Enterprises often lack understanding of the resources needed to support their applications. This leads to either excessive over-provisioning or under-provisioning of resources (CPU, memory, storage). Using machine learning, Federator.ai determines the optimal cloud resources needed to support any workload on OpenShift and helps users find the best-cost instances from cloud providers for their applications.

• Multi-layer workload prediction
  Using machine learning and math-based algorithms, Federator.ai predicts containerized application and cluster node resource usage as the basis for resource recommendations at application level as well as at cluster node level. Federator.ai supports prediction for both physical/virtual CPUs and memories.
  
  
• Auto-scaling via resource recommendation
  Federator.ai utilizes the predicted resource usage to recommend the right number and size of pods for applications. Integrated with Datadog's WPA, applications are automatically scaled to meet the predicted resource usage.
  
  
• Application-aware recommendation execution
  Optimizing the resource usage and performance goals, Federator.ai uses application specific metrics for workload prediction and pod capacity estimation to auto-scale the right number of pods for best performance without overprovisioning.
  
  
• Multi-cloud Cost Analysis
  With resource usage prediction, Federator.ai analyzes potential cost of a cluster on different public cloud providers. It also recommend appropriate cluster nodes and ins