Digital Realty provides a physical home for private AI

Digital Realty is a data center company that supplies colocation and interconnection infrastructure. For AI projects, that means a physical place for computing equipment, the power and cooling it requires, and connections to data, clouds and service providers. It is particularly relevant to enterprises deciding how much AI infrastructure to own while retaining access to an external ecosystem.

01 / ProductA place to operate AI hardware close to data and networks

The AI solutions overview positions PlatformDIGITAL for private, hybrid and sovereign AI environments. Those are deployment strategies rather than one packaged model service. Customers still need to specify their computing hardware, model software and operating responsibilities, either directly or through partners.

High-Density Colocation adds cooling options for dense AI and high-performance computing installations. The page describes configurations from 30 to 150 kW per cabinet and larger multi-megawatt deployments. That published range is a planning signal, not proof that every building has the same ready capacity or can accept any rack without engineering review.

ServiceFabric supplies a virtual interconnection layer between locations, clouds and services. It complements the building rather than replacing it. A well-placed rack can still be ineffective if the application cannot obtain data at the required rate or communicate with the systems where results must be used.

02 / AudienceEnterprises choosing the location and ownership of private AI

Digital Realty is relevant when an organization wants its own AI hardware or a partner-managed deployment in a connected facility. A useful case is sustained inference near an existing enterprise dataset, with selective use of public cloud services. Another is a regional deployment whose physical location must fit a particular governance requirement.

The model is less direct for a small team that only needs occasional generation through an API. Colocation introduces equipment procurement, lifecycle planning and operational coordination. It can be appropriate at scale, but those responsibilities should be justified by the workload rather than by an assumption that owning servers is always cheaper.

The Equinix blueprint provides a related perspective on interconnected infrastructure and private AI. The CoreWeave blueprint describes a GPU cloud route with a different division of responsibility. A buyer should first decide whether it needs a facility for its hardware or managed compute, then compare offers at the same level.

03 / WorkflowA proposed private inference deployment for enterprise documents

Consider an engineering company running a document assistant over proprietary design records. This is a proposed architecture, not a tested Digital Realty deployment. Start by mapping where the documents are stored, who can access them and where users need responses. The correct facility location follows those flows and constraints; it should not be chosen solely from a list of cities.

Separate the persistent workload from occasional peaks. The enterprise may host a steady inference service on its own hardware and use an approved cloud route for a different non-sensitive task. Document which data can cross each boundary. A private connection reduces reliance on a public network path, but it does not grant permission to move every dataset to every connected service.

Scope the hardware with the server and facility teams together. Provide expected rack load, thermal requirements, networking topology and growth stages. For liquid cooling, define the interface between the facility and the IT equipment, including operating temperatures, service ownership and maintenance access. A cabinet power figure is only one input to acceptance.

The NVIDIA partnership page describes DGX-ready colocation and a partner ecosystem. Use that as a route into a specific technical discussion, not evidence that any advertised GPU system is immediately available in a selected location. Confirm the exact hardware design, facility readiness and partner responsibility in the proposal.

Build the connectivity plan around the real application. Identify the source repository, inference service, identity system and monitoring destination. Specify which connections need diversity and how the application behaves when one path is unavailable. Preserve document-level permissions in the retrieval service, so physical privacy does not become a substitute for access control.

The data center services page describes physical assistance such as equipment installation, patching, inventory and power cycling. Assign which tasks Remote Hands may perform and what requires an application engineer. A server reboot is not the same as restoring a retrieval index, confirming model readiness or validating an answer against the correct document revision.

Before production, require an integrated handover covering equipment records, connectivity, escalation and recovery. Check a representative user journey and a planned maintenance scenario. These are proposed acceptance activities for the customer and its partners; this article does not report measured latency, successful failover or an independently audited facility outcome.

04 / PricingThe commercial model combines facility and service scope

The reviewed pages direct buyers to an expert discussion rather than a universal per-GPU tariff. High-density cabinets, larger suites, connectivity and physical services are different parts of the purchase. A meaningful comparison needs the same location, capacity, contract duration and operating responsibilities on both sides.

Ask the proposal to separate reserved facility capacity from measured consumption and identify the applicable power arrangement. Include connections, installation work and recurring support where required. The application’s total cost also includes its servers, storage, software and staff unless a partner explicitly supplies them within the agreement.

For the document assistant, model utilization matters. An owned cluster sized for occasional peaks can leave expensive capacity unused; a smaller steady installation may need an external overflow route. Compare those architectures using the organization’s actual demand profile, data restrictions and response expectations. A low facility rate cannot compensate for unsuitable hardware sizing.

Expansion deserves its own commercial treatment. Available space nearby, an option to expand and committed future capacity are different things. Specify when additional power and cooling can be delivered and how the price changes. The contract should make clear which party carries the risk if the compute hardware arrives before the facility is ready.

05 / DistinctionsConnectivity changes the private AI deployment question

Digital Realty’s combination of facility infrastructure and interconnection is meaningful for data-intensive applications. The question becomes where the compute should sit relative to the data and other services. That can be more consequential than moving the entire workflow into one public cloud because its model API was easiest to prototype.

ServiceFabric also describes programmable connectivity through MCP. Treat this as an infrastructure control capability that requires governance. A proposed automation should have approved destinations, limited permissions and an auditable change record. The existence of an agent interface does not justify allowing a model to create or alter production connections without a defined operating policy.

The physical service layer can make remote ownership more practical by supplying on-site assistance. Its useful boundary is concrete: rack and stack, cabling or visual inspection can be delegated, while application interpretation remains elsewhere. This distinction helps prevent support gaps where each supplier believes another party owns the restoration of service.

06 / QuestionsWhich location can deliver the complete requirement?

Location-level evidence is essential. Confirm the actual cabinet density, cooling method, network availability and deployment schedule for the chosen facility. Portfolio-level capabilities do not prove that the same combination is ready in every market. Ask the technical team to document any work required before accepting the proposed equipment.

Physical sovereignty also needs a careful definition. A server’s address is only one part of the data story; remote administration, backups, telemetry and connected model services can cross other boundaries. Match those flows to the organization’s own requirements and obtain the relevant contractual commitments. The public AI page is not a compliance determination for a particular workload.

Finally, decide who owns the application when something fails. Digital Realty, a hardware vendor, a managed-service partner and the customer may each operate a layer. Name the coordinator and define the evidence each party supplies during an incident. Clear responsibility is more useful than a broad expectation that a private AI environment is automatically self-contained.

07 / DecisionChoose the facility after defining the operating model

Digital Realty is a useful candidate when an enterprise needs a connected physical home for sustained AI infrastructure. Begin with data placement, expected load and ownership of the computing stack. Then select a location and commercial scope that can deliver the complete requirement, including operation and future growth.

For the engineering document assistant, the desired result is controlled access to proprietary knowledge with an understandable recovery path. Colocation can support that goal, but the evidence system, model behavior and permissions remain application responsibilities. Keeping those boundaries explicit makes the infrastructure decision more durable.