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The problem of online scheduling of multi-server jobs is considered, where there are a total of \(K\) servers, and each job requires concurrent service from multiple servers for it to be processed. Each job on its arrival reveals its processing time, the number of servers from which it needs concurrent service and an online algorithm has to make scheduling decisions using only causal information, with the goal of minimizing the response/flow time. The worst case input model is considered and the performance metric is the competitive ratio. For the case, when all job processing time (sizes) are the same, we show that the competitive ratio of any deterministic/randomized algorithm is at least \(\Omega(K)\) and propose an online algorithm whose competitive ratio is at most \(K+1\). With equal job sizes, we also consider the resource augmentation regime where an online algorithm has access to more servers than an optimal offline algorithm. With resource augmentation, we propose a simple algorithm and show that it has a competitive ratio of \(1\) when provided with \(2K\) servers with respect to an optimal offline algorithm with \(K\) servers. With unequal job sizes, we propose an online algorithm whose competitive ratio is at most \(2K \log (K w_{\max})\), where \(w_{\max}\) is the maximum size of any job.