AI API providers enforce rate limits at two levels: RPM (requests per minute) and TPM (tokens per minute). Hitting either means 429 errors and failed requests. OpenAI's limits vary by tier (usage-based), Anthropic's by tier and model, and all providers increase limits with account spend or explicit requests.
Strategies for rate-limited workloads: exponential backoff with jitter (standard; retry with increasing delays), request queuing (buffer requests and drain the queue at the allowed rate), load distribution (spread requests across multiple API keys or providers), and batch APIs (OpenAI's Batch API is 50% cheaper and subject to daily rather than per-minute limits).
For high-volume production systems, build rate limit handling at the infrastructure layer (API gateway or proxy) rather than in every service. LiteLLM and similar proxies handle this centrally.
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