I've been running into some issues as my usage of OpenAI has scaled with multiple users and endpoints. It's gotten pretty messy, with challenges like unclear usage per user, hard-to-track costs, and unexpected spikes hitting the rate limits. To tackle this, I've built a simple gateway to manage API interactions. It offers basic features like rate limiting, per-user usage tracking, and cost estimation, which definitely helps in monitoring usage rather than guessing. I'm curious about how others are handling similar situations when their applications grow beyond just one user.
1 Answer
That sounds like a solid approach! Many teams face this hurdle when transitioning from prototypes to real applications. One thing that's helped us is setting budget alerts via the OpenAI dashboard first, and then we layered in monitoring for each endpoint. We used middleware to log token counts to Postgres and create a daily summary. It's not as neat as having a dedicated gateway, but it gave us good visibility quickly. Also, consider employing caching strategies; we discovered that around 30% of our LLM calls were duplicate prompts from different users, and adding Redis reduced costs significantly more than just relying on rate limits. Are you thinking about incorporating prompt caching or response deduplication into your setup?
Thanks for your insights! The caching idea is really intriguing. I'm currently focused on rate limits and basic tracking, but I'm looking into persistent logging with Postgres and starting to play around with caching within the gateway. I haven’t tackled prompt deduplication yet—just sticking with response-level caching for now, but that's definitely on my radar.