5 Steps to Solving Latency in 2026

IT leaders have been contending with latency for decades. While the basic problem hasn’t changed, solving it is more important than ever. That’s because applications have steadily grown more sensitive to latency. With the advent of agentic AI, this trend will accelerate.

In the past, enterprises only had to support human users, and humans aren’t as sensitive to latency as machines are. Over time, enterprises began working with machine-to-machine architectures and adopted hybrid multicloud environments where latency was a performance bottleneck. Now, AI agents are connecting with each other and performing tasks at machine speed. Even very small delays could add up, particularly as the number of interconnected agentic systems continues to increase. Today’s enterprises won’t realize a return on their AI investments unless they can consistently lower latency across their operations.

In many ways, the rise of agentic AI is reshaping network behavior. Instead of humans driving most application requests, models are constantly talking to other models and platforms. In the future, this will generate up to 100X more inference calls than early GenAI models did.[1] These interactions must happen in real time, across precisely engineered, reliable paths to ensure uninterrupted data flow between agents.

Even a simple task can trigger dozens of tool-to-agent calls. Now imagine this at enterprise scale: thousands of employees triggering AI agents that in turn communicate with countless other services. This exponential mesh of machine‑to‑machine traffic will transform the network into a critical performance backbone where throughput, latency and deterministic routing are no longer nice‑to‑haves, but essential foundations for AI‑driven operations.

Enterprises have traditionally addressed latency with a variety of optimization tactics, such as using infrastructure monitoring software, performing manual troubleshooting, utilizing WAN optimizer appliances and caching data. These tactics helped a bit, but they didn’t fully address the underlying cause of latency: physical distance.

To solve for distance, deploy distributed infrastructure at the edge

There’s an upper limit to how quickly data can travel, and that limit is governed by the laws of physics. Even if you could hypothetically send your data at the speed of light, it could still incur significant latency if it travels long enough. Therefore, distributing infrastructure at the digital edge to keep data localized is the only true way to limit the impact of latency.

Using an edge-first infrastructure strategy to cut down on distance is a simple concept that can be difficult to execute. To help cut through the complexity, let’s take a step-by-step approach to solving the problem of latency.

Step 1: Identify your users

Before you can solve latency, you must think carefully about who the users are for your different applications and how latency might impact them differently.

Again, latency is a more pressing issue for AI agents than it is for humans. It’s also a more complex issue to solve, as agents have different requirements when it comes to latency. The concept may be the same—deploying distributed infrastructure at the edge—but the endpoints you’ll need to connect and the tools you’ll use to do so will vary. Think of it as the same recipe, using different ingredients.

Across industry verticals, there are both human-speed and machine-speed use cases. For example:

• In financial services, there’s high-frequency trading, where agents need to execute trades within microseconds to capitalize on market inefficiencies. To do this, they need access to the latest financial data with as little delay as possible.
• There are also human-facing financial applications such as providing customized financial planning recommendations. These applications are less sensitive to latency, because human users can’t detect very short delays.

Step 2: Establish distributed interconnection hubs

An interconnection hub is where digital ecosystems connect. Deploying interconnection hubs in different strategic locations helps you get closer to all the places digital business happens. This means you’ll be closer to end users, data sources, and most importantly, ecosystem partners.

Connecting with ecosystem partners—whether they’re established hyperscalers or emerging neoclouds—is quicker and easier when you’re literally colocated with those partners. You can exchange traffic without your data even having to leave the building.

Vendor-neutral colocation facilities, such as Equinix IBX® data centers, make ideal interconnection hubs. That’s because they’re already home to dense ecosystems of enterprises and service providers, which makes it easy to find the right partners in the right places.

Step 3: Choose the right network infrastructure and tools

Distance may be the true driver of latency, but the wrong networking technology can exacerbate the effects of distance.

Many enterprises have traditionally relied on the public internet because they saw it as a quick and convenient way to access global connectivity. However, the internet can lead to poor performance because traffic won’t always follow the most direct route. As shown below, traffic bounces between public internet exchange points, adding unnecessary distance to the route.

In contrast, private interconnection ensures a direct route between two points, so data never travels further than it has to. Interconnection also allows enterprises to exercise greater control over data traffic, which can help meet their data privacy and sovereignty requirements. Using a software-defined interconnection solution like Equinix Fabric®, enterprises can access scalable network capacity to keep up with growing AI workloads.

While enterprises are shifting from internet-first to interconnection-led, this doesn’t mean they’ll forgo the internet altogether. The internet is still helpful for things like collecting data from IoT sensors. It’s important that enterprises segment their different public and private traffic flows, and offload their internet traffic for private peering inside strategically located internet exchanges.

Step 4: Interconnect your distributed AI infrastructure

Among the biggest AI challenges enterprises will face is the need to support different workloads with different latency requirements. In particular, inference workloads are highly sensitive to latency. For accurate, timely inference, enterprises need scalable infrastructure at the edge to ensure data is processed close to the source.

In contrast, AI training workloads are less sensitive to network latency, and can thus be positioned in centrally located data centers that offer more processing power and efficiency improvements.

However, compute latency can also be a problem for training workloads. When GPU clusters perform parallel processing, the individual units need to synchronize results to complete the job. If they can’t synchronize quickly, it will cause delays, and companies won’t be able to capitalize on the GPUs they’ve invested millions in.

As enterprises accelerate adoption of GenAI, agentic AI and large‑scale analytics, networks are undergoing a profound architectural shift. AI traffic is growing so quickly—and becoming so distributed—that it’s projected to account for 30% of total WAN traffic globally by 2034, with enterprise and industrial AI traffic leading the way at 48% CAGR.[2] This will create more symmetrical, high‑bandwidth flows across cloud, metro and edge environments.

These workloads also introduce strict performance expectations: real‑time inference, high‑frequency data synchronization, and RDMA‑based transport all demand ultra‑low latency, guaranteed transactions and premium SLA‑backed connectivity. As a result, traditional WAN designs—optimized for asymmetric download‑heavy traffic—are giving way to AI‑optimized interconnections featuring 400/800G DCI links, intelligent traffic steering, LLM‑aware routing and automated operations that can keep pace with dynamically shifting AI clusters.

In short, the rise of distributed AI is transforming the network from a transport layer into a performance‑critical fabric that must be as fast, flexible and scalable as the AI models it serves.

Success with AI depends on addressing both network latency and compute latency across the organization’s distributed digital infrastructure. This means investing in the right interconnection solutions, and placing GPUs in an environment that’s optimized to help them perform at their best. Deploying at Equinix can help with both of these points.

Step 5: Connect with ecosystem partners

Depending on whether you’re supporting human users, machine-to-machine communications or AI agents, you’ll need to connect with different ecosystem partners:

• For human users, you’ll need access to multiple hyperscale cloud providers and SaaS providers that offer productivity and collaboration tools.
• For M2M, you’ll need to stretch your infrastructure using public cloud IaaS.
• For AI agents, you’ll need access to neoclouds such as CoreWeave, Groq and Nebius.

The good news is that the basic concept is the same no matter who you’re trying to connect with. When you deploy inside an Equinix data center, you’ll have access to both established ecosystems and emerging AI ecosystems, all in the same location. Thus, you’ll be empowered to connect with all those different partners quickly and securely.

Ecosystem access at Equinix

It’s important to note that the real fuel of AI—the data—is produced and stored in many different places, including IoT devices, clouds and on-premises data centers. That’s why multiagent environments need interconnection across different distributed systems. Organizations aren’t abandoning hyperscalers; they’re augmenting their architectures with multiple providers to support different workloads. Model training may occur on one platform, inference pipelines on another, and production deployment on yet another.

Learn how real Equinix customers are deploying at the edge to keep latency low, interconnect seamlessly with partners and providers, and unlock the power of AI: Read the e-book How Equinix customers turn data into value.

[1] Alex Woodie, Nvidia Preps for 100x Surge in Inference Workloads, Thanks to Reasoning AI Agents, HPCwire, March 19, 2025.

[2] Global network traffic report, Nokia, 2025.