Metrics question
You own LLM usage, cost, and capacity planning for Glean Model Hub. How would you forecast demand for new model launches and set adoption guardrails so customers can try new capabilities without causing unsustainable inference spend or service degradation?
- Glean
- Metrics
- Hard
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What this question tests
Tests capacity planning: forecasting inference demand for a multi model platform and setting guardrails that let customers try new models safely.
How to approach it
- Clarify the goal, protecting service quality and margin while letting customers pilot a newly launched model.
- Forecast demand from leading indicators, for example adoption curves from prior launches and current query growth by workspace tier.
- Segment demand by account size, since a few large enterprise accounts likely drive most of a new model's early traffic.
- Set guardrails before launch: per workspace rate limits, a traffic cap on the new model, and an automatic fallback to the stable default if latency spikes.
- Provision capacity buffers above forecast peak, since new model launches are bursty, and instrument real time dashboards on cost, latency, and error rate.
What a strong answer includes
- Forecasts with a range, for example ten to thirty percent of eligible workspaces trying a new model in week one, not a single estimate.
- Ties guardrails to automatic circuit breakers, for example auto capping a workspace's experimental spend, rather than manual review after the fact.
- Separates cost guardrails from reliability guardrails, since a cheap model can still degrade shared capacity for everyone.
- Proposes a staged rollout: dogfood, then a few design partners, then general availability, each gated on real usage data.
Common mistakes
- Forecasting only from past launches without adjusting for a larger, more AI hungry customer base.
- Setting guardrails so conservative customers cannot meaningfully evaluate the new model.
Likely follow-up questions
- How would you decide when to lift a guardrail versus keep it permanently?
- What would you do if one large customer wanted to bypass the caps?
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More questions from Glean
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop