Rate Limiting

Benched.ai Editorial Team

Rate limiting controls the number of requests a client or tenant can issue to an AI API within a given time window. Proper limits protect shared infrastructure from abuse, manage fairness, and contain operating costs.

Common Algorithms

Strategy	How It Works	Pros	Cons
Fixed window	Counter resets every N seconds	Simple to implement	Burst at window edges
Sliding window log	Track timestamp list; drop old	Smooth, accurate	Memory grows with traffic
Token bucket	Tokens added at rate R; requests consume	Allows bursts up to bucket size	Additional state per key
Leaky bucket (queue)	Queue of N; dequeue at rate R	Constant outflow rate	Queued requests add latency

Recommended Limit Granularity

Per API key or OAuth client ID.
Distinct model tiers (GPT-4, GPT-3.5) with tighter caps on premium models.
Separate read (inference) vs write (fine-tune) endpoints.

Design Trade-offs

Generous burst capacity improves UX for interactive apps but may starve steady clients.
Low limits reduce abuse but push developers to parallelize across keys.
Header-based quotas are simple but can be spoofed without TLS termination.

Current Trends (2025)

Adaptive rate limits that raise ceilings for low-error clients and throttle noisy ones.
Usage-based billing replacing hard caps: pay-as-you-go beyond free tier.
Real-time quota introspection endpoints so apps can plan retries instead of polling.

Implementation Tips

Return 429 Too Many Requests with Retry-After header in seconds.
Include remaining tokens in response headers to aid client back-off logic.
Store counters in Redis or Memcached with Lua script for atomicity.
Log limit breaches; spikes often indicate bot attacks.