Problems / RAG and retrieval, medium
At 8 s, 3,000 req/s of scrapers start querying your RAG assistant, eating model capacity meant for users.
Free to open. Runs in your browser, no sign-up.
Success ≥ 98% for users, block ≥ 95% of the scrapers.
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. | 200 slots |
Try it first. Open one hint at a time if you get stuck.
Scrapers look like users to your app.
Follow one question through each retrieval stage; the stage that turns red is the one to fix first.
Put a security layer (DDoS, WAF, bot management) in front.