Managing Rate Limits Across Multiple LLM Providers

Why can a request fail below the advertised request limit?

Suppose an illustrative account allows 60 requests and 120,000 input tokens per minute. At 5,000 input tokens per request, the token allowance permits only 24 requests per minute before other restrictions. Designing around 60 requests would overload it.

Long output reservations may also affect admission, depending on the service. Consult the applicable Claude rate-limit documentation or Gemini limits, rather than treating providers as interchangeable.

How should a shared queue work?

Estimate the request’s token demand before admission, reserve capacity and reconcile with reported usage afterward. Keep separate accounting for limits that are actually independent. Multiple API keys on the same account may share a quota; key rotation is not a capacity plan.

Use a central limiter when several workers share credentials. Ten processes with ten independent counters can collectively exceed an account limit even if each process appears compliant.

How do you protect interactive requests from batch jobs?

Give foreground work and background work separate queues with deliberate priorities. Background jobs can absorb longer waits. An interactive request should fail clearly once its deadline expires instead of remaining in a queue indefinitely.

Add per-customer fairness so one large import cannot consume all capacity. For example, reserve a portion of available throughput for ordinary interactive traffic and cap each tenant’s concurrent jobs. Treat those fractions as product choices and tune them from observed demand.

What should happen after an HTTP 429?

Inspect the error and any retry guidance. Honor Retry-After where present, then add bounded exponential backoff with jitter. Stop once the total operation deadline or retry budget is exhausted.

Do not immediately retry every queued job at the same instant. That recreates the spike. Reduce admission until the backlog recovers, and distinguish capacity errors from exhausted balance or spending limits.

When is another provider useful?

An alternative route helps only when it has independent usable capacity and satisfies the request’s requirements. Two resellers can share the same upstream bottleneck. A fallback can also change cost, cache eligibility and data processing.

Track queue depth, oldest queued job, token utilization, 429 rate and time spent waiting. These reveal overload earlier than a monthly usage total.

For Tokely, consult errors and limits and the model-routing guide. Build your queue around the documented constraints, then use the quickstart to connect your application’s request worker.