5 min read · Updated June 10, 2026
How to Rate Limit a Next.js AI API
To rate limit a Next.js AI API, place the limit inside the server endpoint before the paid provider call. Identify the caller using an authenticated user ID when possible, select a limit that matches the cost of the model and plan, reject excess requests with HTTP 429, and record enough usage data to investigate abuse. A distributed rate limiter is preferable when the application runs across multiple serverless instances because every instance must share the same counters.
Where should rate limiting happen?
The rate-limit check must happen before the expensive AI request. If the application calls the provider first and checks usage afterward, the cost has already been incurred. The protected server endpoint should authenticate, validate, rate limit, and only then call the provider.
What identifier should an AI API limit use?
An authenticated user ID is usually more reliable than an IP address because users can share networks and attackers can rotate addresses. IP-based limits are still useful as an additional unauthenticated or abuse-prevention layer.
- Use user ID for account and plan limits.
- Use IP address as a secondary abuse signal.
- Use endpoint or model identifiers for cost-sensitive limits.
- Track failed and successful requests separately when useful.
Which rate-limit algorithm fits AI requests?
Fixed windows are simple but can allow bursts around the window boundary. Sliding windows produce smoother limits. Token buckets allow controlled bursts while enforcing a longer-term rate. The correct choice depends on user experience, model cost, and how much burst traffic the provider can tolerate.
Upstash documents fixed-window, sliding-window, and token-bucket strategies in its TypeScript rate-limit SDK. Whatever implementation you choose, return a clear HTTP 429 response and avoid revealing sensitive infrastructure details.