构建依赖大型语言模型 (LLM) 的应用程序的开发人员必须实施稳健的策略来处理速率限制和服务中断。这些问题可能导致显著的停机时间、用户体验下降和成本增加。有效的解决方案包括使用断路器、带有消息队列（如 RabbitMQ 或 AWS SQS）的异步处理，以及回退到更简单的模型或缓存响应的机制。不同的 LLM 提供商，如 OpenAI、DeepSeek、Anthropic 和 Google，都有独特的速率限制模型和错误代码，开发人员必须考虑到这些，通常采用带有抖动的指数退避进行重试。 AI
影响 确保集成 LLM API 时的应用程序稳定性和成本效益，这对于生产环境至关重要。
排序理由 该集群讨论了在使用 LLM API 时处理技术问题（速率限制、中断）的策略和最佳实践，而不是新的发布或重大的行业事件。
AI 生成摘要 · Google Gemini · 来自 5 个来源。 我们如何撰写摘要 →
arXiv:2607.02043v1 Announce Type: cross Abstract: Disaggregated LLM serving runs prefill and decode on separate GPU pools to keep the two phases from interfering. In practice, this creates a new asymmetry: under bursty, heavy-tailed workloads prefill nodes saturate while decode n…
Disaggregated LLM serving runs prefill and decode on separate GPU pools to keep the two phases from interfering. In practice, this creates a new asymmetry: under bursty, heavy-tailed workloads prefill nodes saturate while decode nodes have compute underutilized, and on a producti…
arXiv:2508.06133v4 Announce Type: replace-cross Abstract: We study offline scheduling for large language model (LLM) serving under a fixed KV-cache memory budget, where requests have heterogeneous prompt (prefill) and response (decode) lengths. Prompt tokens determine initial KV-…
<h2> Key takeaways </h2> <ul> <li>Implement circuit breakers to prevent cascading failures.</li> <li>Fallback to simpler models can maintain service during outages.</li> <li>Asynchronous processing can reduce immediate load on LLMs.</li> <li>Rate-limiting strategies can improve o…
<h1> LLM API Rate Limiting & Retry Strategies: Complete Guide (2026) </h1> <h2> <strong>Published: June 29, 2026</strong> · <strong>15 min read</strong> </h2> <h2> Introduction </h2> <p>Every LLM API — from OpenAI's GPT-5 to DeepSeek V4, Claude 4, and Gemini 2.5 — enforces ra…