What is Federator.ai?
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.
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.
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. Federator.ai supports a few different types of 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.
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.
If I purchase cloud resources from a local managed service provider (MSP), can Federator.ai help to manage them?
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.
If I provide an open API to transfer metric data to Federator.ai, will it lead to an issue of data leakage?
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.
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.
Pricing and Licensing
Federator.ai is available for a yearly subscription and paid annually.
If the deployment in my operations uses both on-prem and managed cloud resources, how will I be charged?
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.