Problems / LLM serving, medium
An exact-match prompt cache sits in front of the model but hits only 5%: timestamps in the prompt make every prompt unique. The model’s token quota is tight.
Free to open. Runs in your browser, no sign-up.
Success ≥ 99%, time to first token p99 ≤ 2.5 s.
45 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 Prompt cache.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s. | 3 replicas × 500 slots · timeout 30 s |
| Prompt cachePrompt cache | AI app calls it.Calls Model.Providers can cache a long repeated prompt prefix, cutting cost and time to first token. | 2000 slots · 5% hit rate |
| ModelLLM endpoint | Prompt cache calls it.Latency has two parts: time to the first token, then a steady stream of tokens per second. | 400 streams · 4M input tokens/min |
Try it first. Open one hint at a time if you get stuck.
Every miss spends quota.
Put the stable part of the prompt first; move timestamps out.
Raise the prompt cache hit rate by normalising prompts.