How to Fix Rate Limiting Not Working in AI-Built Apps
Your API rate limiting is ineffective, leaving endpoints vulnerable to abuse. AI-generated rate limiters often use in-memory stores that reset on deploy or don't work across instances.
Error messages you might see
Error: Too Many Requests - rate limit exceededWarning: Rate limiter store is not shared across cluster workersWhy this happens in AI-generated code
In-memory store in serverless
AI code uses in-memory rate limit stores that reset with each serverless function invocation.
Missing rate limit on critical endpoints
AI applies rate limiting to some routes but misses authentication, payment, or webhook endpoints.
Incorrect key identification
Rate limiting by IP alone fails behind proxies where all requests share the same forwarded IP.
How to fix it
Use Redis-backed rate limiting
Store rate limit counters in Redis so they persist across deployments and are shared across instances.
Rate limit by authenticated user
Use user ID or API key as the rate limit key instead of IP to prevent bypass via proxy rotation.
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Frequently asked questions
What rate limits should I set?
For public APIs, 100 requests per minute is common. For auth endpoints, use stricter limits like 5-10 attempts per minute.
How do I test rate limiting?
Use tools like `autocannon` or `ab` to send burst traffic and verify that requests are rejected after the threshold.
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