What you NEED to know about LLM rate limits
Because of the global GPU shortage, all Large Language Model (LLM) inference providers (OpenAI, Anthropic, Google etc) implement strict rate limits. If you aren't careful, these rate limits can destroy your AI application. In this video I talk about why these rate limits exist, how they work in practice for both OpenAI and Antrhopic, and go into some code demos in Python. I explicitly go over the differences between how Anthropic and OpenAI handle these limits. I also discuss rate limit "tiers" and how you can pay to get your rate limit increased. Finally, I talk about how to reduce the chances of these errors occurring in the first place. Watch the video to improve your artificial intelligence application!
00:00 Rate limits will RUIN your AI app
00:23 Why are there rate limits at all?
02:15 What are rate limit tiers?
04:47 Python rate limit logging demo
08:19 Can I prevent rate limit errors?