Design AI cost optimization infrastructure.

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ANSWER MODE

WRITTEN ANSWER

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### Signal to interviewer

I can design cost systems that reduce spend sustainably while preserving product quality and reliability.

### Clarify

I would clarify cost hotspots, quality thresholds, workload segmentation, and which teams own optimization levers.

### Approach

Build a cost efficiency flywheel: granular observability, policy-driven optimization levers, and prioritized execution backlog with impact validation.

### Metrics & instrumentation

Primary metric: cost per successful outcome. Secondary metrics: cache hit contribution, model-tier routing mix, and optimization cycle throughput. Guardrails: quality regression rate, latency drift, and support escalation increase.

### Tradeoffs

More aggressive optimization reduces spend but can erode user trust if quality drops. Conservative changes protect quality but slow savings realization.

### Risks & mitigations

Risk: local cost wins harm global experience; mitigate with outcome-level guardrails. Risk: opaque cost attribution; mitigate with request-level cost tracing. Risk: optimization fatigue across teams; mitigate with centralized prioritization.

### Example

For an enterprise assistant, low-complexity prompts route to smaller models with caching, while high-stakes financial queries keep premium paths and strict validation.

### 90-second version

Treat cost optimization as a continuous flywheel: observe unit economics, apply policy-driven levers, and validate every saving against outcome quality before scaling.

FOLLOW-UPS

Clarification

Which cost segments should be optimized first for fastest safe savings?
What quality thresholds are non-negotiable during optimization?

Depth

How would you implement request-level cost attribution across model routes?
What governance cadence keeps optimization backlog aligned with product priorities?

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