LibraryCapSource OpenCasesDeveloping a Token-Based Revenue Model for Niloom.ai: Balancing Usage and Bandwidth Considerations

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Developing a Token-Based Revenue Model for Niloom.ai: Balancing Usage and Bandwidth Considerations

Last Updated: 04/14/2026

Case Organization

Niloom.ai

We believe creating augmented and virtual reality content should be easy, fast, and affordable for everyone

https://www.niloom.ai/

38 East 58th St

Case Contributors

Case Disciplines

Data Management Growth Strategy Information Technology (IT) Market Research Operations Product Design & Development Reporting, Financial Planning & Analysis Sales & Business Development Software Design & Development

Skills & Expertise

Adoption Strategy API Design Considerations Behavioral Pricing Strategy Billing System Design Competitive Benchmarking Customer Education Customer Onboarding Strategy Customer Segmentation Data Tracking and Metering Design Demand Modeling Financial Modeling Fraud Prevention Strategy Freemium Model Design Go-To-Market Strategy Industry Research KPI Development Market Research Operations Planning Performance Monitoring Persona Development Pricing Sensitivity Analysis Pricing Strategy Development Product Launch Strategy Revenue Model Design Risk Assessment SaaS Monetization Strategy Scalability Planning Security Considerations System Architecture Planning Technical Feasibility Analysis Tiered Pricing Strategy Token-Based Pricing Design Unit Economics Analysis Usage-Based Pricing Value-Based Pricing

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Background & Objective

The challenge or opportunity you are trying to address for the organization.

Niloom.ai, an AI-powered platform, seeks to implement a token-based revenue model to optimize monetization based on customer usage and bandwidth consumption. The challenge lies in identifying the most effective pricing mechanism that balances: Customer affordability and accessibility – Ensuring the pricing model remains competitive while maximizing revenue. Fair and scalable usage-based pricing – Aligning token consumption with real-time usage and AI model processing requirements.
Learning Objectives

This is what students will learn as they complete the case.

This case challenges students to design a scalable and economically viable monetization model for an AI platform operating under complex technical and usage constraints. It requires integrating pricing strategy, customer segmentation, and system architecture considerations to balance accessibility, fairness, and revenue optimization. Students will explore how token-based systems influence user behavior, adoption, and long-term value creation in AI-driven SaaS businesses. The work reflects real-world challenges in pricing design, infrastructure-aware monetization, and growth strategy for emerging technology platforms.
  •  Analyze token-based pricing models used by leading AI and SaaS platforms to identify best practices and trade-offs 
  •  Evaluate how usage variables such as API calls, compute intensity, and bandwidth consumption impact pricing fairness and scalability 
  •  Assess customer segments and pricing sensitivity to design tiered token structures that align with diverse usage patterns 
  •  Develop a token allocation and pricing model that balances customer acquisition, retention, and revenue optimization 
  •  Evaluate the technical requirements for implementing token tracking, billing systems, and usage monitoring within an AI platform 
  •  Design policies for token consumption, expiration, and purchasing that influence user behavior and platform economics 
  •  Develop a phased implementation and rollout strategy that ensures adoption while minimizing friction and confusion 
  •  Synthesize competitive benchmarking, pricing strategy, and technical feasibility into a cohesive monetization model for Niloom.ai 
Key Action Items

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