I build SaaS products and apps for clients, and many use the OpenAI API. I’m looking for practical advice from developers managing multiple independent client projects.
My main concern is limiting the impact of a security incident. If one application or API key is compromised, I want to minimize disruption to unrelated clients and services.
How do you organize your setup?
Do you use one organization with separate projects and API keys for each client?
Does each client own their OpenAI organization and billing, and grant you developer access?
What isolation does your approach provide, and what risks remain shared across projects?
What controls do you use to detect unusual usage, contain unexpected costs, and revoke compromised credentials quickly?
What do you wish you had set up differently before scaling to multiple clients?
I currently manage around 20 projects, so I’m interested in balancing security, client ownership, and day-to-day administration.
This is an architecture and security discussion. I’m looking for lessons from real deployments and suggestions for a more resilient setup.
I have some experience with this from the past with multiple fairly security-strict clients/customers.
I would NOT use a single organisation.
The setup that worked well (with some extra overhead that can be made easy if done well):
Implement infrastructure-as-code wherever possible with basically a “single” onboarding/provisioning script
Each client under its own OpenAI organisation
Multiple projects under each organisation - for example in my case it was staging and prod projects; staging usually low-cap (lower token limits; used just to test)
See here for some great documentation on managing OpenAI organisations. You can enforce project-level spending limits as well as project-level IP allowlisting. Other than that just use standard monitoring like OpenTelemetry.