Yes, I have encountered issues like inconsistent response times, incomplete documentation, and difficulty managing API rate limits when integrating AI models.

Yes, we’ve faced several challenges when calling the API capabilities of AI model providers. One common issue has been inconsistent API documentation that lacks clarity or doesn’t align with the actual implementation. This often leads to confusion and delays during integration. To address this, we make it a habit to reach out to support teams for clarification and check community forums for additional insights. Using APIPark, this open-source AI gateway, has streamlined the process, allowing us to quickly and securely access multiple AI model providers. It simplifies integration and improves our ability to manage these APIs effectively.

There are quite a few challenges when working with AI model APIs. First, identifying the best model for our specific use case can be difficult—it often requires a lot of testing and comparison. Then, there's the issue of server costs, especially when we have a small number of users. The pricing can become a concern as the usage grows, even if the user base is still limited. Finally, API response times can be a problem, as slow responses affect the overall user experience.

I will first talk about the two core issues I encountered: 1. Integrating various models is very troublesome, looking at various documents, and then debugging, which is quite time-consuming 2. The issue of costs, sometimes I need a more accurate and expensive API, but sometimes a free API can meet my expectations, but it is difficult to make this judgment.