Federator.ai® Questions?

What is Federator.ai?

ProphetStor Federator.ai is an Artificial Intelligence for IT operations platform that utilizes the operation metrics already collected by Prometheus, Datadog, or Sysdig to further provide values to automate operations and optimize cost and performance. Federator.ai uses machine learning technologies and accurately predicts the future workload and resource demands, which could be used in rightsizing the resource allocations for applications, performing cluster capacity planning, as well as autoscaling the right number of container replicas just in time for workload demands.

Features and Benefits

Federator.ai acquires operational data collected by monitoring services. As long as those monitoring services provide enough operational data (at least a month is recommended), Federator.ai can produce its predictions within a few hours.
The longer the monitoring services feed data to our machine learning-based algorithms, the more accurate the result of predictions can be presented by Federator.ai.

Federator.ai utilizes a machine learning algorithm to build different featured models mirroring real operations, aka Digital Twins.
With the help of AI, hundreds of the features in the application workloads can be rapidly identified and inducted to fit models so that the predictions Federator.ai provides can be fast but reliable.

Federator.ai understands application behaviors and the resources they consume at different layers, from controller level to cluster level, to produce a Multi-layer Cascade Causal AI Analysis.
With insights into the intricacies of multi-layer correlations, Federator.ai can continuously optimize resource allocation in operations with minimized impact on critical workloads and, therefore, make its optimization actionable and sustainable.
No matter you are suffering from managing the patchwork of legacy and cloud technology, enduring the concerns of opportunity cost with the infrastructure upgrade, or struggling with the migration from one cloud to another, Federator.ai is an excellent AIOps tool to help.

Federator.ai uses an AI algorithm to predict and recommend the right amount of resource usage for containers, namespaces, and clusters. In many cases, Federator.ai’s recommendations lead up to 70% of cost savings.
Since the usage of resources can be precisely predicted, you can also take advantage of the cost comparisons with different public clouds, so that the opportunities for potential savings in the future can be captured.

Yes, with policy settings, Federtor.ai can apply the resource recommendations of a container automatically.

Yes. Federator.ai supports a few different types of automation:



  1. Auto resource provisioning to adjust the right amount of resource usage for containers and namespaces.
  2. Prediction-based HPA that automatically scales containers based on predicted workloads.
  3. Application-aware scaling for Kafka consumer and Web Services using NGINX ingress controller.

Yes, Federator.ai provides resource predictions/recommendations and cost analysis for the VMware VM cluster and AWS VM Cluster.

Yes, Federator.ai can be integrated with Gitlab and Terraform when deploying applications to Kubernetes clusters.


Federator.ai supports metrics from Datadog, Sysdig, and Prometheus for Kubernetes clusters and VMware vCenter and AWS CloudWatch for VM clusters.
If the monitoring services you are adopting are not listed above, our engineering team can still integrate with them as long as they provide open APIs for Federator.ai to connect.

Resources like CPU, memory, storage, and network bandwidth running on top of VMware, AWS, Azure, Google Cloud, and IBM Cloud can be visualized, predicted and optimized.

Yes. As long as those resources or cloud instances are originally from popular public cloud providers, like AWS, Azure, Google Cloud, and IBM Cloud, they can be governed by Federator.ai.


Federator.ai employs the way of agentless data collection. It utilizes metadata via APIs from monitoring services you adopt and provides its patent analysis to facilitate excellent operations.

Federator.ai only collects metadata of your IT operations. Those operational metadata are a series of values of quantity at successive times, which involves no confidential information about your business.


At this time, Federator.ai is a standalone containerized application running on Kubernetes. It could be installed on any Kubernetes cluster either in the public cloud or on-premise data center.

Federator.ai can be installed on any Kubernetes cluster, either in public cloud or on-premise data center. One installation of Federator.ai helps you manage multiple clusters, while Federator.ai can be installed on specific clusters for executing HPA.

Federator.ai is available on RedHat MarketplaceAWS MarketplaceSUSE/Rancher Marketplace, and Datadog Marketplace. Federator.ai can also be installed directly to any Kubernetes cluster. Please submit your request for a free software download on our website.


No. Federator.ai can offer information to those who are concerned, from the development team to the operation team. We will arrange training sessions for your engineers involved, and also provide technical support via documentsvideos, email, and phone.

Pricing and Licensing

Federator.ai is available for a yearly subscription and paid annually.

When you contact our sales team, we will help you estimate the requirements for the number of licenses your operation needs.
A license of Federator.ai applies to both VMs and Kubernetes clusters. The number of objects, including nodes, namespaces, and controllers, will be summed up and charged as a whole, so you don’t need to pay for them separately.
The number of objects that Federator.ai governs differs from the operation environments. The total requirement of your operations should be evaluated first by our engineer and then our sales team will offer you a quotation.

Federator.ai comes with a free-tier license to manage up to 10 objects (cluster nodes, namespaces, containers). A regular license is required to run Federator.ai for more managed resources.

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