Is Your Enterprise Network Ready for AI Workloads?

Legacy enterprise networks were not designed for AI. They were designed for traditional applications that tolerated delay, accepted occasional packet loss, and moved data north-south between users and servers. None of these assumptions apply to AI workloads.

The public internet is a best-effort medium. That means no guaranteed latency, no reserved bandwidth and no predictable path between two points. That’s an acceptable tradeoff if all you’re doing is serving web pages. It’s unacceptable when a dropped packet means a stalled inference job, a degraded model response, or a training run that has to restart from a checkpoint.

MPLS solved the reliability problem but introduced a different one: rigidity. These networks are based on fixed circuits, provisioning cycles that take weeks or even months, and change windows that can’t match the agility of modern AI infrastructure. GPU clusters spin up in hours. MPLS circuits don’t.

AI workloads need something that neither option provides: private, low-latency connectivity with deterministic performance, high-bandwidth east-west throughput, and the ability to provision and reconfigure in minutes, not months.

An enterprise’s connectivity choice is no longer just an infrastructure decision; it directly impacts business performance. Latency, reliability, and provisioning speed determine how fast AI models reach production, how consistently they perform, and what it costs to run them. CIOs who leave this decision below the line will own the consequences when AI initiatives underdeliver. And in most organizations, those consequences land at the executive level first.

Why AI workloads demand interconnection

The AI infrastructure conversation has largely moved on from GPUs. The harder problem is ensuring connectivity across a sprawling, distributed AI supply chain. This includes public clouds, neocloud providers, private infrastructure, and a growing roster of specialized AI service providers. Enterprise AI workloads need to reach all these partners, often simultaneously.

Agentic AI raises the stakes further. These workloads don’t run all in one place. They decompose tasks, call external models and tools, chain inference across providers, and route outputs back into downstream systems, and they do it all in real time. Every hop in that chain can introduce latency. Every boundary crossing is a potential point of failure. And as token consumption scales, the cost of an inefficient routing compounds with every new request.

For all these reasons, the network is no longer passive infrastructure. It’s become an active variable in model performance, inference costs, and reliability SLAs, particularly for agentic workloads. Private interconnection across clouds, neoclouds, and AI service providers makes deterministic, low-latency multiparty orchestration possible.

Enterprises building agentic AI on public internet or legacy MPLS are absorbing latency, higher costs and fragility. Private interconnection can help solve these issues. CIOs that recognize this fact now have the opportunity to drive better AI performance throughout their organization.

Why the internet isn’t good enough for AI workloads

To move over the internet, data bounces from one internet exchange point to another. It gets where it’s going eventually, but not via the most direct path. There’s also no way to prioritize certain packets over others. The internet is publicly accessible, so any enterprise traffic has to compete with cat videos and recipe blogs for bandwidth.

Example of cloud-to-cloud traffic routed over the public internet

This means that not only is latency higher, but it’s also less predictable. Having delays in your AI applications is one thing, but having delays that you can’t prepare for is completely disqualifying.

Data sovereignty issues may also arise. Some enterprises need to ensure that certain AI datasets never cross national borders. When they use the internet, they can’t control the exact routing, which means they could unintentionally violate sovereignty regulations.

Interconnection between AI endpoints allows organizations to control the route that data takes. And since it’s a private connection, there’s no competition for bandwidth. As a result, the organization gets predictably low latency and the peace of mind that comes from knowing their sovereignty requirements are properly accounted for.

Interconnection as an ecosystem driver

Today’s AI workloads are distributed because AI itself requires a supply chain of hyperscalers, neoclouds, model providers, data partners, and security providers, all working in concert. The connectivity choices enterprises make determine which partners they can reach, how fast, and how easily they can swap one out when the market moves.

Ecosystem density, vendor-neutrality and interconnection are all inseparable:

• A neutral infrastructure foundation ensures that enterprises can freely choose the cloud providers and AI partners that best meet their needs, and then change their decision whenever the need arises.
• Ecosystem density ensures those partners are actually reachable—colocated and directly connectable, not three public internet hops away.
• Private interconnection provides the deterministic, low-latency path to move data between those partners without delay.

Enterprises need all three to drive business value.

In theory, the public internet can reach the same endpoints. In practice, it adds latency twice: The path isn’t optimized, and the distance that data travels is longer to begin with.

MPLS creates different issues: The network is stable, but the provisioning cycle isn’t. When a new model provider emerges and becomes the must-have integration, competitors on private interconnection can be live within minutes. Those on MPLS could be stuck waiting weeks for a circuit.

AI ecosystem partners cluster where it’s easiest to interconnect with each other and with their joint customers. Equinix data centers are purpose-built for this. They’re vendor-neutral, they span the globe, and they bring together more than 10,500 organizations already in our ecosystem. Thus, they make the ideal foundation for businesses to access the distributed partners that their AI strategies depend on, without being constrained to a particular provider’s environment.

In 2025, more than 60% of existing Equinix customers added new services by connecting to partners that were already present in the same facilities. That’s ecosystem density working as a competitive advantage, not just a feature.

Case study: Dow Jones Newswires

Dow Jones Newswires delivers real-time financial insights to customers, who use these alerts to inform their automated trades. Customers must execute trades before the opportunity disappears, so their tolerance for latency is practically non-existent. Even the most optimized internet connection possible wouldn’t be good enough.

The company is able to limit latency to mere microseconds, both because they’re using Equinix Fabric® for private virtual interconnection and because they’re physically colocated with customers in the same places the trades are happening: inside an Equinix colocation data center.

Read the full Dow Jones Newswires story.

How CIOs can get started with AI-ready interconnection

The infrastructure decisions that CIOs make in the next 12-18 months will be difficult to unwind. AI ecosystems compound: The partners that an enterprise connects to today become the foundation for the agentic workloads they deploy tomorrow. Switching costs are real, and so are the costs of being locked into an inflexible topology.

Getting started means optimizing for choice, not just performance. CIOs should prioritize connectivity platforms that let them add, change and remove ecosystem partners without rearchitecting. This flexibility will be essential, because the definition of the right AI supply chain will shift along with the market. The preferred neocloud from 2025 may not be the right answer in 2027.

AI is redefining the role of the network, and today’s leaders care more about outcomes than they do about the technical details. Enterprises must be able to quickly reconfigure their network infrastructure to match their intended outcomes, while preserving vendor-neutrality and ecosystem access. Equinix is dedicated to helping our customers do exactly that, and we’re thrilled to share what’s coming next.

Learn how to get started: Access the Equinix Connectivity Advisor. Answer a few simple questions, and within minutes, you’ll get a customized architecture recommendation for your business.