Data gravity and cloud risks: why critical decisions should remain on-premise

In the era of generative AI, it is data—not servers and applications—that is becoming the primary source of power over businesses, processes, and entire economies. Data gravity means that wherever the data is physically located, that is where the "brain" of the organization is located and where decisions are made; the question is no longer "should we move to the cloud," but "which decisions must remain sovereign on-premise, under the full control of the data owner."

Data gravity in the age of AI

Data gravity is a phenomenon in which growing data sets "attract" applications, models, and decision-making processes closer to themselves because moving data is expensive, slow, and risky. In practice, the place where you keep your operational logs, transactional data, and technical documentation becomes the natural decision-making center of the organization.

In the SaaS model and the classic public cloud, the center of this gravity is very often located outside the company's infrastructure and even outside its jurisdiction: this is where models are trained, customer behavior is analyzed, and recommendations are made, which the management only formally approves. Over time, decisions become a function of someone else's infrastructure and algorithms, and the organization loses its actual autonomy over the decision-making process.

Latency, p99, and real SLAs

AI generates a constant stream of requests to large, often unstructured sets; any additional delay reduces the quality of the experience and the effectiveness of teams. In the cloud, the main barrier is the network: packets fly through carrier links and the Internet or private VPNs, introducing jitter and latency spikes that break p95/p99 SLAs in real-time systems.

On-prem, data and compute are on the same L2/L3 network, often in the same rack, with 25/40/100 GbE links and a deterministic path to storage. GPUs do not wait for data, it is easier to maintain high resource utilization, and OT systems, production floors, and trading can realistically rely on near real-time inference.

Cloud risks in US-EU relations

For many European companies, the cloud in the EU region seemed to be a compromise between convenience and regulations, but the law does not end at the borders of the data center – ownership and jurisdiction over the provider are key. The CLOUD Act and FISA 702 allow US authorities to access data stored by US companies, even if the servers are located in Europe.

The Schrems II ruling invalidating the Privacy Shield enforces additional safeguards and individual risk assessments for transfers to entities subject to US law; the "EU region" in the cloud alone does not guarantee GDPR compliance or data sovereignty. The financial, healthcare, administration, and defense sectors cannot assume that a foreign hyperscaler solves the problem of jurisdiction and oversight for them.

From compliance to sovereignty

Compliance is now the minimum requirement; the goal is sovereignty, understood as the ability to independently shape data and AI policy, regardless of the interests of foreign countries and corporations. For Europe, this means the need to have its own infrastructure, its own models, and its own data ecosystems, in which European institutions define the rules of the game.

Industry aspects of sovereignty: sensitive data, IP, advantage

In the financial sector, energy, health, public administration, and advanced industry, data is simultaneously evidence, a strategic resource, and a map of the system's weakest points. Loss of control—even without a high-profile leak—can mean a loss of negotiating leverage, the ability to defend critical infrastructure, or the pace of innovation.

In banking and insurance, data reflects credit risk, customer behavior, fraud, and household microeconomics; if the models assessing this risk operate in an environment accessible to foreign regulators, this creates a risk of "transparency" for the European financial system. In administration, tax and health data and population registers create a picture of society, the analysis of which by foreign entities would be a powerful tool of influence.

In the energy sector, data from the grid, SCADA, demand forecasts, and renewable energy production create a digital twin of critical infrastructure; analyzing this data allows for both market optimization and the identification of points vulnerable to sabotage. In industry and R&D, sovereignty concerns models, know-how, process parameters, recipes, and design documentation—the foundation of advantage over companies from the US and China, which often have cheaper capital and a larger domestic market.

When these resources end up in global cloud services, the organization gives up not only information, but also the ability to deeply analyze the sources of its own advantage; any leak, takeover, sanction, or change in the law in the operator's country immediately translates into business and political risks.

Transmission and egress costs: when gravity becomes a tax

With petabyte-scale data warehouses, multimodal archives, and telemetry logs, data gravity turns into a real "tax on innovation." Every experiment that requires moving data between regions or clouds generates high fees for egress, cross-region, and cross-cloud, as well as additional delays and attack surface.

In an on-prem sovereignty model with modern object storage, you scale capacity in your own DC, don't pay to "pull" your own data, and locate compute where critical data sets physically reside. This allows you to aggressively experiment with models and agents without the risk of cloud bills destroying the ROI of AI projects.

Sovereignty in the US-China-Europe race

The US controls global cloud platforms and Big Tech aggregating petabytes of data, China combines massive AI engineering with a state strategy of technological expansion, and Europe has for years ceded control of its digital infrastructure to entities outside the EU.

The "sovereign cloud" and "sovereign AI" strategies are an attempt to reverse this trend: data and decision-making systems critical to the economy are to be subject to European law and processed on infrastructure controlled by EU entities. Without such a shift, Europe risks becoming a data supplier and market for other countries' models, with key business and public decisions being made on foreign infrastructure.

The architecture of sovereign on-prem AI: layer by layer

For the "institutional on-prem brain" to be more than just a metaphor, the architecture must be precisely defined.

• Hardware layer – server clusters with GPUs (e.g., H100/L40), fast NVMe as local cache, and scalable object storage (S3-compatible) for source data, vectors, and archives, which minimizes "waiting for data" and maximizes GPU utilization.

• Network and segmentation – 25/100 GbE spine-leaf architecture, VLAN/VXLAN segmentation, separate OT/IT domains, and QoS for inference/training traffic ensure deterministic, predictable paths to data.

• Storage and sovereignty – Object-based, software-defined storage with built-in encryption, WORM/immutability, and air-gap (logical or physical) for critical copies provides cloud flexibility without giving up control over replication location and metadata.

• Orchestration layer – Kubernetes and containers with node affinity policies, namespace isolation, and separation of regulated environments from less critical ones combine security requirements with DevOps flexibility.

• Cognitive layer (ECS) – An enterprise-class cognitive system, such as SAVANT-AI, that handles data ingest, RAG, agent orchestration, auditing, and governance- ly within this infrastructure, without "calling the cloud."

In the air-gapped variant, the entire system is physically cut off from the Internet, eliminating channels through which data, logs, or metadata could leak outside – a requirement in defense, intelligence, and energy sectors, among others.

Why critical decisions should be kept on-premise

If data gravity means that decisions follow data, keeping key data sets on-premise allows you to keep decision-making gravity on the organization's side. Instead of sending data to someone else's cloud, we bring AI models and agents into our own server room as a sovereign cognitive system operating within boundaries defined by the company and regulators.

Sovereign AI architectures based on GPUs, object storage, network isolation, and containerization achieve cloud-like performance without relinquishing control over data and decision-making code. The organization decides which models to run, how long to store inference logs, who can audit decisions, and how data is used in subsequent projects—which is critical in banking, energy, and defense.

SAVANT-AI as a sovereign "institutional brain" on-prem

SAVANT-AI embodies this philosophy: instead of yet another cloud application, it is a sovereign Enterprise Cognitive System installed on the customer's infrastructure. The system combines data from ERP, MES, PLM, legal systems, technical documentation, and open data sources into a single "nervous system" for the organization, and the entire process—from ingestion, through model training, to recommendation generation—remains under the full control of the data owner.

As a result, data gravity remains on the organization's side: SAVANT-AI "approaches" the existing data, rather than the data migrating to an external silo in the cloud. For regulated industries and companies building advantages in R&D, this means that the digital brain of the organization remains where it should be—in its own sovereign environment, immune to geopolitical tensions and regulatory changes.