Throttling
Intentionally slowing or spacing requests to stay within rate limits or avoid overwhelming a downstream system.
Throttling controls the pace of requests so you do not overload downstream systems or violate rate limits. It spaces out calls to keep services stable.
Teams use throttling for APIs, webhooks, email sends, and batch jobs—anywhere bursts can trigger errors or bans. It smooths traffic and protects shared resources.
In workflows, throttling sits at queue consumers or HTTP clients. The benefit is predictable performance, fewer retries, and happier partners.
Frequently Asked Questions
When should I throttle requests?
When downstream rate limits exist, when bursts cause failures, or when shared resources need protection. Prefer proactive throttling over reacting to 429s.
How do I choose a throttle rate?
Use published limits or observed safe rates. Start conservative, monitor errors/latency, and adjust gradually.
What’s the difference between throttling and rate limiting?
Rate limiting is a cap imposed by a service; throttling is your strategy to stay under that cap by controlling throughput.
How do I combine throttling with retries?
Back off on retry and respect throttle ceilings. Don’t retry immediately into the same limit; add jitter to avoid bursts.
Should throttling be global or per-tenant?
Prefer per-tenant or per-key limits to prevent one tenant from starving others. Also keep a global safety cap.
How do I monitor throttling effectiveness?
Track request rate, 429/5xx errors, latency, and backlog. Adjust if errors rise or queues grow.
Can throttling live in the client?
Yes—implement in clients or worker pools to spread load before hitting the server. Also handle server-sent rate-limit headers.
Does throttling impact SLAs?
It can increase latency. Balance throttle rates with SLA targets, and scale workers if needed to meet both.
What patterns implement throttling?
Token bucket, leaky bucket, fixed-window, and concurrency caps. Choose based on burst tolerance and simplicity.
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