Optimize AI Inference: Real-Time NodeBalancers Metrics for AI Workloads

In traditional applications, traffic patterns are largely predictable; they are driven by human activity and gradual use changes. However, in the era of the agentic web, AI agents perform autonomous actions, meaning that request and traffic patterns differ.

• Requests are triggered programmatically (not by users)

• AI agents generate parallel API calls (fan-out behavior)

• Traffic spikes are sudden and nonlinear

• Latency directly impacts model performance and user experience

This makes the load balancer a critical control point. Without visibility into connection-level behavior:

• Traffic bursts go undetected until latency spikes

• Back-end saturation appears too late

• Scaling decisions become reactive instead of proactive

If a load balancer cannot distribute traffic efficiently during a massive burst of inference requests, the entire AI application suffers from unacceptable latency, degrading the user experience and potentially interrupting critical autonomous tasks. 

In this volatile environment, NodeBalancers metrics — specifically monitoring session spikes and back-end health — are critical for ensuring that latency-sensitive inference calls do not bottleneck.

A common architecture for AI inference systems includes:

• Global routing (DNS or edge) directing users to the nearest region

• Regional load balancing using Akamai NodeBalancers

• Back-end pools of GPU-enabled compute nodes that are handling inference

Each region operates independently for latency and fault isolation.

• Reduces inference latency by serving traffic locally

• Prevents cascading failures across regions

• Enables independent scaling of GPU clusters

At the regional level, NodeBalancers:

• Distributes incoming TCP/UDP traffic

• Routes traffic to healthy GPU nodes

• Removes failing nodes via health checks

With real-time metrics, NodeBalancers becomes more than a routing layer — it becomes a signal source for system health and scaling decisions.

Akamai Cloud Pulse provides Layer 4 (transport layer ) metrics that expose how traffic behaves before it impacts application performance:

• Traffic volume (ingress/egress)

• Burst detection (new sessions)

• Concurrency (active sessions)

• Serving capacity (active back ends)

This mapping helps teams reason about system behavior and make faster, more accurate scaling decisions.

• Ingress traffic rate

• Egress traffic rate

• TCP/UDP breakdown

These metrics show how much data is flowing through the system.

Why it matters for AI
Sudden increases often indicate fan-out behavior, where AI agents trigger multiple inference requests simultaneously.

Figure 1 shows how these metrics appear in real-time dashboards.

• New sessions

• New TCP sessions

• New UDP sessions

These metrics act as early warning signals.

Why it matters for AI
Spikes in new sessions often precede:

• Back-end overload

• Increased latency

• Connection drops

This makes them critical for proactive scaling.

• Total active sessions

• Active TCP sessions

• Active UDP sessions

These represent real-time connection load on your infrastructure.

Why it matters for AI
High concurrency indicates:

• Sustained inference demand

• Long-lived connections

• Potential pressure on back-end systems

• Total active back ends

• Active TCP back ends

• Active UDP back ends

These metrics show how many back-end nodes are healthy and serving traffic (Figure 2).

Why it matters for AI
A drop in active back ends is often the first sign of:

• GPU node failures

• Failed health checks

• Resource exhaustion

We know that every engineering team has its own way of monitoring infrastructure. That’s why we’ve made NodeBalancers metrics highly flexible with:

• Native dashboards: View your metrics instantly within the Akamai Cloud Pulse user interface by using customizable dashboards, date ranges, and grouping filters (Figure 3).

• OpenTelemetry integrations: Export metrics using OTel Collector and expose them in Prometheus-compatible formats, enabling integration with tools like Grafana.

• Programmatic access: Retrieve your metrics programmatically using the Akamai Cloud API (built on legacy Linode infrastructure) to automate your monitoring and alerting workflows.

Akamai NodeBalancers joins a growing list of services supported by Akamai Cloud Pulse metrics, which currently includes Managed Database Services and Akamai Object Storage. By bringing these resources into a centralized monitoring environment, we are making it easier than ever to monitor the holistic health of your Akamai Cloud environment.

Ready to gain deeper insights into your traffic routing? Check out the resources below to start exploring NodeBalancers metrics on Akamai Cloud Pulse:

• Monitor NodeBalancers

• Key concepts and terms — Akamai Cloud Pulse

• Export metrics to external tools

As AI workloads continue to evolve, real-time visibility at the load balancer layer will play an increasingly critical role in maintaining performance and reliability. Log in to your Cloud Manager today to explore NodeBalancers metrics in Akamai Cloud Pulse to better understand and optimize your traffic at scale.