What is Federator.ai?
General
Why do I need Federator.ai?
The Wall Street Journal has identified the optimization of operational costs to improve the bottom line as the top priority for CIOs, as many enterprises struggle to increase their top line. Digital transformation involves challenges with complex operational environments and unpredictable resource demands that even sufficient skilled IT manpower may find difficult to manage.
This is where Federator.ai comes in – an AI-powered solution that helps enterprises optimize IT operations, from on-premises to MultiCloud environments. By integrating with popular IT operation monitoring software available in the market, Federator.ai uses machine learning algorithms to predict the dynamic needs of applications. It then proposes resource scaling recommendations and automates continuous optimization to ensure that IT operations run smoothly and safely with minimal resource utilization.
Federator.ai not only frees engineers from time-consuming tasks allowing them to focus on high-value tasks but also bridges the gap between operation APIs and corporate KPIs for the executives to drive business growth with excellent digital transformation.
Does Federator.ai fit my company’s needs?
No matter if you are struggling with managing the patchwork of legacy and cloud technology, enduring concerns about opportunity costs with infrastructure upgrades, or facing challenges with migrating from one cloud to another, Federator.ai is an excellent AIOps tool to assist you.
Federator.ai offers AI-based resource allocation recommendations for VM clusters and Kubernetes clusters in on-premise, hybrid cloud, and MultiCloud environments. Its intuitive interface provides operational intelligence through charts and tables for resource utilization, application- aware primary performance metric assurance, cost trends and management, and MultiCloud cost optimization. With Federator.ai, enterprises can make informed business decisions during their digital transformation journey.
Is Federator.ai a cloud-based SaaS application?
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.
What infrastructure can Federator.ai optimize?
Federator.ai can provide visualization, prediction, and optimization on CPU, memory, storage, and network bandwidth on VMware vSphere and Tanzu, as well as public clouds like AWS, Azure, Google Cloud, and IBM Cloud.
How long will Federator.ai take to produce operational predictions?
Federator.ai acquires operational data collected by monitoring services. If those monitoring services have already gathered operational data from the clusters for a certain duration, Federator.ai can retrieve the historical data from those monitoring services and produce predictions for the clusters 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.
Why can Federator.ai deliver such a quick prediction with little consumption of resources?
Federator.ai utilizes a machine learning algorithm to create various feature models that mirror real operations, also known as Digital Twin.
With the help of AI, hundreds of features in the application workloads can be rapidly identified and incorporated into the models, ensuring that the predictions provided by Federator.ai are both fast and reliable.
What is the most suitable environment for this solution?
Federator.ai is best used for managing and optimizing resources for applications on Kubernetes and virtual machines (VMs) in VMware or AWS EC2 clusters. It provides AI-based workload predictions, resource recommendations, correlation and causality analysis, automatic scaling, and MultiCloud support.
Features & Benefits
Why can Federator.ai produce reliable and sustainable optimization for operations?
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.
What is Federator.ai’s patented Multi-layer Cascade Causal Analysis?
This technology is a holistic approach to IT and cloud resource management by efficiently identifying and classifying application workload patterns and pinpointing correlations between application metrics and IT and cloud stack resources. Federator.ai solution leverages its in-depth understanding of the correlations between hardware/cloud resources, Kubernetes layers (cluster, node, pod), and applications to provide unique application-aware resource planning and optimization capabilities for enterprise and data center IT/Cloud Operations.
How can Federator.ai help you to make cost-effective decisions for data migration?
By using the accurate workload predictions provided by Federator.ai, you can easily compare the costs of on-premises resources with self-defined pricing against those of public cloud resources from published price books in various regions. Based on this analysis, you can identify the most cost-effective data migration scenarios, whether it involves moving data from on-premises to the cloud or between different cloud providers. This enables you to optimize your cloud spending and determine the best migration strategies.
Does Federator.ai automatically adjust container resource usage based on its recommendations?
Does Federator.ai support automation?
- Auto resource provisioning to adjust the right amount of resource usage for containers and namespaces.
- Prediction-based HPA that automatically scales containers based on predicted workloads.
- Application-aware scaling for Kafka consumer and Web Services using NGINX ingress controller.
What allows Federator.ai to be application-aware?
Federator.ai is designed to optimize IT cloud operations by being application-aware. It accomplishes this by using machine learning to analyze the behavior and performance of various applications running on a cloud infrastructure. By doing so, it can dynamically adjust resource allocation and optimize system settings to improve application performance and reduce costs. Additionally, Federator.ai is able to detect and respond to potential issues before they impact the applications, allowing for more resilient and reliable cloud operations.
Why is knowing the correlation between applications important for optimization?
For example, if one wants to increase the primary application workload by 20%, equally adding 20% CPU, memory, and bandwidth to every microservice is not efficient. Federator.ai’s correlation and impact analysis helps allocate necessary resources like CPU to the most relevant microservices to achieve performance objectives.
How can cloud instance procurement be optimized?
Using machine learning-based analysis, Federator.ai quickly classifies workload patterns for individual applications based on historical data. While ensuring optimal performance, Federator.ai identifies applications with steady-state production workloads or those with flexibility in start and end times. By maximizing the proportion of reserved and spot instances over more expensive on-demand instances, Federator.ai recommends the optimal combination of cloud instances for both application resilience and cost savings.
Integrations & Environments
Does Federator.ai make resource recommendations for non-Kubernetes VM clusters?
Yes, Federator.ai provides resource predictions/recommendations and cost analysis for the VMware VM clusters and AWS/ GCP/ Azure VM Clusters.
Does Federator.ai support any CI/CD integration?
Yes, Federator.ai can be integrated with Gitlab and Terraform when deploying applications to Kubernetes clusters.
What monitoring services does Federator.ai support?
Federator.ai supports metrics from Datadog, Sysdig, and Prometheus for Kubernetes clusters and VMware vCenter, AWS CloudWatch, Google Cloud’s operations suite , and Azure Monitor 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.
Can Federator.ai be used to monitor IT operations without employing additional monitoring services?
No. Federator.ai is an agentless solution that partners with monitoring services and focuses on analyzing metadata that comes from those monitoring services.
Can Federator.ai manage cloud resources purchased from a local MSP?
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.
The resources requirements to run Federator.ai?
Federator.ai has a minimum requirement of 5.1 CPU cores and 5.1 GB of memory. The actual CPU and memory requirements of Federator.ai depend on the size of the clusters being monitored and the number of applications being tracked. In some cases, these requirements may increase to as much as 22 CPU cores and 42 GB of memory.
To operate properly, Federator.ai also requires a StorageClass with at least 176GB of storage and ReadWriteOnce access mode. Furthermore, for the AI Engine to function correctly, there must be at least one worker node with a minimum of 2 CPU cores (with a maximum limit of 8 cores) and 1 GB of memory.
Security
Do I need to install agents of Federator.ai?
Federator.ai employs an agentless data collection approach. It utilizes metadata via APIs from monitoring services you adopt and provides its patented analysis to facilitate excellent operations.
Is there a risk of data leakage if I use an open API to transfer metrics data to Federator.ai?
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.
Installation
Is Federator.ai a cloud-based SaaS application?
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.
Where can Federator.ai be installed?
Federator.ai can be installed on any Kubernetes cluster, whether in a public cloud or on-premise data center. One installation of Federator.ai allows you to manage multiple clusters, while you can also install Federator.ai on specific clusters to execute HPA (Horizontal Pod Autoscaling).
Do I need to install Federator.ai agent in every cluster node/VM in a cluster?
From where can I install Federator.ai software?
Training
Pricing & Licensing
What term length options are available on Federator.ai?
Federator.ai is available for a yearly subscription and paid annually.
How do I know the number of licenses of Federator.ai I should make for purchase?
When you contact our sales team, we will help you estimate the requirements for the number of licenses your operation needs.
How does Federator.ai charge for a hybrid deployment using both on-premises and managed cloud resources?
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.
How much is a license of Federator.ai?
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.
Can I have a free trial of Federator.ai?
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.
