Service level agreements (SLAs) are considered not only a central tool for managing QoS compliance, but also a differentiating factor between service implementations. In today's application environments with fast instrumentation deployment cycles in hybrid Cloud platforms, managing QoS compliance poses tremendous challenges, including how to deliver solutions that live up to promised QoS properties and preemptively identify provisioning risks before they lead to violations. Current approaches are usually reactive, i.e. the application infrastructure reacts to changes in QoS metrics, with a huge focus on compliance enforcement after violations have occurred. Cloud service provisioning demands a proactive approach to QoS management, with support for robust predictive scaling of service capacity based on multiple metrics, including business goals as well as infrastructure-level and QoS metrics. This paper presents an approach for adaptive service provisioning in the Cloud based on QoS analytics. A major contribution of the approach is the development of an analytics engine for predictive elasticity management of Cloud service provisioning that integrates in-depth mining of SLA compliance history with knowledge of business context, e.g. workload variability, a customer's business goals, application performance, and service operational context. In this work-in-progress report, we describe the proposed framework and discuss possible implementation and deployment scenarios.