Rate Limits, Quotas, and Abuse Prevention
AI endpoints can be abused to consume expensive tokens, flood providers, or extract data at scale. Apply per-user and per-tenant rate limits, maximum input/output sizes, request timeouts, concurrency limits, and budget alerts. Use idempotency for retried jobs and queue bulk processing instead of allowing unbounded fan-out.
Rate limits should account for model cost and workload, not just request count. A 100-token classification request and a large multimodal request have different resource profiles. Return retry guidance for throttling and avoid exposing provider credentials or internal quota details unnecessarily.
Quota policy sketch
const limits = {
requestsPerMinute: 20,
maxInputCharacters: 20_000,
maxOutputTokens: 1_000,
maxConcurrentJobs: 3
};
// Enforce server-side per user/tenant, before provider invocation.💡 These are illustrative values, not universal defaults. Derive limits from provider quotas, product requirements, user tiers, abuse patterns, and measured cost per request.
Key Takeaways
- Limit both frequency and request size.
- Track cost by user, tenant, feature, and model.
- Queue expensive batch work and set budget alerts.
Knowledge Check
Why is request-count-only rate limiting insufficient for AI workloads?