Cost Optimization at Scale Questions

Addresses cost conscious design and operational practices for systems operating at large scale and high volume. Candidates should discuss measuring and improving unit economics such as cost per request or cost per customer, multi tier storage strategies and lifecycle management, caching, batching and request consolidation to reduce resource use, data and model compression, optimizing network and input output patterns, and minimizing egress and transfer charges. Senior discussions include product level trade offs, prioritization of cost reductions versus feature velocity, instrumentation and observability for ongoing cost measurement, automation and runbook approaches to enforce cost controls, and organizational practices to continuously identify, quantify, and implement savings without compromising critical service level objectives. The topic emphasizes measurement, benchmarking, risk assessment, and communicating expected savings and operational impacts to stakeholders.

HardSystem Design

38 practiced

Design a cost-optimized inference platform for a 70B-parameter LLM that must serve 1M requests/day with a 200ms P95 SLO and a target cost of under $0.50 per 1k requests. Discuss model serving strategy (sharding, tensor-slicing, quantization), caching, autoscaling, and hardware selection. Provide estimated trade-offs.

HardTechnical

80 practiced

Describe advanced techniques to reduce training cost for very large models: gradient checkpointing, ZeRO optimizer stages, pipeline parallelism, mixed precision, and dataset sharding. For each, explain roughly how much memory/compute savings you might expect and the engineering trade-offs.

MediumTechnical

48 practiced

You have limited engineering bandwidth and conflicting requests from product and infra teams. Propose a prioritization framework to decide when to pause feature work to pursue cost-saving initiatives. Include decision criteria, stakeholders to consult, and an example scoring formula.

MediumSystem Design

48 practiced

Design a multi-tier storage plan for a 5 PB ML dataset used for training and analytics. Requirements: keep last 90 days hot for training, older data accessible within 6 hours for retraining, cost target 50% lower than keeping all on SSD, and minimal operational overhead. List components, lifecycle policies, and expected trade-offs.

EasyTechnical

40 practiced

List and rank the top 7 cost drivers you would expect for an AI product that develops and serves deep learning models (training + inference + storage + networking). For each driver briefly explain why it grows with scale and one mitigation lever an AI engineer can use.

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