In several applications of real-time matching of demand to supply in online marketplaces, the platform allows for some latency to batch the demand and improve the efficiency. Motivated by these applications, we study the optimal trade-off between batching and inefficiency under adversarial arrival. As our base model, we consider K-stage variants of the vertex weighted b-matching in the adversarial setting, where online vertices arrive stage-wise and in K batches -- in contrast to online arrival. Our main result for this problem is an optimal (1-(1-1/K)^K)- competitive (fractional) matching algorithm, improving the classic (1-1/e) competitive ratio bound known for its online variant (Mehta et al., 2007; Aggarwal et al., 2011). We also extend this result to the rich model of multi-stage configuration allocation with free-disposals (Devanur et al., 2016), which is motivated by the display advertising in video streaming platforms. Our main technique is developing tools to vary the trade-off between "greedy-ness" and "hedging" of the algorithm across stages. We rely on a particular family of convex-programming based matchings that distribute the demand in a specifically balanced way among supply in different stages, while carefully modifying the balancedness of the resulting matching across stages. More precisely, we identify a sequence of polynomials with decreasing degrees to be used as strictly concave regularizers of the maximum weight matching linear program to form these convex programs. At each stage, our algorithm returns the corresponding regularized optimal solution as the matching of this stage (by solving the convex program). Using structural properties of these convex programs and recursively connecting the regularizers together, we develop a new multi-stage primal-dual framework to analyze the competitive ratio. We further show this algorithm is optimally competitive.