Rate Limiting

Rate limiting is a protective mechanism. Without it, one misbehaving user or application can consume all your resources, starving other users. Rate limiting ensures fair access and prevents abuse.

There are multiple types of rate limits. Per-user limits: each user can make 100 requests per hour. Per-IP limits: each IP address can make 1,000 requests per hour (useful when you don't have user authentication). Per-API-key limits: each API key (representing an application or organization) has its own quota. Global limits: the system can handle 1 million requests per hour total; if you exceed that, requests get rejected.

Rate limiting serves multiple purposes. It prevents abuse (one user can't monopolize the system). It prevents overload (sudden traffic spikes don't crash the system). It protects against cost surprises (if you're using APIs, you control spending by limiting requests).

Implementation can be simple (reject requests that exceed the limit) or sophisticated (queue them and process later). Queuing is better for the user experience (instead of immediate rejection, requests get processed when capacity is available), but requires more infrastructure.

Adaptive rate limiting adjusts limits based on current load. When the system is under-utilized, allow more requests. When it's at capacity, reduce limits. This maximizes utilization while preventing overload.

Cost-based rate limiting is increasingly common for AI. If you're using APIs, you want to limit based on cost rather than just number of requests. One request might cost $0.001 and another might cost . You might have a monthly budget: once you've spent 000, no more requests until next month.

Token-based rate limiting is relevant for LLM APIs. A request that uses 100 tokens costs less than one using 1,000 tokens. You might limit per token, not per request.

The challenge is balancing fairness and efficiency. If you have 1000 customers and 10 have very high demand while 990 are light users, how do you allocate capacity? Do you give each customer an equal share (fair but potentially wasteful)? Or do you give high-demand customers higher limits (efficient but less fair)?

Rate limiting can be transparent (users know their limits and can see current usage) or opaque (users don't know the limits until they hit them). Transparent is better for user experience and reduces surprises.

There's also the question of granularity. Should you limit requests per second, per minute, per hour? Different choices affect how the system behaves. Very strict per-second limits create a staccato effect where requests go in bursts. More lenient limits allow smoother traffic.

Graceful degradation is important. Instead of hard rejections (your request is denied), you might use backpressure (your request is accepted but will be processed more slowly) or fallback (route to a lower-quality but faster service).