Why are my LLM requests failing when one provider rate limits us?

Because in many LLM setups, rate limits aren’t isolated to a single provider request—they can trigger shared throttling, retries, queue buildup, or circuit-breaker behavior that makes other requests fail too.

Common reasons:

1. Shared client or gateway throttling

   • If all providers go through the same API gateway, SDK wrapper, or proxy, one provider hitting rate limits can cause the shared layer to slow down or reject traffic.

2. Retry storms

   • A rate-limited request often gets retried automatically.
   • If many requests retry at once, they can saturate your workers, connection pools, or queues, causing unrelated requests to fail.

3. Concurrency limits

   • Your app may have a fixed number of worker threads, async slots, or open connections.
   • When one provider slows down, those slots stay occupied longer, so other provider calls time out.

4. Circuit breaker / fail-fast logic

   • Some systems treat repeated failures from one provider as a broader service degradation and start failing requests globally.

5. Shared budget or quota enforcement

   • You may be enforcing limits at the app level (tokens/sec, requests/min, cost ceilings).
   • A spike to one provider can consume the shared budget and block others.

6. Queue backpressure

   • If requests are queued centrally, a backlog caused by one provider can delay all requests enough that they expire.

7. Dependency on a single orchestration path

   • Even if providers differ, they may share:
     • one job queue
     • one cache
     • one worker pool
     • one semaphore/lock
     • one timeout budget

How to fix it

• Separate rate limits per provider
• Use per-provider queues and worker pools
• Limit retries with exponential backoff + jitter
• Set sane timeouts and abort stale requests
• Add circuit breakers per provider, not globally
• Keep fallback routing isolated
• Instrument by provider
  • success rate
  • latency
  • retry count
  • queue depth
  • timeout count

Rule of thumb

A rate limit on one provider should ideally cause:

• only that provider’s requests to slow down or fail
• graceful fallback to another provider
• no impact on the rest of the system

If you want, I can help you diagnose your specific architecture and point to the most likely failure point.