In e-commerce, pricing models are a key aspect of an online business’ competitiveness and profitability. There are two viable approaches relevant to pricing: dynamic pricing and personalized pricing. Dynamic pricing is the price adjustment based on real-time market conditions (demand, seasonality, competition, and user behavior). This method allows companies to maximize profits by adapting prices to external and internal factors. Price personalization is focused on setting individual prices for different users based on their behavior, preferences, and demographic characteristics. It is substantiated that using customer data, companies can offer personalized discounts or special offers, which helps increase loyalty and conversion. Both approaches significantly increase the effectiveness of pricing policies, allowing companies not only to optimize revenues, but also to provide more relevant offers to customers. An analysis of modern technologies, such as machine learning and artificial intelligence, which support the implementation of these models in business practices, is conducted. Structuring the benefits and risks associated with the implementation of dynamic and personalized prices, as well as their impact on the consumer experience and the market as a whole.