Problems / AI security and governance, medium
At 8 s a 20,000 req/s flood hits your public LLM API. Every flood request costs GPU time.
Free to open. Runs in your browser, no sign-up.
Success ≥ 98% for users, block ≥ 95%.
20 requests a second for 30 seconds: 100% request (reads).
What each component does in this design, and what calls it. The simulator mimics each one from these settings.
| Component | Its job here | Settings |
|---|---|---|
| Load balancerLoad balancer | Traffic enters here.Calls AI app.Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header. | – |
| AI appApp service | Load balancer forwards to it.Calls GPU serving pool.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s. | 3 replicas × 500 slots · timeout 30 s |
| GPU serving poolSelf-hosted serving pool | AI app calls it.GPU servers batch requests together; throughput rises with batch size until memory runs out. | 400 slots |
Try it first. Open one hint at a time if you get stuck.
GPUs are the most expensive thing to waste.
Look at what got through, or what was wrongly blocked: which filter is responsible for this kind of traffic?
Put DDoS protection, a WAF and bot management in front.