AI Engineering interview question · Cost and latency · staff level
Cut the cost of an LLM feature serving 10 million requests a day by 60% without hurting quality. Walk through your plan.
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Why it is asked
A staff-level cost question: can you find where the money goes, order the levers by risk, and protect quality with evals while you cut?
The answer
I would measure where the tokens go, then apply the safest savings steps first, each one gated by the eval set, and only then move traffic to cheaper models.
Assume (hypothetical numbers) 2,000 input and 300 output tokens per request, priced at $3 and $15 per million. That is $60,000 a day for input plus $45,000 for output: $105,000 a day.
1. Exact-match cache. If 10% of requests repeat, serving them from cache saves 10%: $94,500. 2. Trim the prompt and the output. Remove unused instructions and old history (2,000 to 1,400 tokens) and ask for a shorter answer format (300 to 220 tokens): $67,500, a 36% total cut so far. 3. Prefix caching. Put the 1,000 static tokens (system rules, examples) first so the provider can reuse them; if cached tokens cost 10% of normal: $43,200, about 59%. Some providers charge extra to write the cache, so the real saving depends on the hit rate; check it in usage data. 4. Routing. Send easy requests to a model ten times cheaper. If 60% qualify: $19,872, about 81% saved.
If part of the traffic is not interactive, a batch API at about half price is another low-risk step.
The target is 60%, so the plan has margin if routing qualifies fewer requests than hoped. Quality is protected because every step runs the golden set, and routing is rolled out as an A/B test with a quality metric and a guardrail on complaints. I would also report savings from real usage logs, not from this spreadsheet.
What the interviewer listens for
- Measure input and output token spend before choosing levers
- Order savings steps by risk: caching and trimming before model changes
- Show the arithmetic and leave margin above the target
- Gate each step with evals and roll routing out as an experiment
Follow-ups the interviewer may ask
- How do you decide which requests are easy? Hint: a classifier or rules trained on cases where the small model matched the large one on your eval set.
- What would you not cut? Hint: safety checks and the context the answer depends on.
Common mistakes
- Switching everything to a cheaper model first, with no quality gate
- Ignoring output tokens, which are often priced several times higher than input
Written by Roni Das, who builds distributed systems and AI systems for a living.
Learn it properly
This is taught step by step in the course lesson Token Cost Engineering: Where the LLM Bill Actually Goes.
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This is 1 of the 150 questions in the AI Engineering (LLM applications) pack
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