Rate limiting is what keeps your APIs stable under pressure. It helps to control how many requests a user or system can make, especially when working with heavy AI models. This guide walks through how API rate limiting works and how you can implement it in real-world systems. Exploring common strategies and learning how to handle the rate limit and errors helps you across different stacks. How to Implement Rate Limiting in an API (Step by Step) Step 1: Define what you want to limit Start by selecting the key used to track requests. Which are usually: IP address (simple, but less accurate) User ID (better for authenticated systems) API key (common for AI APIs) For an AI system, API keys or user IDs give more control and fairness. Step 2: Set a clear rate limit policy Decide the number of requests you want within a particular time. Examples: 100 requests per minute per user 1,000 requests per hour per API key Keep the limits realistic.…