Is There an OpenAI API Proxy to Allocate Limited Token Budgets to Multiple Users?

Below is an overview of the situation and some recommended next steps:

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Current Situation

OpenAI does not currently offer an out-of-the-box proxy solution that generates multiple token-limited API keys from one “real” API key. In practice, most production applications that need to abstract the OpenAI API key from end users implement their own proxy server. This proxy intercepts user requests and applies your own quota controls, usage tracking, and endpoint restrictions before forwarding calls to the OpenAI API.

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Considerations for a Custom Proxy

If you decide to build your own proxy, here are a few points to keep in mind:

• Secure Management of the API Key: Ensure the real key is never exposed to your users. All communication to the OpenAI API should happen server-side.

• Usage Monitoring & Quotas: You’ll need to implement tracking of token usage per generated token. This is essentially an application-level budgeting feature that subtracts usage from each student’s balance.

• Endpoint/Model Restrictions: To restrict which endpoints or models each student can use, incorporate checks in your proxy before forwarding the request.

• Token Generation: Develop a mechanism to generate custom tokens for each student. This token would effectively be a session key or API key surrogate that the proxy maps to a usage budget and possibly permission set.

• Scaling & Deployment: Decide whether to host your proxy on-premise or use a publicly hosted environment. For university settings with modest usage (<$1/user typically), a cloud instance with careful budgeting might be sufficient.

• Rate Limiting & Abuse Prevention: Since all requests are funneled through a single proxy, implement rate limiting and other protective measures to avoid abuse or inadvertent cost spikes.

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Existing Solutions

As of now, there isn’t a widely recognized or officially recommended third-party solution that meets all your criteria (public hosting, token generation with custom per-token budgets, endpoint/model restrictions, etc.) for under a minimal fee. Some developers have built custom proxies for internal use or research projects, but they are typically not packaged as a service.

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Next Steps

• Prototype a Simple Proxy: Start by setting up a basic proxy that intercepts requests, checks usage against a stored quota, and then forwards approved requests to the OpenAI API. This will allow you to validate your usage tracking and budget management.

• Implement Administrative APIs: To support features like programmatically generating tokens, consider building RESTful endpoints that let you manage student tokens, view usage reports, and adjust quotas as needed.

• Explore Open-Source Alternatives: Keep an eye on community projects as this is a recurring need for educational and low-cost apps. You might find open-source prototypes on GitHub, but be sure to review them carefully for security and compliance with OpenAI’s terms of service.

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Summary

There isn’t a ready-made, widely supported service that meets all your needs for proxying OpenAI API requests with individual student quotas. The common approach is to build a lightweight proxy server with custom logic for usage tracking, token generation, and request validation. This way, you maintain centralized control over your billing while abstracting the API key from your end users.

If you have further questions on implementing any part of this solution, feel free to ask!