AI Engineering · 400-level
LLMOps: Cost, Latency, Observability
AIE-4023 creditselectivebadge: operatorprereqs: AIE-301
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What's inside
Sections & lessons
01
Routing & caching strategies
- Sending each request to the cheapest model that still passesconcept45 min
02
Cost-attribution & the 1000x slider
- Turning a $0.02 demo run into a visible daily cost cliffconcept60 min
03
Token-level latency
- Racing several models on the identical taskconcept60 min
04
Quantization quality-delta at scale
- What int8/int4 actually costs in eval scoreconcept60 min
05
Observability: traces & cost attribution
- Reading a production trace back to its dollar costconcept45 min
06
Lab + eval-gate: routing strategy under a quality floor
- Recommend a routing/caching plan that survives scale (Operator badge)concept120 min
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Vocabulary
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Optional · watch & try
Go deeper, elsewhere
Hand-picked public explainers and open tools — always optional, never required, never graded.
LLM Inference: Cost vs. Latency vs. ThroughputFrames the three-way tradeoff every serving decision makes Cut Your LLM Costs and Latency up to 86% with Semantic CachingConcrete caching technique with a measured before/after Fix Your LLM Latency: What Actually Works in ProductionProduction-grade latency debugging checklist Artificial Analysis — LLM Price CalculatorCompare real token pricing and speed across model providers
This module ends in a gate you can fail.
That's what makes passing it mean something. Take the DSAT, get placed, and start earning.