pricing

AI changes pricing because each use costs money and value per use varies. The interviewer wants the trade-offs of each model, matched to the product, and a hybrid that protects margin.

A market-entry question where copying the US playbook fails. The interviewer wants segments, languages, pricing that works with inference costs, distribution and the competitive landscape.

A pricing question about where the value of falling model costs should go. The answer depends on margin targets, competition and whether price or capability wins customers.

A packaging decision with adoption, revenue and margin pulling in different directions. The interviewer wants the trade-offs and a structure that captures value without stalling adoption.

Matching free is tempting and often ruinous when every use costs money. The interviewer wants a segmented response that protects the customers you can keep, the margin you need, and the value only you deliver.

Figma reported exactly this drop, driven by AI hosting costs, and moved to enforce credit limits. Find which features and customers cost the margin, meter heavy use, and keep margin only where AI does not buy retention.

A margin question with a constraint added halfway through. The average user is not the problem; the distribution is, and a flat price hides it until the model gets more expensive.

When the metric is the invoice, every edge case becomes a billing dispute. Principal-level answers design a definition customers can audit and a price that survives the worst month.

A low attach rate can mean few accounts buy, or buyers don't use it, or the denominator is wrong. Measure penetration inside buying accounts and active use of paid seats before changing the packaging.

A launch question where positioning, pricing, evaluation and capacity all have to line up. The interviewer wants to see you avoid cannibalising the wrong tier and give developers what they need to adopt it.

Embeddings are infrastructure that customers store and rebuild around. The interviewer wants retrieval quality on real tasks, multilingual coverage, cost, and above all a plan for version changes, because switching embeddings means re-indexing everything.

Batch pricing sells your idle GPU hours. Promise a completion window rather than a latency, make the API shape unsuitable for interactive use, and don't charge for work you couldn't do.

Routing as a platform feature saves customers money and moves quality decisions onto you. Make it opt-in, show which model answered, bound the quality loss and never change the model set under production traffic.

Data for free usage is a legitimate trade if it is explicit. Draw a hard line so production apps serving regulated users move to paid, and keep enterprise excluded by default.

AI costs vary by request, so pricing has to change, and people judge changes by fairness. Show the cost case, the communication and how you made it right when it landed badly.