Understanding the Limitations of LLM APIs and How to Navigate Them
When building AI applications, starting with API-based deployment is often the easiest and most cost-effective approach. APIs provide a quick entry point.
And yet, they come with several limitations that developers need to understand and manage effectively.
1. Rate Limits and Usage Quotas
One of the biggest challenges when using LLM APIs is dealing with rate limits and usage quotas.
LLMs are highly compute-intensive. They require significant resources, often multiple GPUs, to run efficiently. To balance server loads and prevent misuse, API providers enforce rate limits and usage quotas. These limits cap the number of requests we can make in a given time frame.
If not properly accounted for, these restrictions can disrupt the functionality of our application. Especially if it relies on high-frequency requests. Developers must plan for these constraints by designing applications that stay within the allowed limits; Or by negotiating higher quotas with the API provider.
2. Error Handling and Reliability Issues
LLM APIs, like all APIs, are prone to errors, dropped requests, and timeouts. These issues can be problematic because most LLM…
