In the Internet of Things (IoT) era, an increasing number of data management applications, such as for connected vehicles and smarter cities, face the challenge of querying and analyzing massive volumes of spatiotemporal data. These applications frequently perform queries that join moving objects with spatial data, such as selecting sub-tracks crossing a bridge. However, spatiotemporal queries are not well supported or natively supported by current state-of-the-art relational database systems. Most of existing systems build a spatial index directly over the raw spatiotemporal data, which leads to performance issues when scaling out for both indexing and query. In this paper, we focus on building a Spatio Temporal historian as a Service (STaaS) by extending the IBM Blue mix Time Series Database service. The STaaS service manages to process spatiotemporal queries over high volume historical data. The experiments show that STaaS service could easily scale out by adding shards, and achieve dramatic speed-up on spatiotemporal query with support of our hybrid data store. Moreover, we have already deployed STaaS on Blue mix Staging (Internal User Testing) Zone to collect feedback for improvement before porting it into the product zone in the future.