When migrating to a new LLM inference provider, it's crucial to conduct thorough load testing that accurately reflects your production traffic patterns. This involves simulating not just average rates but also peak sustained and burst traffic, while accounting for prompt and completion token counts, streaming versus non-streaming calls, and tool-use. Providers like OpenAI and Anthropic enforce both request-rate and token-rate limits, which can be hit differently depending on traffic shape, making it essential to monitor rate limit headers and retry-after information to understand actual capacity and headroom. AI
IMPACT Provides guidance for developers on how to effectively test LLM inference provider capacity to ensure smooth migrations and avoid unexpected rate limiting.
RANK_REASON The item provides practical advice and technical guidance on how to perform load testing for LLM inference providers, rather than announcing a new product or research.
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