The Need for Rate Limiting
Unbounded context access leads to system instability. A single runaway client can degrade performance for everyone. Rate limiting protects system stability while ensuring fair resource allocation across consumers.
Rate Limiting Strategies
Fixed Window
Simple to implement: allow N requests per time window. Risk of thundering herd at window boundaries—all clients simultaneously becoming unblocked.
Sliding Window
Smooth request distribution by considering rolling time periods. More complex implementation but eliminates window boundary spikes.
Token Bucket
Clients accumulate tokens over time, spend tokens on requests. Allows bursting within limits while maintaining long-term rate control. Widely applicable and well-understood.
Implementation Considerations
Implement rate limiting as close to the edge as possible—reject unwanted traffic early. Use distributed counters for horizontally scaled services. Return clear rate limit headers so clients can self-throttle.
Intelligent Throttling
Beyond simple rate limits, implement priority-based throttling. Critical operations proceed while background tasks wait. Degrade gracefully under load—serve cached context, skip non-essential enrichment, queue non-urgent updates.
Monitoring and Adjustment
Monitor rate limit utilization across clients. Identify legitimate high-volume users versus abuse. Adjust limits based on system capacity and business requirements. Implement automated scaling triggers when limits are consistently hit.
