Mayank Pant is an AI pricing and billing specialist who helps companies reconcile unpredictable inference costs with prices customers can understand. Affiliated with Stripe as a billing solutions architect in 2025 and 2026, he has focused on the commercial infrastructure that makes variable-cost AI products financially sustainable.
His work progressed from consumer subscription strategy and retention to the distinct economics of AI software, where heavy users can erode margins and rapidly changing features complicate pricing. His AI Engineer Europe presentation outlined several practical principles:
- Hybrid subscription and usage-based pricing: A predictable base subscription establishes the customer relationship; metered charges allow revenue to scale with costly activity.
- Value-aligned billing metrics: Infrastructure providers might charge for API consumption, while application customers respond more readily to completed workflows or measurable outcomes. Pant uses Gamma’s generated presentations and Intercom’s resolved support conversations to illustrate those distinctions; neither product is attributed to him.
- Credits as a flexible pricing layer: Credits can preserve understandable customer-facing plans while a company adjusts the computational cost of individual features. Existing customers can retain earlier terms as plans evolve.
- Billing guardrails and customer trust: Usage caps, spending alerts, rate limits, optional top-ups, and transaction-level records help prevent unexpected invoices and make charges explainable.
- Pricing as an iterative product decision: Customer churn, upgrades, feedback, and pricing experiments should inform ongoing adjustments. Billing infrastructure must support those changes without requiring substantial engineering work each time.
Pant treats pricing as part of the product itself: the billing unit should express customer value, protect margins, and give customers meaningful control over what they spend.
