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Design a cost-control and quota system for an internal LLM platform serving many teams.

Give every team an LLM API key and your bill becomes a mystery with a six-figure surprise at month end. Attributing, limiting, and optimizing spend is a platform feature. Here is how to build it.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Give every team an LLM API key and your bill becomes a mystery with a six-figure surprise at month end. Attributing, limiting, and optimizing spend is a platform feature. Here is how to build it.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·How language models work, enough to build onFree12mTokens: the unit everything else is priced inBefore you can reason about cost, latency, context limits, or chunk sizes, you need one mental model: the system charges you in tokens, not words. This lesson installs that habit and shows where it comes back in every later module.Applied AI Engineering·ProductionSign in13mLatency, cost and quality: pick your constraintThree quantities you cannot maximise at once, and the levers that move each. Most systems are tuned by accident; this lesson is how to decide deliberately, starting from the requirement rather than from the model.

UP NEXT ON YOUR JOURNEY

Next in this trackDesign a system that monitors the quality of millions of AI support conversations and flags the bad ones.Next in this trackHow do CAP and consistency tradeoffs apply to an ML feature store and online serving?Next in this trackDistribute a 10GB file from one bandwidth-limited source to thousands of machines as fast as possible.

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