In the recent past, we are witnessing a proliferation in the number of web/mobile applications being hosted on a service provider’s Cloud. This has led to a surge in the traffic to the data centers hosting Virtual Machines (VM) running the cloud instances. In a cloud environment, a workload is defined as the requests coming in for the applications which are hosted on VM instances. Workload characterization helps in modeling the associations and correlations in the workload. Workload characterization models that are representative of the ground truth, can be leveraged for: (i) an accurate capacity planning, (ii) better resource utilization, (iii) reducing the spin-up times of VM instances, and (iv) maintaining compliance with Service Level Agreement (SLA). We propose a first-of-its-kind generative Dirichlet process-based model using Latent Dirichlet Allocation (LDA) for workload characterization. The characterization model is dependency preserving, regularized, and generative in nature, that relates the workload to the underlying application or user’s behavior that might have generated the workload. To evaluate the descriptive and predictive accuracies of the proposed model, we designed experiments using the Bit Brains Trace (BBT) and Alibaba Cluster Trace. The descriptive accuracy of the proposed workload characterization model is assessed by comparing a synthetic workload against the real workload using Pearson Correlation Coefficient (PCC) and Akaike Information Criterion (AIC) as the metrics. We have also performed statistical tests to assess the similarity between real workload and synthetic workload.