Maximizing Kubernetes Cloud ROI

Comprehensive cloud cost visibility and optimization is now a reality for organizations that need actionable alignment across Engineering, CloudOps, and FinOps to maximize their return on cloud investments.

Organizations gain Kubernetes cost visibility, detailed cost allocation against real-time performance metrics, and fully automated Kubernetes optimization from StormForge’s intelligent machine learning capabilities integrated with CloudBolt’s Augmented FinOps offerings.

Joint Solution Benefits #

• Complete shared cost distribution—Accurately allocate costs and  create non-contested attribution with showback/chargeback based on real container-level performance data. 
• Continuous rightsizing—Dramatically reduce insight-to-action lead time through continuous, automated Kubernetes optimization powered by advanced machine learning. 
• Proactive Kubernetes optimization at scale—Include Kubernetes clusters in organization-wide cost visibility and optimization workflows that have previously been isolated from the rest of cloud spend.

Value Throughout the FinOps Phases #

Inform: Gain visibility into Kubernetes efficiency challenges

In depth reporting into resource cost and usage illustrates what applications are overprovisioned (yielding waste), with granular breakouts to properly build out chargeback models across your estate. 

Optimize: Apply intelligent recommendations to manage cloud costs

Configuring Kubernetes settings properly to ensure efficient consumption of resources is complex. With thousands of settings across dynamic usage, engineers need to balance application performance and reliability with cost efficiency. Machine learning is the perfect solution to manage the thousands of varying parameters required to efficiently run Kubernetes at scale. As usage patterns shift and applications are updated, ML can properly account for these changes and forecast accurate optimizations to apply.

Operate: Ensure a culture of accountability with continuous automation

Finding the problem and fixing it is great, but operationalizing that fix to ensure applications are continuously optimized at scale requires automation. Combining automation with policies ensures this continuous automation is reported and codifies a bias for action.