<p>Charging consumers reasonable prices and compensating service providers fairly are crucial for the successful operation of on-demand service platforms. These factors are also central to a platform’s ability to generate substantial profits. Information plays a pivotal role in platform decision-making. However, due to the complex dynamics of both the consumer and supply markets, along with the fluctuating behaviors of consumers and service providers, and cost considerations, it is challenging for platforms to fully characterize demand or supply. Nevertheless, platforms can relatively easily and accurately obtain partial information about the distribution functions, such as the upper and lower bounds, and the mean. Assuming the demand or supply function follows a multiplicative structure and only partial information (upper and lower bounds, and the mean) is available, we apply a distribution-robust optimization approach to model platform decision-making under two perspectives: pessimistic (maximin) and optimistic (maximax). We demonstrate that, under these conditions, there exists a unique optimal combination of price and wage that maximizes the platform’s profit. This study reveals that the platform’s profit under the optimistic mindset is equivalent to that under certain demand or supply conditions. In response to criticisms that the maximin approach is excessively conservative, we show that the performance under this approach can reach up to 83% of the profit achieved under deterministic demand or supply, indicating that the maximin approach is less conservative than often claimed. To test the robustness of our main results, we extend the model to incorporate an additive structure for the demand or supply function, as well as the scenario where the demand or supply functions follow a uniform distribution. The analysis demonstrates that, under certain conditions, the platform’s profit under partial information can be up to 2.5 times higher than in the case of a uniform distribution.</p>