The question isn’t on-prem or cloud. It’s control without cost, scale, or innovation trade-offs
PC Enterprises are hitting a familiar inflection point: cloud costs keep rising, control keeps slipping, and AI workloads are exposing the limits of a cloud-only data strategy. At the same time, the pressure to build an internal, sovereign AI and data platform is accelerating. While 95 percent of enterprise leaders plan to build their own platform within the next thousand days, only 13 percent are actually this today.
Those who are succeeding are already seeing up to five times ROI, largely because they’ve established sovereign, AI-ready foundations that unify data, governance, and operational control. And there’s a clear pattern among these high performers: 42 percent are running on hybrid infrastructure. This approach gives them what the cloud-only model cannot: seamless deployment flexibility, cost discipline, and sovereignty at scale.
This is the real dividing line. Not cloud versus on-prem, but whether you control your AI and data – where they live, how workloads run, and what it all costs – wherever, whenever, and however you need.
Data warehouse solutions sit at the intersection of enterprise analytics and AI strategies, because they represent the single source of truth for accurate, consistent, and reliable data. If you feel boxed in by a cloud data warehouse model that can no longer support your modern workloads or your economic model, here are the questions that can help you regain clarity, control, and choice. 1. Are you paying for the volume of queries, or the value of the insights?
Cloud consumption economics were supposed to democratize analytics. In practice, they’ve created cottage industries around metering management. With SaaS data platforms, every query, spike, and model training run becomes a line item. It’s a strange dynamic: in the era where curiosity should compound advantage, analytics is treated like a utility bill.
Leading enterprises are shifting to capacity-based models, where cost is tied to available horsepower, not the number of times the engine is revved. This encourages deeper analysis, supports AI training and feature exploration, and removes the tax on simply asking more from your data.
