Launches Fabric Intelligence, Global AI Solutions Lab, and expanded ecosystem to accelerate enterprise adoption of agentic AI and edge-to-cloud workloads
At its inaugural AI Summit in Mumbai, Equinix, the world’s digital infrastructure company, unveiled its Distributed AI infrastructure — a global-first approach designed to support the scale, speed, and complexity of modern intelligent systems, including the emerging wave of agentic AI.
Unlike traditional applications, AI is inherently distributed, with distinct requirements for training, inferencing, and data sovereignty. Equinix’s new AI-ready backbone leverages its 270+ data centers across 77 markets to unify these environments, enabling enterprises to deploy AI workloads seamlessly from edge to cloud.
“This is the infrastructure AI has been waiting for. The real challenge is connecting it all—securely, efficiently and at scale.” — Jon Lin, Chief Business Officer, Equinix
Key announcements included Fabric Intelligence, a software layer for Equinix Fabric® that delivers real-time awareness and automation for AI and multicloud workloads. Available in early 2026, it integrates with orchestration tools to dynamically optimize performance and reduce manual effort.
Equinix also launched a Global AI Solutions Lab across 20 locations in 10 countries, giving enterprises a collaborative environment to de-risk AI adoption, co-innovate with partners, and accelerate deployment. The company further expanded its vendor-neutral AI ecosystem of 2,000+ partners, with private access to cutting-edge platforms like GroqCloud™ coming in 2026.
“Enterprises that fail to adopt a distributed AI strategy will find themselves at a competitive disadvantage.” — Dave McCarthy, IDC
Use cases range from predictive maintenance in manufacturing to fraud detection in financial services, underscoring how distributed AI can drive business resilience and agility.
“By bringing AI closer to users and data, Equinix is setting the stage for enterprises to innovate securely, globally and at scale,” said Jon Lin.