This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17992880. This paper addresses an important and timely gap in cloud data analytics by arguing that monetary cost should be treated as a first-class optimization objective. As analytical workloads increasingly move to usage-based cloud platforms, traditional database research focused on performance under fixed resources becomes insufficient. The authors introduce the concept of cost intelligence and propose an architectural vision for cloud data warehouses designed to balance performance and cost explicitly. A key strength of the paper is its clear identification of two foundational challenges: automatic resource deployment and cost-oriented auto-tuning. By framing these challenges as system-level concerns, the paper moves beyond incremental tuning techniques and highlights architectural components that are largely absent from current cloud data warehouse platforms. This framing provides a useful research agenda that aligns well with real-world operational challenges in large-scale analytics environments. The paper is primarily conceptual and does not include empirical validation or prototype implementation, which limits direct assessment of feasibility. Nevertheless, as a vision and problem-framing contribution, it effectively motivates cost-aware analytics as a necessary direction for future cloud data warehouse research. Competing interests The author declares that they have no competing interests. Use of Artificial Intelligence (AI) The author declares that they did not use generative AI to come up with new ideas for their review.