Rate Limits Are a Product Problem in Multi-Model AI Apps

Short summary

Rate limits in multi-model AI apps are a product problem, not just an API error. When background batch jobs and customer-facing chatbots share the same token pool, blind retries degrade the user experience. Teams need workflow-level traffic policies with priority queues, concurrency limits, and fallback contracts that preserve output requirements. Monitoring should track tokens, queue wait, and workflow priority — not just requests per minute.

• •Rate limits are a product design problem: blind retries hurt high-priority workflows
• •Implement workflow-level priority queues with concurrency and wait-time limits
• •Fallback models must preserve output contracts: JSON schema, tool calling, context length, and quality

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