Equinix has announced Equinix Inference Exchange, a new distributed AI inference platform built with NVIDIA and Together AI, designed to help enterprises move AI projects from testing to production faster. The service combines NVIDIA's validated computing architectures, Together AI's open-model inference platform supporting more than 200 open-source models, and Equinix's global network of over 280 data centers across 77 metropolitan areas. The solution will be delivered through Equinix's data centers and connected via Equinix Fabric to clouds, networks, and AI providers, with availability targeted for the first quarter of 2027.

Why Does It Matter Where AI Inference Runs?

As enterprises scale AI beyond experimental pilots into production systems, a critical question has emerged: where should AI inference actually run? The traditional approach of centralizing everything in one location no longer works for companies managing multiple clouds, data sources, and geographic regions. Inference Exchange addresses this by allowing companies to run AI workloads closer to their users, applications, and data, reducing latency and improving performance while maintaining security and compliance.

The shift reflects a fundamental change in how enterprises think about AI infrastructure. Rather than simply renting computing power from a single provider, companies now need to orchestrate AI workloads across distributed environments while controlling costs, governance, and model choice. This complexity has become a bottleneck for organizations trying to move from experimentation to operational AI systems.

"AI is transforming enterprise technology at extraordinary speed, and the infrastructure decisions enterprises make today will define their competitive position for years to come. Equinix is uniquely positioned to deliver what this moment demands based on our nearly three decades building the trusted exchange where the world's enterprises run, connect and orchestrate their most critical workloads," said Adaire Fox-Martin, Chief Executive Officer and President of Equinix.

Adaire Fox-Martin, Chief Executive Officer and President, Equinix

What Makes This Partnership Unique?

The collaboration brings together three complementary strengths. NVIDIA contributes its Enterprise Reference Architectures, which are validated blueprints for building AI systems. Together AI provides access to more than 200 open-source models, giving enterprises flexibility to choose models based on performance and cost rather than being locked into proprietary options. Equinix contributes its massive infrastructure footprint and interconnection network, which already hosts eight of the top 10 AI model providers and nine of the top 10 AI clouds.

This ecosystem density matters significantly. Enterprises can connect inference workloads to data, applications, and partners without building new infrastructure from scratch. The platform is designed for three primary use cases:

• Metro Edge Inference: Running AI workloads closer to users for lower-latency responses and faster decision-making.
• Open-Model Migration: Helping enterprises shift from proprietary, closed AI systems to open-source alternatives to reduce costs and increase operational flexibility.
• Sovereign AI Deployments: Supporting companies and regions with strict data residency requirements and regulatory compliance needs.

"Together AI was built on the conviction that open, accessible AI is what will define the industry moving forward, because enterprises shouldn't have to choose between model performance and operational flexibility," said Vipul Ved Prakash, co-founder and CEO of Together AI.

Vipul Ved Prakash, Co-Founder and Chief Executive Officer, Together AI

How Will Enterprises Deploy Distributed AI Inference?

Equinix Inference Exchange simplifies distributed AI deployment by handling the operational complexity that typically slows enterprises down. The platform offers both shared and dedicated deployment options, allowing customers to choose between multitenant environments for cost efficiency or single-tenant environments when they need dedicated capacity. Here's how enterprises can approach distributed inference deployment:

• Define Your Inference Location Strategy: Determine whether workloads should run at the edge near users, in regional data centers, or in centralized locations based on latency, cost, and governance requirements.
• Select Appropriate Open-Source Models: Choose from Together AI's 200+ supported open-source models based on performance benchmarks and operational needs rather than defaulting to proprietary options.
• Establish Secure Connectivity: Use Equinix Fabric to connect inference workloads to data sources, applications, and cloud providers with low-latency, secure connections across distributed environments.
• Monitor Performance and Costs: Track inference performance, latency, and expenses across multiple locations to optimize placement and resource allocation over time.

The operational complexity of managing distributed inference has become a significant barrier for enterprises. Each new connection between clouds, model providers, and data sources typically requires separate networking projects and specialized staff. Equinix is addressing this challenge by automating connectivity management through its Fabric infrastructure.

What's the Broader Industry Context?

Industry analysts note that performance, cost, and governance have become strategic considerations as AI workloads spread across multiple providers, data sources, and environments. Organizations are increasingly focused on where inference runs and how quickly it can be deployed into production. Solutions that simplify inference deployment while preserving flexibility are becoming essential for achieving business outcomes.

"Performance, cost and governance have become strategic considerations as AI workloads grow more distributed across providers, data sources and environments. Organizations are increasingly focused on where inference runs and how quickly it can be deployed into production. Solutions that simplify inference deployment while preserving flexibility will become increasingly important to achieve business outcomes," noted Nick Patience, Vice President and Practice Lead for AI Platforms at The Futurum Group.

Nick Patience, Vice President and Practice Lead, AI Platforms, The Futurum Group

Equinix is also launching a companion service called Equinix Fabric One, a managed connectivity service built on open specifications developed by AWS and Google Cloud. This service automates the provisioning and management of connections across distributed AI and cloud environments, allowing enterprises to define what should be connected while Equinix handles routing, encryption, resiliency, and failover behind the scenes. Fabric One is expected to enter beta later in 2026, with general availability in 2027 and an initial launch in North America.

The announcements were made at Equinix Horizon, the company's inaugural customer and partner event, underscoring the significance of these infrastructure developments for the enterprise AI market. As AI adoption accelerates, the infrastructure decisions enterprises make today will shape their competitive positioning for years to come.