Telemetry Driven Cost Governance for Enterprise Data and AI Platforms

Abstract

Modern organizations are considerate of enterprise data and AI platforms, which usually provoke excessive and unpredictable operating expenses. Dynamism in cost management Traditional cost management systems use manual reviews, and reports that are backwards looking and which are never effective when dealing with dynamic clouds. The paper is suggesting a Telemetry-Driven Cost Governance model which operates on a real-time platform telemetry, automated policy assessment and controlled execution in order to govern costs on a continuous basis. The model combines the compute, storage, and data pipeline telemetry into a single analytics layer and measures the costs against specific preconceived thresholds. The approval gates and safety checks are automated through recommendations that are carried out in order to maintain system stability. A quantitative analysis presents good outcomes. The total platform cost has been cut by 24 percent, and compute cost is lowered by 28 percent and storage cost has been cut down by 19 percent. Effort of manual analysis of costs was reduced 90%. There was an increase in policy compliance of 76-96 and job success rate was 97.8 percent to 98.6 percent respectively. The findings have shown that Telemetry-based governance can provide a proactive, safe, and quantifiable cost optimization of enterprise-level data and AI platforms.