The Missing Layer in AI Infrastructure: Why AI Networking Must Be Full-Stack, Open, and AI-Operated

 

AI infrastructure is growing at an unprecedented pace. Enterprises are racing to build clusters of GPUs, scale up AI workloads, and modernize their data pipelines. Yet one critical layer is often overlooked in these initiatives: the network.

While AI and data leaders focus on compute, storage, and models, the network quietly becomes the bottleneck. Traditional, static networks — built for legacy application traffic — can’t handle the dynamic, latency-sensitive, high-throughput demands of distributed AI workloads. And without visibility, orchestration, and automation across the full stack, enterprise IT leaders are flying blind in one of the most critical infrastructure domains of the decade.

At ONUG, where the community has long championed open, cloud-scale networking, this challenge is both familiar and urgent. It’s time to reframe the conversation: AI networking isn’t a peripheral concern — it’s the missing layer in AI infrastructure. And the solution is not just faster switches. It’s a full-stack, open, and AI-operated networking layer designed for the AI era.

The Blind Spot in AI Buildouts

Enterprise AI infrastructure is hitting production, but many organizations are discovering that their network isn’t ready.

Why? Because today’s networks were never designed for high-volume traffic, dynamic scaling of AI, or the precision tuning required for GPU interconnects. Most enterprise networks lack native support for lossless transport, multi-tenancy, or real-time visibility — all of which are essential when running distributed AI workloads where every millisecond counts.

Meanwhile, proprietary stacks slow down innovation. Observability is fragmented. Upgrades are risky. Operators are juggling CLI scripts, YAML files, and ticketing systems just to troubleshoot basic issues.

This isn’t sustainable. And it’s not how AI infrastructure should operate.

A New Layer: Full-Stack AI Networking

To solve this, enterprises need to rethink how networks are built and managed — starting with a full-stack approach.

We break this down into two key areas:

• Networks for AI: The physical and virtual infrastructure optimized to connect GPUs with high-throughput, low-latency, lossless configurations.
• AI for Networks: Intelligent automation powered by AI that simplifies Day 0–2 operations, from deployment to troubleshooting to compliance.

This full-stack model includes:

• Open Network Operating Systems (NOS) like SONiC and Cumulus that decouple software from hardware
• Multi-vendor orchestration layers that unify fabrics across OEMs
• Observability and telemetry frameworks — offering deep packet inspection, metadata extraction, and visibility across 4G/5G/AI fabrics
• LLM-based copilots that assist with upgrades, audits, performance tuning, and real-time issue resolution

Whether you’re deploying a reference architecture like NVIDIA Spectrum-X or an open fabric with SONiC, this approach ensures you’re not building AI infrastructure on a 20th-century network foundation.

Why Open Matters More Than Ever

Vendor-neutrality isn’t just a cost issue — it’s a control issue. The more proprietary your stack, the slower you move.

Open platforms like SONiC enable IT teams to:

• Tune the network stack based on their specific AI workloads
• Replace and upgrade hardware without rewriting the orchestration playbook
• Integrate seamlessly with observability tools, automation platforms, and security frameworks

We recently hosted a PlugFest that brought together leading switch vendors, solution providers, and enterprise users to test and validate SONiC-based fabrics. The takeaway? Open networking is no longer an experiment — it’s ready for enterprise AI at scale, and it’s being certified and hardened by the community.

From Complexity to Clarity: AI-Powered Operations

Operating AI infrastructure shouldn’t require navigating dozens of tools or relying on tribal knowledge. Networks must evolve to support simplified, AI-powered operations.

That means:

• Unifying management across the operations
• Leveraging real-time telemetry for proactive troubleshooting
• Automating repetitive tasks like compliance checks, and performance audits
• Using copilots to generate insights, summaries, and reports that accelerate time to resolution

This is the future of AI networking — simplified, scalable, and guided by data.

Build the Right Layer

The network is where performance, cost, and reliability intersect — and where you can gain or lose the most.

The time is now to invest in AI networking as a full-stack discipline — not a siloed afterthought. By embracing open, AI-powered, and multi-vendor infrastructure, IT leaders can finally align the network with the speed of innovation in AI.

FAQ’s

1. Why is AI networking considered the missing layer in enterprise AI infrastructure?
While compute and storage dominate AI infrastructure discussions, the network often becomes the bottleneck. Traditional networks lack the flexibility, observability, and low-latency capabilities needed to support modern, distributed AI workloads at scale.

2. What does a full-stack AI networking architecture include?
A full-stack AI network includes:

• Open NOS like SONiC or Cumulus
• Multi-vendor orchestration layers
• Deep observability with telemetry and metadata inspection
• LLM-powered copilots for upgrades, audits, and troubleshooting
  This enables seamless, intelligent, and lossless AI data pipeline operations.

3. How does open networking like SONiC benefit AI infrastructure?
Open networking decouples software from hardware, giving IT teams vendor freedom, better scalability, and faster upgrades — crucial for adapting networks to rapidly evolving AI workloads.

4. What is the role of AI in managing AI networks?
AI is used to power intelligent automation — handling deployment, upgrades, compliance, performance tuning, and real-time troubleshooting, reducing reliance on manual intervention and scripts.

5. How can enterprises future-proof their AI infrastructure with the right networking stack?
By adopting a full-stack, open, and AI-operated network layer, enterprises can reduce costs, boost performance, and scale AI workloads with confidence — ensuring the network is no longer a limiting factor.