites from brute force attacks, bot abuse, suspicious requests, and various common web threats through an external filtering layer.
Instead of relying solely on traditional security plugins inside WordPress, W7SFW helps stop harmful traffic early without requiring complex configuration or code changes. If you are running a WordPress website, activating W7SFW is a simple but powerful step toward improving security, maintaining stable performance.