AI API Rate Limits, Retries, and Backoff

Rate-limit strategy

AI API rate limits are normal production events, not edge cases. Treat them as capacity signals and design the product response before traffic spikes.

The safest pattern is bounded retries, exponential backoff with jitter, queueing for non-urgent work, and clear user feedback when a request cannot run now.

Retry budget

Every retry can cost money and add load. Set a retry budget per workflow so failure handling does not become a hidden spend multiplier.

Use idempotency keys or request IDs for user actions that could be submitted twice. This makes it easier to deduplicate logs, invoices, and user-visible results.

• Retry transient 429, 502, 503, and timeout events.
• Do not retry invalid keys, unsupported models, malformed requests, or low balance.
• Cap total retry time so users are not stuck waiting.
• Log every retry attempt with trigger reason and final outcome.

Fallback rules

A fallback model can help when capacity is constrained, but it must be allowed by the workflow. Background enrichment can usually fallback more safely than regulated advice or high-value customer actions.

Document which models can replace each other and what quality checks are required after fallback use increases.

SilkRouter operations

With SilkRouter, keep API keys scoped by workflow or client, monitor failures in the dashboard, and review usage after incidents. The goal is to make rate limits explainable, not invisible.