Designing Rate Limiting for APIs
Token bucket, leaky bucket, fixed and sliding window algorithms, plus the patterns for building rate limiters that work in distributed systems without falling over.
Designing Rate Limiting for APIs
Token bucket, leaky bucket, fixed and sliding window algorithms, plus the patterns for building rate limiters that work in distributed systems without falling over.
Why do you need rate limiting on an API even if your servers can handle the traffic?
Capacity is only one reason. The others bite harder in production: - A single buggy client in a retry loop can saturate a downstream database or a paid third-party API - Abusive scrapers and credential-stuffing bots cost real money on egress, CPU, and tokens - Without limits, one customer can degrade service for everyone (noisy neighbor) - Auth endpoints without limits turn into brute-force playgrounds Rate limiting is a fairness and safety control. You add it before you need it, not after the incident.
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