Digital Realty PlatformDIGITAL
Digital Realty helps enterprises build AI-optimized infrastructure with high-density colocation, scalable power and cooling, and secure hybrid connectivity through PlatformDIGITAL.
About Digital Realty PlatformDIGITAL
Digital Realty’s AI solution is designed for organizations running compute-intensive AI and machine learning workloads. Built on PlatformDIGITAL, it brings together global data center capacity and interconnection services so teams can deploy AI infrastructure closer to data sources, cloud platforms, and users. The focus is on supporting high-performance environments while enabling hybrid architectures and consistent operations across regions.
Key Features
- High-density colocation: Data center capacity designed to support AI/HPC-style power and cooling requirements.
- Scalable infrastructure planning: Options to expand capacity as AI workloads and hardware footprints grow.
- Hybrid-ready connectivity: Interconnection capabilities to link private infrastructure with cloud and network providers.
- Low-latency architecture support: Connectivity choices that help reduce latency between compute, storage, and data sources.
- Global platform approach: A consistent deployment model across multiple metros and regions for distributed AI initiatives.
- Enterprise-grade facilities: Operational controls and physical infrastructure built for reliability and continuous uptime needs.
- Ecosystem support: Ability to align deployments with cloud, network, and technology partners as needed.
- Sustainability alignment: Emphasis on energy-aware operations and infrastructure efficiency for long-term scale.
Pricing
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Quote-based model: Pricing is typically custom and depends on deployment requirements.
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Common cost drivers: Space/rack or suite needs, power density, cooling design, connectivity services, and location.
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Project scope matters: Costs vary based on whether the environment is built for training, inference, or mixed workloads, and on scaling expectations.
Pricing last updated: February 10, 2026 at 11:11 AM
Use Cases
- AI model training & fine-tuning: Deploy high-density colocation capacity for GPU-heavy training clusters where power and cooling headroom matters.
- Inference at scale: Place inference infrastructure closer to users, apps, or data sources to reduce latency and improve response times.
- Hybrid AI architecture: Connect private AI infrastructure to public cloud services for burst capacity, data pipelines, or managed AI services.
- Data gravity & data locality needs: Keep large datasets close to compute while still enabling secure connectivity to clouds, partners, and teams.
- Multi-site / distributed AI operations: Standardize deployments across metros/regions for resilience, compliance, and capacity planning.
- High-performance analytics workloads: Support adjacent HPC, big data processing, and data-intensive pipelines that share similar infrastructure requirements.
Pros & Cons
Pros
- Built for high-density workloads: Designed to support power, advanced cooling, and performance needs common in AI/HPC environments.
- Carrier-neutral connectivity options: Flexible interconnection choices to reach clouds, networks, and partners.
- Global platform footprint: Useful for organizations that need consistent infrastructure across multiple regions.
- Enterprise-grade facilities: Strong fit for reliability-focused deployments where uptime and operational controls matter.
- Ecosystem approach: Works well when deployments require coordination across multiple providers and technology partners.
Cons
- Complex solution design: High-density AI deployments often require detailed engineering decisions (cooling, layout, network design).
- Not a self-serve SaaS: Procurement and rollout typically follow an enterprise sales and implementation process.
Integrations
- Public cloud connectivity: Private, high performance links to major cloud platforms (hybrid and multi cloud patterns).
- Network & carrier ecosystem: Connectivity through carrier neutral options for WAN, internet, and private networking.
- Partner ecosystems: Ability to align with hardware, AI infrastructure, and solution partners for end-to-end deployments.
- Interconnection fabric: ServiceFabric based connectivity to coordinate and manage links between infrastructure, clouds, and ecosystems.
FAQ
Last edited
February 10, 2026 at 11:11 AM by Venkatraman
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