Rate limiting is the first line of defense against API abuse. It limits the number of requests a client can make in a time period, protecting your infrastructure from brute force attacks, DDoS, and scraping.
Rate Limiting algorithms
Token Bucket: tokens are added to a bucket at a constant rate. Each request consumes a token. If the bucket is empty, the request is rejected. Allows controlled bursts.
Sliding Window: maintains a log of request timestamps within a sliding window. More accurate than Fixed Window but requires more memory. Efficiently implemented with Redis sorted sets.
Leaky Bucket: requests are processed at a constant rate. If the bucket overflows, requests are rejected. Ideal for protecting upstream systems with limited capacity.
Practical implementation
Express-rate-limit for Node.js, Flask-Limiter for Python, or custom implementations with Redis. In API Gateways like Kong, NGINX, or Cloudflare, rate limiting is configured at the proxy layer.
Design considerations
Use standard headers: X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset. Implement exponential backoff on the client side. Differentiate limits by endpoint (login more restrictive than public GET).
Rate limiting is essential for any public API. At Vynta, we implement rate limiting strategies that protect your API without affecting legitimate users.