The ProphetStor Federator.ai Cluster Overview dashboard displays resource usage predictions and recommendations for Kubernetes clusters and nodes and historical usage.
The ProphetStor Federator.ai Application Overview dashboard displays predicted CPU and memory usage and recommendations for each application.
The ProphetStor Federator.ai Kafka Overview dashboard displays usage information and recommendations about autoscaling Kafka consumer replicas.
The ProphetStor Federator.ai Cost Analysis Overview dashboard shows deployment cost of a Kubernetes cluster and recommendations of cluster configuration and estimated cost/savings when it is deployed on public cloud service providers.
Federator.ai dashboard displays workload prediction and resource recommendations for Kubernetes or VM clusters and applications.
Federator.ai provides predictions and resource recommendations for clusters, nodes, namespaces, applications, and controllers
Based on predicted workload of a cluster, Federator.ai recommends most cost-effective cluster configuration for different public cloud provider.
Federator.ai analyzes and projects cost trend for individual namespace.
Overview
ProphetStor Federator.ai
is an AI-based solution that helps enterprises manage, optimize, and auto-scale resources for any applications on Kubernetes. Using advanced machine learning algorithms to predict application workload, Federator.ai scales the right amount of resources at the right time for optimized application performance.
AI-based workload prediction for containerized applications in Kubernetes clusters and VMs in VMware clusters
Resource recommendations based on workload prediction, application, Kubernetes, and other related metrics
Automatic scaling of application containers
Multicloud cost analysis and recommendations based on workload predictions for Kubernetes clusters and VM clusters
Actual cost and potential savings based on recommendations for clusters, Kubernetes applications, VMs, and Kubernetes namespaces
With a ProphetStor Federator.ai license, you can apply an AI-based solution to track and predict the resource usages of Kubernetes containers, namespaces, and cluster nodes to make the right recommendations to prevent costly over-provisioning or performance-impacting under-provisioning. Utilizing application workload predictions, Federator.ai auto-scales application containers at the right time and optimizes performance with the right number of container replicas through Kubernetes HPA or Datadog Watermark Pod Autoscaling (WPA)
.
Separate from this Federator.ai license, an official Datadog Integration
with out-the-box dashboards and recommended monitors is available. For additional information on Federator.ai, you can view the ProphetStor Federator.ai Feature Demo
video.
This application is made available through the Marketplace and is supported by a Datadog Technology Partner. Click Here to purchase this application.
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The ProphetStor Federator.ai Cluster Overview dashboard displays resource usage predictions and recommendations for Kubernetes clusters and nodes and historical usage.
The ProphetStor Federator.ai Application Overview dashboard displays predicted CPU and memory usage and recommendations for each application.
The ProphetStor Federator.ai Kafka Overview dashboard displays usage information and recommendations about autoscaling Kafka consumer replicas.
The ProphetStor Federator.ai Cost Analysis Overview dashboard shows deployment cost of a Kubernetes cluster and recommendations of cluster configuration and estimated cost/savings when it is deployed on public cloud service providers.
Federator.ai dashboard displays workload prediction and resource recommendations for Kubernetes or VM clusters and applications.
Federator.ai provides predictions and resource recommendations for clusters, nodes, namespaces, applications, and controllers
Based on predicted workload of a cluster, Federator.ai recommends most cost-effective cluster configuration for different public cloud provider.
Federator.ai analyzes and projects cost trend for individual namespace.
