Rate Throttling Explained

Protect your APIs from abuse and overload — limiting request rates per client to ensure fair usage and system stability.

Rate Throttling

Rate throttling is a traffic management technique that limits the number of requests a client can make to an API within a time window, protecting services from abuse, overload, and ensuring fair resource usage.

Explanation

Rate throttling protects APIs from excessive usage — whether from abusive clients, buggy integrations, or DDoS attacks. Common algorithms include fixed window (count requests per time window), sliding window (smooth the window boundary), token bucket (allow bursts up to a limit), and leaky bucket (process at a steady rate). Responses include HTTP 429 (Too Many Requests) with Retry-After headers. Rate limits can apply per user, per API key, per IP, or globally. Different endpoints may have different limits based on their cost and sensitivity.

Bookuvai Implementation

Bookuvai implements rate throttling on all API endpoints using Redis-backed sliding window counters. We set per-endpoint limits based on expected usage, return clear 429 responses with Retry-After headers, and implement tiered limits for different API key tiers.

Key Facts

  • Limits requests per client within a time window
  • Algorithms: fixed window, sliding window, token bucket, leaky bucket
  • Returns HTTP 429 with Retry-After headers when limits are exceeded
  • Protects against abuse, DDoS, and ensures fair resource usage
  • Different limits per endpoint based on cost and sensitivity

Related Terms

Frequently Asked Questions

What is the difference between rate limiting and throttling?
The terms are often used interchangeably. Strictly, rate limiting rejects excess requests immediately (429 response). Throttling may queue or delay excess requests instead of rejecting them. In practice, most implementations reject with 429.
Where should rate limiting be implemented?
Implement at the API gateway for global protection, and at the application level for per-endpoint granularity. Cloud load balancers and CDNs can also enforce rate limits as a first line of defense before requests reach your application.
How do I choose rate limit values?
Start with expected usage patterns plus a safety margin. Monitor actual usage to tune limits. Set generous limits initially and tighten based on data. Different endpoints need different limits — an auth endpoint should have stricter limits than a read endpoint.