Federator.ai Reviews

The predictions its machine learning-based engine provides are pretty awesome! Quick, accurate but few resources consumed.

Um… I don’t appreciate its console in a dark black color. Haha..

Actually, I like its features and most of its design in the console to provide information.

Cost savings of course! Its recommendations fit the demand of applications with minimal resources. Give the thumbs up.

Cost savings is the most stunning benefit. Federator.ai shows us cost savings opportunities by avoiding the over-provisioning of resources through its machine-learning predictions and comparing the pricing of resources our operations require from different public clouds.

In practice, every siloed business unit has access to the console, which is good for helping unify the business objectives. But we do need multi-tenancy support for avoiding our parameters being touched by other units.

Thanks to Federator.ai, my team can be warned ahead of time of potential resource shortage before it occurs, which prevents application performance degradation.

It is impressive to provide visibility of the correlations between applications the resources with quantified figures. It provides a solid base for actionable optimization.

Role-based access control to the software is needed so that accounts involved in the project can be managed by different access privileges. It would be better to be multitenant.

Optimization for cost and performance. The current utilization of resources and its predictions can minimize idle resources but supply enough resources for operations.

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

Current performance is good, continue to maintain

NA

K8s,AIOps,Recycle capacity

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

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

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.

The comparison of potential savings in the multi-cloud environment is based on its accurate application workload predictions. It made a meaningful cost comparison and streamlined our decision-making process for workload migration between different cloud service providers.

I really haven't found anything to dislike about Federator.ai. It saves a lot of time for our team on the cloud migration issue.

It benefits us a lot in cloud migration assessment and resource planning. The setup was easy and I enjoyed their awesome technical support.

Its self-evolved machine-learning algorithms can pretty much accurately capture application workloads. It gives me the confidence to follow its timely recommendations to allocate cloud resources.

It’s not a SaaS application, it’s a standalone containerized application running on Kubernetes. Some industries with a strict information security policy, such as banking or telecom, will love it, but I personally prefer SaaS.

In my company, we’ve got both VM (on-prem) and Kubernetes (cloud) environments, and it is difficult to have a comprehensive view of resource utilization. Federator.ai do release the drudgery of resource management. Resource utilization can be clearly monitored and easily optimized on both on-prem and cloud environments.

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.

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

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.

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.

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.

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.

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

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.

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.

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

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.