This hack targets institutions that have seasonal spikes and dips in the requests for services. Setting up a private cloud to cater to the situation of highest traffic is not financially viable and does not make optimized use of resources and using public cloud services is also quite an expensive ordeal in the long run. Therefore, our solution suggests a small private cloud be set up. It then captures usage and request trends using a machine learning technique and dynamically creates new instances on the public cloud in such a way that the performance required is now obtained by this new hybrid cloud at the lowest expense of the institution. Once the trend goes back to levels which can be supported by the private cloud, the public domain instances are released. We also provide an interface giving the current usage of public services like GCE, AWS and so on and also provide manual options to request and release cloudspace for non seasonal spike in requests.