Cloud instances offer a flexible and scalable approach to workload management, as new instances can be easily launched to accommodate application growth. However, it is crucial for both optimal performance and cost savings to launch appropriate types and numbers of cloud instances to fit applications’ requirements.

With the help of machine learning-based analysis, Federator.ai can classify workload patterns for individual applications in no time, after receiving historical data, and offer appropriate portfolios of cloud instances to fit application demands. Federator.ai not only can ensure optimal performance for applications relevant to primary workload metric by providing enough resources, including the number of CPUs, memory, storage, and network capacity of cloud instances, but it can also prioritize the allocation of cheaper reserved instances and spot instances for certain applications in order to achieve cost-effective combinations of instances.