This paper addresses the challenge of contract design within modern social platforms such as YouTube, Medium, and TikTok, where the primary goal is to develop monetization incentives for content creators. The inherent complexities of these platforms, influenced by factors such as regulation and scalability, often hinder the provision of personalized contracts, despite the diversity among content creators. We model this challenge as a multi-agent combinatorial contract design problem in which the principal (e.g., a digital platform) delegates an identical task (e.g., video production) to agents (e.g., content creators) and motivates them through contracts. Each agent’s payment depends solely on the success of his assigned task, and the principal gains utility upon each successful task. Consequently, an optimal contract may vary among agents due to their heterogeneity, while a public contract offers the same terms to all agents. The price of non-discrimination (PoN) captures the difference in the principal’s utility when applying non-discriminatory (public) contracts versus customized (optimal) contracts for individual content creators. We analyze how the price of non-discrimination interacts with the combinatorial structure of agents’ technologies, establishing tight and nearly tight bounds on the price of non-discrimination across various scenarios.