Federator.ai logo


Federator.ai logo
Federator.ai logo


By ProphetStor

Certified enterprise ready

Federator.ai helps enterprises optimize cloud resources, maximize application performance, and save significant cost without excessive over-provisioning or under-provisioning of resources, meeting the service-level requirements of their applications.

Software version


Runs on

OpenShift 4.4+

Delivery method


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 instance types based on resource usage.

Application Correlation and Impact Analysis

Federator.ai provides analysis and system recommendations based on the correlation between microservices of an application, providing insights about how individual microservices are impacted by external factors that affect the primary workload. This correlation is crucial for preventing over- or under-provisioning of resources.

Custom Datadog/Sysdig Dashboards

Predefined custom Datadog/Sysdig Dashboards for workload prediction/recommendation visualization for cluster nodes and applications.

Pricing summary

Plans starting at

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Paid Version

Subscription based on number of managed objects

Managed objects include cluster nodes, namespaces, and deploymentConfigs/statefulSets/deployments

Technical support included

Additional resources

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