DevOps teams struggle with too many performance tuning options. Trial and error is time-consuming and unreliable for setting autoscaling thresholds, often leading to over-scaling with wasted resources or under-scaling that fails to meet performance goals during peak loads.
Federator.ai uses intelligent autoscaling with application workload predictions to learn each pod’s capacity and scale the number of pods at the right time. The Application-aware HPA offers a simple, cost-effective way to autoscale application containers and meet performance goals with optimal resource allocation.
Machine Learning-based HPA
Leverage Federator.ai’s machine-learning-based autoscaling to eliminate the need for manual tuning and experimentation with metric thresholds, ensuring optimal scaling results.
Just-in-Time Autoscaling
Utilize continuous, real-time workload and performance metrics to learn dynamic patterns and container capacity, enabling the scaling of the right number of replicas at the right time.
Application-aware Autoscaling
Automatically scale Kubernetes containers based on application-specific workload and KPI metrics, achieving autoscaling aligned with real workload demands and performance targets.
Red Hat has put countless hours into curating our partner ecosystem and this includes Cloud Cost Management companies to help our customers manage their day 2 operations and onward.
Companies like ProphetStor run on our Red Hat OpenShift Container Platform to provide performance, cost and efficiency management at the workload level.
Cody Richard
Global Partner Solutions Architect, Red Hat
