Performance Optimization and Cost Control in Snowflake: A Strategic Approach

Abstract

As organizations increasingly migrate critical data workloads to cloud-native platforms, Snowflake has emerged as a leading data warehouse solution offering flexibility, scalability, and performance. However, its utility-based pricing model introduces new complexities in managing cost and optimizing performance. This review provides a strategic analysis of Snowflake’s architectural elements, AI-driven optimization approaches, cost governance techniques, and workload management best practices. Experimental results demonstrate that intelligent orchestration, auto-scaling, and query optimization can lead to performance gains of up to 50% and cost savings of 30–50%. The paper also introduces a theoretical model that combines AI prediction, policy enforcement, and dynamic warehouse orchestration to enable adaptive, cost-efficient resource management. We conclude with future research directions emphasizing real-time analytics, deeper AI integration, and multi-cloud interoperability.