Cloud instances provide flexible and scalable workload management, allowing new instances to be launched easily for application growth. However, launching the right types and quantities of instances is essential for optimal performance and cost savings.
Federator.ai uses machine learning-based analysis to quickly classify workload patterns from historical data and recommend suitable cloud instance portfolios to meet application demands. It ensures optimal performance by providing the necessary resources—CPUs, memory, storage, and network capacity—and prioritizes the use of cost-effective reserved and spot instances to minimize costs.
Federator.ai can achieve both the application resilience and cost savings by quickly identifying, classifying, and predicting the application workloads with its machine learning-based analysis
Enhanced Application Resilience
Identify application workload patterns and generate ML-based predictions to ensure optimal performance by allocating the right instance types at the right time.
Optimized Cost Savings
Maximize cost savings by prioritizing reserved and spot instances over on-demand ones, offering optimal portfolio recommendations that meet application demands while reducing procurement costs.
A win-win sales strategy for MSPs
Provide MSPs with tools to create lucrative instance portfolios that maximize margins and minimize end-user costs, fostering strong, long-term client relationships.
Example Scenarios for MSP
Video | Win-Win Sales Strategy for An MSP and Its End-Users
Shasta Ho
CEO of Nextlink
