AI and Load Balancing: Rethinking Network Infrastructure for the AI Era

Source: VMware Blog

Load Balancing

AI and Load Balancing: Rethinking Network Infrastructure for the AI Era

Umesh Mahajan – December 17, 2025

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How the AI Era Is Re‑Imagining Enterprise Load Balancing for App Delivery, Resilience, and Security

The AI revolution is pushing enterprises from hardware‑defined load balancing to software‑ and AI‑defined architectures that deliver:

• Enhanced application resiliency
• Intelligent autoscaling
• Self‑healing capabilities
• Predictive AI, GenAI, and LLM‑driven intelligence

Just as cloud computing gave rise to software‑defined (SD) load balancing, the AI wave is evolving SD into AI‑defined load balancing. This shift changes how organizations design infrastructure to support modern AI workloads and to extend AI benefits to existing applications.

Why AI Workloads Stress Traditional Load Balancers

“When you build modern AI applications for enterprises, there has to be a very high level of performance, latency, resilience, security and elasticity.”
— Chris Wolf, Global Head of AI and Advanced Services, VCF division, Broadcom

“Load balancers in the AI era must be able to manage services and fulfill enterprise requirements across multiple private AI environments.”

Core Requirements for AI‑Era Load Balancers

1. Massive Throughput & Low Latency

   • Support Tbps traffic rates.
   • Provide deterministic latency for real‑time inference.

2. Elastic Autoscaling & Self‑Healing

   • Auto‑scale horizontally based on demand spikes.
   • Detect and remediate unhealthy nodes without human intervention.

3. Infrastructure‑as‑Code (IaC) Compatibility

   • Declarative configuration (YAML, Terraform, Helm).
   • Seamless integration with CI/CD pipelines.

4. Built‑In Global Server Load Balancing (GSLB)

   • Distribute traffic across multi‑region AI clusters.
   • Optimize latency and failover globally.

5. Integrated Security Stack

   • Web‑Application Firewall (WAF) and API security baked in.
   • Real‑time anomaly detection, traffic‑pattern recognition, and rate‑thresholding.
   • End‑to‑end encryption and data‑loss‑prevention for sensitive AI data.

6. Kubernetes‑Native Operation

   • Native support for K8s service meshes (e.g., Istio, Linkerd).
   • Ability to expose AI micro‑services via Ingress/Egress controllers.

Security Considerations for AI‑Driven APIs

• API‑Centric Threat Landscape – AI apps exchange massive volumes of sensitive data via APIs, making them prime targets for injection, credential‑stuffing, and data‑exfiltration attacks.
• Comprehensive Protection – Deploy a unified WAF/API security platform that can:
  1. Inspect request/response payloads for malicious patterns.
  2. Enforce rate‑limiting and quota controls.
  3. Apply behavioral analytics to flag anomalous traffic.
• Dynamic Thresholding – Use AI‑powered anomaly detection to automatically adjust security policies based on real‑time traffic baselines, ensuring optimal resource allocation without sacrificing protection.

Takeaway

To thrive in the AI era, enterprises must adopt AI‑defined load balancing that couples ultra‑high throughput, elastic autoscaling, self‑healing, and AI‑enhanced security. By embedding these capabilities directly into the load‑balancing layer—especially within Kubernetes‑driven micro‑service environments—organizations can deliver resilient, secure, and performant AI applications at scale.

AI‑Defined Load Balancing

Load balancing in the AI era should itself be AI‑driven, and it does so across three key dimensions.

1. Predictive Intelligence – Resiliency & Real‑Time Scaling

• Health‑score monitoring with dynamic thresholds that adapt in real time to traffic bursts.
• Static thresholds are impractical; over‑provisioning for peak load is cost‑prohibitive.
• Active‑active HA guarantees continuous operation.
• Auto‑scaling + auto‑healing detect traffic patterns and remediate issues automatically, often without admin intervention.

2. Generative AI – Operational Efficiency

• AI co‑pilots let admins ask natural‑language questions and receive answers, analytics, and contextual insights drawn from:
  • Application health scores
  • Latency measurements
  • Design guides
  • Knowledge‑base documentation
• Provide correlated analytics, multi‑factor inference, and workflow‑specific insights.
• Infrastructure‑as‑Code enables programmatic configuration changes, reducing manual effort.
• AI‑assisted capacity management and performance troubleshooting flag emerging issues long before they impact users, boosting productivity.

3. AI‑Powered Self‑Service – Zero‑Training Interfaces

• Engineers receive intuitive, AI‑guided assistance for deployment and configuration.
• Result: faster roll‑outs without compromising quality or security.

A Real‑World Example

Broadcom’s VMware Avi Load Balancer meets all AI‑era requirements. Rigorous studies show that enterprises using this solution achieve:

While the core software‑defined load‑balancing principles—scale‑out performance, dynamic availability, and application‑level security—remain, AI amplifies them and embeds intelligence directly into the infrastructure.

Takeaway: Organizations that adopt AI‑defined load balancing will support both AI and non‑AI workloads more effectively and reap the benefits of built‑in infrastructure intelligence.

Learn more:
VMware Avi Load Balancer – Broadcom

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Author

Umesh Mahajan – General Manager, Application Networking and Security Division, Broadcom.
Senior business executive with entrepreneurial and general‑management expertise, overseeing Lateral Security and Avi Load Balancer products.