Building a Future-Ready AI Network: What to Know Before You Buy

Enterprise interest in AI and large-scale AI/ML workloads has never been higher. Whether you’re training models, running inference or building AI-powered services, GPUs and compute clusters are essential. But one often underestimated area can quickly become a bottleneck: the network.

A sub-optimal network can cut GPU efficiency by up to 50% during compute-exchange-update phases, especially when running latency-sensitive workloads such as RoCEv2.

That’s why using a robust decision framework like the AI Network Decision Framework from IP Infusion, matters for enterprises and service providers charting an AI-powered future.

Below, we unpack the framework’s core axes and map them to practical choices for ANZ organisations, showing how you can build an AI network that balances speed, ROI and strategic freedom.

Core Trade-offs: What Every AI Network Decision Must Balance

IP Infusion outlines four key trade-offs that shape any high-performance AI or data-centre network:

• Strategic Control vs. Simplicity — Would you rather own the full stack and remain flexible or choose a single-vendor solution that’s plug-and-play?
• Performance vs. Cost — High-speed fabrics and lossless transport are critical for AI, but they come at a price. How do you get performance without excessive spend?
• Innovation vs. Stability — Do you adopt the newest, cutting-edge components, or stick with a validated, proven ecosystem?
• Openness vs. Vendor Validation — Open, interoperable architecture gives freedom but may carry validation overhead. Proprietary, vendor-locked stacks offer ease and support but at the cost of lock-in.

These dimensions form four broad “archetypes” of AI networks: Proprietary/vendor-locked; Commercial open (e.g. OcNOS on whitebox hardware); Open-source or DIY; and open with commercial support.

Which Archetype Fits Your Organisation?


Here’s a quick breakdown of each archetype and where they tend to fit:

1. Proprietary (Vendor-Locked)

• What it offers: Turnkey reliability, vendor support, pre-validated infrastructure — minimal integration effort.
• Why an enterprise might choose this: If stability, minimal operational overhead, and vendor support matter more than cost or flexibility.
• Trade-offs: Higher total cost, vendor lock-in, limited flexibility to evolve.

2. Commercial Open (e.g. OcNOS + whitebox + open optics)

• What it offers: Cost-efficient, highly flexible and open networking, backed by enterprise-grade support and licensing.
• Why this works for ANZ businesses Especially good for organisations seeking performance without lock-in or for service providers deploying large-scale fabrics on a budget.
• Trade-offs: Slightly more operational responsibility than proprietary stacks, but significantly lower TCO and more control.

3. Open-Source / DIY (SONiC / custom NOS)

• What it offers: Maximum control, customisability and freedom – ideal for organisations with strong in-house engineering resources.
• Why consider: If your organisation behaves more like a hyperscaler, needing absolute control and custom network logic.
• Trade-offs: Highly complex operations, requires deep expertise, longer deployment times, higher risk.

Why OcNOS (or similar disaggregated/open solutions) Often Win for AI/ML


For many enterprises, especially outside hyperscalers, the Commercial Open archetype (e.g. OcNOS) provides a powerful balance of performance, cost and flexibility. According to IP Infusion, organisations using commercial open NOS solutions can achieve 40–60% total cost of ownership (TCO) savings compared to proprietary stacks.

Key benefits:

• Lossless, AI-optimised fabrics with support for technologies such as IPoDWDM and 400G ZR/ZR+, supporting high-throughput, low-latency data centre interconnect (DCI) or AI fabric traffic.
• Perpetual licensing — no recurring software “feature” fees, providing long-term cost predictability.
• Vendor-backed support and partner network — giving you enterprise-style reliability even on whitebox hardware.
• Strategic freedom — ability to design, evolve, and customise network fabric without vendor-lock-in constraints. Great for future-proofing AI infrastructure and evolving workloads.

Build Your AI Network Using the Framework

If you’re considering building or upgrading your network to support AI/ML workloads, here’s a practical decision path using the AI Network Decision Framework:

1. Define your priorities up front

• Is speed and latency (GPU ROI) critical? Or is total cost and TCO a higher priority?
• Do you require turnkey simplicity or do you need flexibility and control for future innovation?

2. Use the decision tree

• If you prioritise turnkey reliability, go for a proprietary stack.
• If you value flexibility, cost-efficiency, and vendor independence – consider commercial open (e.g. OcNOS).
• If you have experienced engineering resources and want full control, open-source / DIY may be realistic.
• If you want balance, open ecosystem with support, consider a supported open distribution.

3. Benchmark metrics that matter

• Test Job Completion Time (JCT) under typical AI workloads – evaluate throughput, latency, lossless transport.
• Compare total cost of ownership over 3-5 years (hardware + software + support).
• Consider flexibility costs – how easy is it to scale, reconfigure or migrate if your needs change?

4. Pilot before you commit

• Use pilot clusters – IP Infusion offers virtual demo and PoC capabilities (e.g. OcNOS VM) to test traffic patterns, latency, and ROI before large-scale deployment.

5. Design for growth

• Build a network fabric that can evolve – use open optics, modular architecture, and standard protocols.
• Avoid lock-in components unless you truly need the vendor ecosystem benefits.

What This Means for ANZ Organisations

For organisations in Australia and New Zealand, whether enterprise, data-centre operator or service provider, the AI demand curve is real. But so are constraint factors such as cost, vendor lock-in, legacy infrastructure, geography, skills availability.

Adopting a commercial open networking approach (e.g. OcNOS with whitebox hardware + open optics) gives ANZ organisations:

• Lower costs compared with proprietary stacks – helpful in budget-conscious projects.
• Flexibility to scale capacity and performance as AI needs grow.
• Support from global and local partner networks for deployment and lifecycle support.
• A future-ready fabric that aligns with global best-practice for AI data centres.

Working with a trusted regional partner, one experienced in open networking, with knowledge of ANZ regulatory and operational requirements, makes the journey easier.

Deploying AI isn’t just about GPUs – the network is equally critical. Using the AI Network Decision Framework helps guide strategic choices, balancing performance, cost, flexibility and control.

For many organisations, the sweet spot lies in commercial open networking – combining the best of whitebox cost efficiency with enterprise-grade support and flexibility.

If you’d like help assessing your network readiness, running a pilot with OcNOS or designing a future-ready AI fabric for ANZ operations, IDS is ready to support you every step of the way.