Federator.ai logo

Federator.ai

Federator.ai logo
Federator.ai logo

Federator.ai

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

4.6.1

Runs on

OpenShift 4.4+

Delivery method

Operator

Rating

5 reviews

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.

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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Free 30-day Trial

No restriction on the number of CPU cores

Federator.ai trial version

Additional resources

Want more product information? Explore detailed information about using this product and where to find additional help.

Reviews

Read what others are saying about this product in our review section.

Showing 1-5 of 5

  • RC

    Ring C.

    Review source: Organic
    Review source: Organic
    Not ratedFeb 26, 2021

    "Experience in using Federator.ai"

    What do you like best?

    Federator.ai’s resource planning feature much helps with resource allocation planning for my applications. The cost allocation feature shows the real and potential usage per namespace provides detailed insights into how cluster resources are used.

    What do you dislike?

    Federator.ai neither provides a well formatted (better customizable) report that summarizes the resource usage and planning of applications nor provides the automation feature to apply the resource recommendations automatically.

    Recommendations to others considering the product:

    I suggested to provide online chat services in order to enable the participants to respond quickly.

    What problems are you solving with the product? What benefits have you realized?

    I have Kubernetes clusters running many container applications. Allocating proper resources to ensure my applications won’t become unstable because of insufficient resources and won’t waste too many resources because of unnecessary over-provision has always been one of my primary concerns when administrating my clusters. Federator.ai provides the resource usage monitoring and allocation recommendations that reduce a lot of management efforts for me.

  • AS

    Administrator in Computer & Network Security

    Review source: Organic
    Review source: Organic
    Not ratedApr 15, 2021

    "Best Resource Management tool with AI-enabled for Kubernetes"

    What do you like best?

    Continuously collecting workload metrics from infrastructure to application, provide predicted resource allocation recommendation to reduce cluster resource waste, also saving cost regardless of on local or public cloud. It is a high ROI for my team.

    What do you dislike?

    If it can support multi-tenant, that would be better for my company and other application teams.

    Recommendations to others considering the product:

    It is very helpful to manage Kubernetes resource and right-sizing recommendation, saving money as well. All of the recommendations all based on workload and prediction. It is very nice.

    What problems are you solving with the product? What benefits have you realized?

    Most application resource allocation is overprovision, which has wasted much Kubernetes cluster resource and money. Federator.ai provides right-sizing based-on workload prediction results, reduce the waste situation. We could release more resources for other applications.

  • WB

    Willem B.

    Review source: Organic
    Review source: Organic
    Not ratedMar 18, 2021

    "Robust production ready AI engine"

    What do you like best?

    Automated resources reservation planning based on AI recommendations. This is raising consolidation on our clusters, with an automated process.

    What do you dislike?

    Licensing model based on central processing unit (CPU) core.

    What problems are you solving with the product? What benefits have you realized?

    It solves Kubernetes main problem: waste of resources. This is impactful on the whole chain. We are truly involved in Green IT, and the waste of resources from upper layers ruins all our efforts we are making on the infrastructure side. Moreover it solves the resources reservation and HPA configuration which is a pain point for developers and devops teams.

  • US

    User in Computer Software

    Review source: Organic
    Review source: Organic
    Not ratedFeb 22, 2021

    "Auto-Scaling via resource recommendation"

    What do you like best?

    All applications are automatically scaled to meet the predicted resource usage, I do not have to worry about over provisioning for anything or shut down servers as usage goes down.

    What do you dislike?

    I have not found something that I dislike about auto-scaling as of yet.

    What problems are you solving with the product? What benefits have you realized?

    It solving main business problems of health and load on our web and applications. Being able to reduce unnecessary spending and increase application service quality.

  • AP

    Administrator in Architecture & Planning

    Review source: Organic
    Review source: Organic
    Not ratedMar 26, 2021

    "Based on K8s, the automated resource plan recommended by AIOps, it performed very well"

    What do you like best?

    Based on K8s, the automated resource plan recommended by AIOps, it performed very well

    What do you dislike?

    Current performance is good, continue to maintain

    Recommendations to others considering the product:

    NA

    What problems are you solving with the product? What benefits have you realized?

    K8s,AIOps,Recycle capacity