Problems / AI systems, hard
The support assistant runs at 80 questions a second against a tight token quota, and at 20 s its only model provider goes down for 10 seconds.
Free to open. Runs in your browser, no sign-up.
Success ≥ 99%, time to first token p99 ≤ 3 s.
80 requests a second for 40 seconds: 100% question (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 gateway.Layer 4 balancers route by IP and port; layer 7 balancers read HTTP and can route by path or header. | – |
| AI gatewayApp service | Load balancer forwards to it.Calls Semantic cache (checked first), Embedding model, Vector DB, LLM endpoint.Little's law: throughput ≈ concurrency ÷ latency. 200 slots at 50 ms serve about 4,000 req/s. | 3 replicas × 200 slots · timeout 30 s |
| Semantic cacheSemantic cache | AI gateway checks it first.A semantic cache matches questions by meaning using embeddings, not exact text. | 25% hit rate |
| Embedding modelEmbedding model | AI gateway calls it.Embeddings turn text into vectors where similar meanings sit close together. | – |
| Vector DBVector DB | AI gateway calls it.Approximate indexes like HNSW trade a little recall for much faster search. | – |
| LLM endpointLLM endpoint | AI gateway calls it.Latency has two parts: time to the first token, then a steady stream of tokens per second. | 300 streams · 6M input tokens/min |
Try it first. Open one hint at a time if you get stuck.
Two failure modes: 429s all the time, and an outage once.
Fewer model calls (a better semantic cache, prefix caching) fix the quota; a second provider fixes the outage.
Raise the semantic cache hit rate, then add a fallback model.
Raise the semantic cache hit rate (or enable prefix caching) and add a fallback model from another provider.