Problem
Apps calling OpenAI directly lack control over traffic
Solution
A gateway layer between the app and AI provider.
Real-World Use Cases
An AI gateway like this can be useful in several scenarios:
1. Protecting AI APIs
Applications calling OpenAI directly may expose API keys or allow uncontrolled usage.
A gateway adds a control layer for authentication and rate limiting.
2. Managing API usage
Teams running multiple AI features can enforce request limits and monitor usage through the gateway.
3. Centralizing AI traffic
Instead of every service calling OpenAI directly, requests pass through a single gateway where traffic policies can be applied.
4. Observability
The gateway provides metrics and request logs, making it easier to monitor how AI APIs are being used.
Architecture diagram
Client Application
|
v
+----------------------+
| AI Gateway |
|----------------------|
| API Key Auth |
| Rate Limiting |
| Load Balancer |
| Health Checks |
| Metrics + Logging |
+----------+-----------+
|
v
+----------------------+
| AI Provider API |
| (OpenAI) |
+----------------------+
Features
API key authentication
token bucket rate limiting
backend routing
health checks
metrics
Example request
curl http://127.0.0.1:8080/v1/chat/completions \
-H "Authorization: Bearer OPENAI_API_KEY" \
-H "X-API-Key: user1" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-4o-mini",
"messages": [
{"role": "user", "content": "Hello"}
]
}'
The request is sent to the gateway instead of OpenAI directly.
The gateway validates the X-API-Key, applies rate limiting, and forwards the request to the configured OpenAI backend.
GitHub repo
If you're interested in the implementation, the full project is available here:
https://github.com/amankishore8585/dnc-ai-gateaway
Feedback and suggestions are welcome.
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