Rate limiting is a critical mechanism for managing API access by controlling the frequency of user requests to prevent system overuse, whether accidental or malicious. This practice is essential for maintaining service quality, especially for public APIs that handle computationally-intensive tasks or sensitive data, as it prevents resource starvation, manages costs, enforces user quotas, controls data flow, and acts as a security measure against various attacks like DoS and scraping. Several algorithms, including Leaky Bucket, Fixed Window, Sliding Log, and Sliding Window, offer different approaches to implementing rate limiting, each with its advantages and drawbacks in terms of handling request bursts, boundary conditions, and scalability across distributed systems. Implementing rate limiting in a distributed environment requires synchronization policies to prevent users from exceeding global limits when requests are sent to multiple nodes, often using a centralized data store to manage counters, although this can introduce latency and race conditions. Tools like Kong API Gateway facilitate the setup of scalable rate-limited services by providing configurable plugins that support different algorithms and data synchronization options, offering flexibility in managing API traffic and ensuring reliable performance even in large, distributed systems.