This study proposes a method to reduce the cost for acquiring impressions for location-based, mobile advertising firms. Such firms act on behalf of advertisers to execute mobile, in-app, ad campaigns. Ad space is sold on ad exchanges that auction impressions one-at-a-time, on a real-time basis. In this paper, we examine whether ad firms should work with one or multiple ad exchanges to minimize the total procurement cost (equal to the cost incurred to acquire impressions plus the computing cost). By working with more ad exchanges, the ad delivery firm can bid lower on each exchange and potentially save on the total procurement cost. However, ad exchanges typically require ad firms to spend a minimum amount on the exchange. Hence, it is not smart to work with an exchange but acquire very few impressions from this exchange. Working with multiple exchanges also incurs a higher computing cost, corresponding to the computing capacity needed to support the bidding architecture. We solve an optimization problem to determine the optimal number of ad exchanges to use to acquire impressions and the optimal bidding policy on each of these exchanges. We also propose a novel, selective bidding strategy where some bid requests are returned with a zero bid. That is, the ad firm deliberately passes on some opportunities to win impressions. Doing so reduces the computing cost (because returning a zero bid expends minimal computing resources). However, the firm needs to bid higher on other opportunities to meet its demand. We find the optimal selective bidding strategy in addition to the optimal number of ad exchanges and the optimal bidding strategy when non-zero bids are returned. Finally, we demonstrate our solution for a real ad firm (Cidewalk) where the firm is shown to reduce its total cost by 33% by working with multiple ad exchanges (instead of working with a single ad exchange) and the use of selective bidding (instead of returning a nonzero bid for every bid request). We consider an ad firm that acts on behalf of advertisers to execute mobile, in-app, ad campaigns. The firm commits to provide an advertiser a specified number of ad placements (impressions) on mobile apps, usually in a specified location, and within a specified time horizon. The supply for ad space arrives, in real time, in the form of bid requests from one or more mobile ad exchanges. The ad firm needs to bid on each impression in such a way that the goals of several ongoing campaigns are met at minimum cost. The ad firm needs to execute multiple campaigns simultaneously and get its supply (for ad space, or impressions) from multiple mobile ad exchanges. By working with more than one ad exchange, the direct cost of procuring the necessary impressions can be lowered. However, this lower cost needs to be balanced with the cost of the additional computing resources needed to work with multiple mobile ad exchanges and the (possible) extra cost of meeting the minimum spend (or participation fee) imposed by each ad exchange. Here, there are two key decisions that the firm needs to make. First, it needs to select the set of mobile ad exchanges to obtain its supply; each mobile ad exchange is characterized by specific supply uncertainties, location dependent bid curves, and a participation fee. Second, for each ad exchange and location, the ad firm needs to determine its bidding policy, that is, how much to bid for each bid request. We show that the proposed near-optimal bidding strategy, the strategy to bid at each exchange-location combination, is state independent. We first solve a general problem of selecting among multiple nonidentical ad exchanges. We next analyze the special case with identical mobile ad exchanges and show that, depending on the particular parameter setting, the near-optimal number of ad exchanges and the near-optimal bid amount can be weak complements or substitutes. Finally, we propose a cloud-based architecture to procure impressions where the ad firm uses a selective bidding strategy that can further lower procurement costs. The ideas of this paper are applied to a real problem and the savings from our approach (about 33% lower cost) are demonstrated. History: Giri Kumar Tayi, Senior Editor; Martin Bichler, Associate Editor. Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2023.1221. [ABSTRACT FROM AUTHOR]