Problems / AI systems, medium
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.
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Success ≥ 99%, time to first token p99 ≤ 3 s.
Like a coding problem, a solution has to pass every case, not just the one it was built for. Submit in the lab runs all 2; 0 of them are hidden until then.
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 |