Digital Realty (DLR) Launches ServiceFabric MCP for AI-Native Infrastructure Control
Key takeaways
- Digital Realty (DLR) Launches Service Fabric MCP for AI-Native Infrastructure Control Maham Fatima Tue, June 30, 2026 at 8:36 PM GMT+7 2 min read DLR DDOG DLR-PJ DLR-PL Digital Realty Trust Inc.
- The new service acts as a programmable interface, enabling AI systems and agents to securely interact with infrastructure, applications, and enterprise services.
- Designed to be open and vendor-neutral, ServiceFabric MCP supports diverse AI models and hybrid environments, allowing enterprises to connect across public clouds and third-party facilities.
Digital Realty (DLR) Launches Service Fabric MCP for AI-Native Infrastructure Control Maham Fatima Tue, June 30, 2026 at 8:36 PM GMT+7 2 min read DLR DDOG DLR-PJ DLR-PL Digital Realty Trust Inc. (NYSE:DLR) is one of the AI stocks on Wall Street's radar. On June 17, Digital Realty launched Service Fabric Model Context Protocol/MCP, a new programmable layer designed to make physical data center infrastructure "AI-native." By using the company's AI Private Exchange/AIPx architecture, this protocol allows enterprise AI environments to manage power density, cooling, and data placement across more than 800 global data centers.
The new service acts as a programmable interface, enabling AI systems and agents to securely interact with infrastructure, applications, and enterprise services. It provides capabilities for intent-based provisioning, real-time network telemetry, and automated security controls. By integrating with tools like Slack and Datadog, it aims to streamline the deployment and management of AI workloads at scale.
Designed to be open and vendor-neutral, ServiceFabric MCP supports diverse AI models and hybrid environments, allowing enterprises to connect across public clouds and third-party facilities. Digital Realty is positioning this platform as a foundational element of its broader strategy to support the next wave of enterprise AI, focusing on the rigorous requirements of production workloads.