The advances of location-acquisition technologies and rapid development of GPS equipped devices have generated large spatio-temporal mobility traces fostering the opportunity of discovering inherent knowledge about urban dynamics. This mobility analytics of the movement behaviours of people and vehicles is the key component of building any intelligent transportation system (ITS) application. However, extracting usable knowledge from huge volume of mobility data is both time-consuming and computationally intensive. In this work, we propose a cloud-based spatial trajectory trace management framework, namely, Traj-Cloud to effectively store, retrieve and efficiently process the mobility traces for real-time applications. Traj-Cloud is a SaaS cloud which provides three major mobility services, namely, (i) trajectory-indexing to efficiently handle huge real-time trajectory updates; (ii) geo-tagging, map-matching services in a distributed computing platform (i.e., Google Compute Engine) and (iii) trajectory-processing to resolve mobility based spatio-temporal queries to improve efficiency and reduce response time. Traj-Cloud has been implemented in Google Cloud Platform (GCP) and empirical results on real-life dataset of NYC taxi traces and mobility trajectories of an academic campus yields the effectiveness of the proposed framework.