How would you handle model rate limits and backpressure?

Controls admission, concurrency, queues, tenant fairness, token budgets, and overload behavior before provider throttling cascades.

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TL;DR

I control admission before starting expensive work, limit concurrent calls, and maintain bounded queues for tasks that can wait. Capacity accounts for both requests and token volume because a few large prompts can consume a quota faster than many small ones. When capacity is exhausted, the system returns a clear retryable response, defers eligible background work, or degrades instead of accepting an unbounded backlog.

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Capacity management, fairness, bounded queues, throttling, and graceful overload.

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Core answer
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Overview
Provider limits are downstream capacity constraints, not errors to solve with unlimited local queues.
I control admission before starting expensive work, limit concurrent calls, and maintain bounded queues for tasks that can wait. Capacity accounts for both requests and token volume because a few large prompts can consume a quota faster than many small ones. When capacity is exhausted, the system returns a clear retryable response, defers eligible background work, or degrades instead of accepting an unbounded backlog.
Checkpoint
Stronger answer
Optional upgrade with examples, trade-offs, and sharper phrasing.
Per-tenant quotas and fair scheduling prevent one customer or batch from starving interactive traffic. Retry-after guidance, exponential backoff, and jitter coordinate recovery, while local token buckets smooth bursts below provider limits. Queue age and remaining deadlines determine whether waiting work is still useful before it reaches the provider.

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Details

Queuing every request until the provider recovers.
Limiting request count but ignoring token volume.
Allowing batch work to consume interactive capacity.

How would you handle model rate limits and backpressure?

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