How should an AI platform monetize: subscriptions, usage-based, or bundles?

### Signal to interviewer

I can design monetization strategy that aligns pricing mechanics with workload economics and buyer behavior.

### Clarify

I would clarify customer purchasing preferences, workload predictability, sales motion, and margin targets.

### Approach

Deploy a monetization mix: baseline subscription for core value, usage-based metering for variable demand, and bundles for cross-workflow adoption.

### Metrics & instrumentation

Primary metric: net revenue quality across plan types. Secondary metrics: plan conversion mix, expansion contribution, and billing predictability. Guardrails: gross margin instability, price-plan confusion, and elevated churn post-renewal.

### Tradeoffs

Pure subscriptions improve predictability but can underprice heavy value users. Pure usage pricing aligns spend and value but can deter adoption.

### Risks & mitigations

Risk: pricing complexity hurts sales cycle; mitigate with clear plan narratives. Risk: adverse selection into low-margin plans; mitigate with guardrail thresholds. Risk: model cost shocks; mitigate with periodic pricing review cadences.

### Example

Offer platform subscription for collaboration and governance, metered inference for advanced workloads, and vertical bundles for team-specific outcomes.

### 90-second version

Monetize with a hybrid model tuned to workload behavior: predictable subscription core, usage-based elasticity, and bundles for expansion. Keep customer experience simple while preserving economic flexibility.