I’m building a SaaS application that uses the OpenAI API for some of its features. One of my concerns is managing API rate limits effectively, especially when multiple users are accessing the service simultaneously.
However, I’m looking for suggestions or best practices from experienced developers who’ve handled similar challenges. Specifically:
How do you prevent exceeding API rate limits in high-concurrency scenarios?
Are there any tools, frameworks, or design patterns you recommend for implementing effective rate-limiting?
How do you communicate rate limit delays to end-users in a way that doesn’t hurt user experience?
I’d greatly appreciate any advice or examples from your own projects. Thank you in advance!
I’d rather sell a few subscriptions for more than a lot of subscriptions for less - ensuring I always have enough resources to service all users when they need it. But of course, unused capacity causes opportunity cost, so there’s that.
It depends on your application, your data integrity concerns, and your application architecture. I personally like to combine account affinity routing (sometimes unnecessary) with a time gated FIFO queue. The queue can be a kafka partition, rabbitmq, or just an array in memory lol. I was looking around to see if there’s a reactive pattern out there for this, but it doesn’t look like it atm unfortunately. Still, easy to implement.
IMO it’s better to disable service for some users than to deliver crap service to all users. It depends on your product, but if step 1 failed, I’d go for limiting access to low tier users and say the system’s over capacity. But (for me, realistically) I’d consider it a general outage and would try to get resources from wherever possible. I think the best user experience is to prevent onboarding if you’re out of resources, and onboard new users as they become available.