We’re running multiple applications (let’s call them APP1 and APP2) under a single OpenAI account.
We’d like each app to use a separate API key so we can:
Track usage and costs per app.
Potentially avoid collisions with rate limits if each key has independent limits.
Implement different usage controls and environment variable setups for each app.
However, we’re aware there’s also an account-level usage limit (monthly credits, overall token usage, etc.).
A few questions come to mind:
Are the rate limits strictly “API key–based,” or is there also an org-level (account-level) rate limit that aggregates the usage across all keys?
If there are key-based limits, will having separate keys for APP1 and APP2 help us keep one app’s spikes from throttling the other?
Does anyone have experience or best practices regarding tracking costs and usage at the app level, given that all keys belong to one account?
Are there any official guidelines on how to set up or handle this scenario?
We’ve seen references to multiple API keys, but we’re not sure if this fully separates the rate-limiting enforcement.
Any clarifications or pointers would be appreciated.
Hi, I noticed that we can set project-specific rate limits in the ‘Settings > Limits’ page.
However, I’m also interested in whether there’s any built-in throttling mechanism on OpenAI’s side,
besides these user-configurable rate limits.
Specifically:
Does OpenAI automatically throttle certain requests (e.g., to handle sudden spikes or protect from abuse)
even if our configured rate limit has not been reached?
If so, how do we determine whether we’ve been throttled by the platform vs. hitting our rate limit settings?
Conversely, do you recommend implementing our own client-side throttling (e.g., in Node.js, Nginx, or Python)
to avoid hitting the rate limit in the first place?
Any clarifications or best practices would be greatly appreciated!
There are default rate limits on your org which are determined by your trust tier.
If you hit your org’s rate-limit, you’ll get a typical error code 429.
Whenever you hit project's rate limit, you'll get an error that looks like this.
{
"error": {
"message": "Rate limit reached for <model_name> in project <project_id> organization <org_id> on requests per min (RPM): Limit 1, Used 1, Requested 1. Please try again in 1m0s. Visit https://platform.openai.com/account/rate-limits to learn more.",
"type": "requests",
"param": null,
"code": "rate_limit_exceeded"
}
}
Yes you should be reasonably limiting requests, in addition to implementing the recommendations from the safety best practices guide, for end-users to prevent abuse.
Thank you for clarifying. I understand that the organization-level rate limit is always in place, and it resets after the set time window (often 1 minute), so once we exceed the limit, we just need to wait until the window resets. Since we haven’t configured any specific project-level rate limits, we’re effectively only subject to the default org limit. That answers my concern perfectly!
