FinOps AI, Optimization of cost and performance control

Cost Optimization and Performance Control in Hybrid Multi-Cloud Agentic AI Environments

Rapidly escalating cloud expenses and unpredictable performance pose significant challenges for organizations deploying Agentic AI applications in hybrid multi-cloud environments.

Webinar

This webinar introduces modern cost optimization, performance control, and FinOps AI cloud solutions. This approach leverages automated observability alongside advanced modeling and gradient optimization techniques.

Automated observability enables precise workload tuning. Learn how to:

•  Identify the most resource-intensive applications
•  Pinpoint queries with the highest resource usage and failure rates
• Highlight applications with substantial data spills to local and remote storage

Modeling and optimization capabilities then determine optimal configurations, resource allocations, and workload management changes. You can then accurately predict budgets to consistently achieve Service Level Goals (SLGs) across all hybrid multi-cloud environments. These modeling results mitigate financial and operational risks.

We reviewed practical scenarios through detailed case studies, including pre-deployment application sizing, cloud platform selection, optimized cloud migration strategies, and dynamic capacity management. Additionally, we discussed the results of validating recommendation accuracy by comparing predicted and actual cost and performance outcomes.

Enterprise and cloud architects,  Data center operations managers and  FinOps professionals gained insights:

• How to automate observability to improve effectiveness of tuning efforts and building analytical models
• Techniques for applying modeling and optimization to choose minimal configurations and budget needed to meet SLGs for each workload
• Best practices for establishing continuous cost optimization and performance control processes