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Platforms create value by matching participants on alternate sides of the marketplace. While many platforms practice one-to-one matching (e.g., Uber), others can conduct and monetize one-to-many simultaneous matches (e.g., lead marketing platforms). Ideally, the choice between the two modes of matching should be made not ex ante, but rather based on the relative premium that participants perceive for exclusive matches and the nature of heterogeneity in these two sets of valuations. This paper studies the problem of designing an auction format for such platforms, i.e., a set of rules for allocation and pricing of matches. For the sake of practicality, we require deterministic auctions that incentivize truthful bidding, and we formulate the optimal incentive-compatible (IC) auction as a mixed integer mathematical program. Although the optimal IC auction is notoriously hard to solve, its value is that it leads to a heuristic design that is simple to implement, provides good revenue, and has speedy performance, all critical in practice. Specifically, we develop multiple relaxations of the optimal auction to obtain upper bounds on the (unknown) optimal revenue and, conversely, refinements that produce heuristic auctions whose optimal revenue is a lower bound. By demonstrating a tight gap between the two bounds for one such design, RM, we prove that it has excellent revenue performance and places low information and computational burden on the platform and participants.