Data warehousing in the AI era starts with sovereignty

The question isn’t on-prem or cloud. It’s control without cost, scale, or innovation trade-offs

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.

Architecturally, this often means adopting open, horizontally scalable platforms that deliver performance through parallelism rather than per-query pricing. Real-time ingestion and hybrid storage also reduce the need for costly ETL pipeline management and redundant storage of duplicated data.

EDB Postgres® AI (EDB PG AI) follows this capacity-first approach with its open data warehouse environment, WarehousePG. With MPP scale-out, hybrid storage via PXF, and predictable per-core pricing, WarehousePG gives teams the freedom to explore and iterate without the anxiety of consumption-based bills.

2. Which workloads make sense to repatriate or offload first?

A big misconception in the cloud warehouse conversation is that improving cost, control, or sovereignty requires a full cloud repatriation, a dramatic rip-and-replace. That fear alone keeps so many stuck with architectures that no longer fit their workloads or strategy.

But the organizations moving fastest aren’t abandoning their cloud platforms altogether. Instead, they’re becoming workload-aware, meaning they choose to run workloads where they are best optimized, most cost efficient, and fully meet governance requirements. It’s less like rebuilding your house from scratch, and more like moving your appliances to the right rooms and outlets to optimize efficiency and energy costs.

The first step is identifying workloads that may not need to live in your cloud platform at all. For example:

• High-volume, predictable reporting jobs
• Repetitive transformations that burn credits without adding value
• Heavy ETL/ELT pipelines that are infrastructure and ops-intensive
• Workloads constrained by data sovereignty and data residency requirements

Gaining control doesn’t have to mean disrupting the analytical front-end your teams currently rely on. WarehousePG is designed for incremental modernization: With petabyte-scale MPP performance, high SQL compatibility, and hybrid deployment flexibility, teams can offload targeted workloads in hours, not quarters. The result is immediate savings (often up to 5x TCO improvement) without destabilizing the existing analytics and BI environment.

3. Is your architecture supportive of a sovereign data and AI platform approach?

As AI scales, the limits of fragmented architectures become glaring. BI lives in one system, ML in another, vector search somewhere else, and real-time data in its own pipeline. Every hop adds cost and delay—enterprises end up paying more to move data than to learn from it. And because pipelines take time to build, and require people (or agents) to manage, models and dashboards often operate on stale signals, slowing AI time-to-value.

A sovereign platform approach solves this by unifying workloads—transactional, operational, analytical, and AI—on a single, coherent foundation in an enterprise’s own controlled environment. That foundation must support the new baseline requirements of modern AI: for example, native vector search, semantic reasoning, in-database inference, real-time ingestion, and the flexibility to run in the cloud, on-prem, or in hybrid configurations that ensure governance and cost control.

EDB PG AI is this sovereign platform. It provides a petabyte-scale analytical engine with WarehousePG, native vector search, MADlib and Python frameworks for in-database machine learning and inference, Flow Server for streaming data in real time, and PXF for direct querying of data lakes—all unified on a Postgres-based foundation. The result: BI, ML, vector search, and AI workloads operate directly where the data resides—without fragmentation and without compromising sovereignty.

Sovereignty is how enterprises take control back

Kyobo Book Center, the largest bookstore chain in South Korea, was seeking a strategic escape from unpredictable and soaring compute costs on their 50TB cloud data warehouse. Kyobo adopted WarehousePG to establish cost control, gain superior performance, and meet strict data residency mandates.

“We have been plagued by runaway costs for querying our 50TB cloud data warehouse. EDB Postgres AI for WarehousePG will give us a way to rein in costs with superior performance — and we can do it with total data sovereignty.”

The next thousand days won’t be defined by a choice between cloud or on-prem. It will be defined by control – and who can build architectures flexible enough to deliver every workload where it performs best, costs least, and complies most fully.

A sovereign, workload-aware strategy doesn’t replace your warehouse. It frees it.

And platforms like EDB Postgres AI for WarehousePG reflect the pattern emerging across high-performing enterprises: regain optionality, reduce dependence, and run AI wherever gravity—and governance—demand.

Sponsored by EDB.