Managing large-scale VM deployments across platforms like Amazon EC2, Azure, Google Compute Engine, and VMware vSphere can be complex. Each environment has unique configurations, leading to inefficiencies, high costs, and performance issues from misconfigurations and over-provisioning. In hybrid and multi-cloud setups, these challenges are even greater, complicating consistent resource utilization and cost management.
To address these issues, Federator.ai offers an AI-powered solution that provides intelligent, application-aware optimization across these environments. By leveraging machine learning to analyze workload patterns and adjust resource allocations in real-time, it ensures optimal performance and cost efficiency. This approach helps prevent over-provisioning and underutilization, making it ideal for managing diverse VM infrastructures with minimal manual intervention.
Visibility and Optimization of VM Resources
Leverage collected metadata and operational metrics to gain real-time visibility into VM resource usage with Federator.ai. Quickly identify opportunities for optimizing clusters and nodes, enabling accurate provisioning to ensure operational resilience.
Cost Improvement
Optimize infrastructure utilization with Federator.ai by providing accurate capacity projections and recommending optimal instance types and quantities. Assess the TCO of on-premises components, enabling executives to evaluate long-term costs and make cost-effective decisions.