While everyone obsesses over GPU shortages and model training costs, a quieter crisis is unfolding in data centers worldwide. Organizations are discovering that their most expensive hardware—those coveted GPUs—sits idle not because of compute limitations, but because of networking bottlenecks that can take weeks to resolve.
The irony is stark: companies spend millions on bleeding-edge AI hardware, then watch it gather dust while network engineers manually configure hundreds of switches to provision a single new tenant. This isn’t just inefficiency—it’s a fundamental mismatch between the speed of AI innovation and the pace of traditional infrastructure.
The Hidden Cost of Network Rigidity
Physical segregation offers maximum security but creates a different problem entirely. When each tenant requires dedicated hardware, provisioning becomes a manual, time-intensive process. GPU utilization plummets while teams wait for network configurations that should take minutes, not weeks.
The alternative—sharing network infrastructure—brings its own challenges. A single misconfiguration can bring down critical training workloads for multiple tenants. With significant miswiring rates in large deployments, the risk isn’t theoretical.
Meanwhile, DevOps engineers accustomed to spinning up cloud resources with a few API calls find themselves submitting tickets and waiting for manual network provisioning. The disconnect between cloud-native expectations and on-premises realities creates friction that slows innovation.
Enter Netris: Cloud Networking for the Physical World
Netris tackles this challenge by bringing cloud-like networking constructs to on-premises infrastructure. Rather than treating networking as a separate, complex domain, the platform makes it consumable through familiar interfaces that DevOps teams already understand.
The architecture rests on three integrated pillars that work together rather than as separate point solutions:
- VPC-Style Abstraction: Instead of configuring individual switch ports, operators define tenants by logical servers. The platform automatically generates the underlying network configurations—VRFs, VXLANs, routing policies—reducing tenant onboarding from weeks to minutes.
- Softgate for Cloud Services: Traditional switches can’t provide elastic load balancers, NAT gateways, or elastic IPs. Softgate runs on standard Linux servers to fill this gap, delivering cloud networking services that scale horizontally while integrating seamlessly with the switch fabric. At 100 Gbps forwarding rates and 25 million packets per second, it can even replace dedicated hardware routers in many scenarios.
- Fabric Management: The centralized controller serves as a single source of truth, supporting Infrastructure as Code through Terraform and REST APIs. Automated discovery identifies switches, validates cabling, and flags the miswiring issues that plague large deployments.
Real-World Impact
The practical benefits extend beyond faster provisioning. Multi-tenancy becomes truly dynamic—VXLANs extend to individual hosts and DPUs, enabling isolation at the per-GPU or per-container level. This granular control means different AI workloads can safely share expensive hardware without compromising security or performance.
For DevOps teams, the experience mirrors public cloud simplicity. Need a load balancer for your inference workload? Provision it through Kubernetes constructs or Terraform. No tickets, no waiting, no diving into switch configurations.
Network engineers retain control through guardrails—they define which IP subnets and switch ports are available for self-service consumption, ensuring operational integrity while enabling developer velocity.
The Broader Implications
Netris represents more than just network automation—it’s addressing a fundamental architectural mismatch. As AI workloads become more diverse and dynamic, the networking layer needs to match that agility. Static, manually configured networks create artificial bottlenecks in what should be fluid, software-defined environments.
The platform’s hardware-agnostic approach—supporting Nvidia, Arista, Dell, and Edgecore switches—also addresses vendor lock-in concerns that often complicate large-scale deployments. Integration with Nvidia UFM extends this unified management to Infiniband fabrics, critical for high-performance AI training.
Implementation Reality Check
No solution is without tradeoffs. Netris uses proprietary telemetry collection methods, which may concern organizations committed to fully open-source monitoring stacks. The platform doesn’t integrate out-of-the-box with external IPAM solutions like Infoblox, requiring custom development for some use cases.
Switch agents currently need manual installation, though zero-touch provisioning is planned. The core agent code remains closed source, limiting direct customer extensibility. For multi-data center deployments, controller redundancy and disaster recovery require additional architectural planning.
The Path Forward
The networking challenges in AI infrastructure won’t solve themselves. As workloads become more complex and hardware more expensive, the cost of manual provisioning and rigid architectures will only increase. Organizations that address these bottlenecks now will have significant advantages in deployment speed and resource utilization.
Netris offers a compelling path forward, particularly for organizations with substantial multi-tenant requirements and cloud-native DevOps teams. The platform’s unified approach eliminates the integration complexity of point solutions while delivering the self-service experience that modern development teams expect.
The question isn’t whether networking needs to evolve for AI infrastructure—it’s whether organizations will proactively address these bottlenecks or continue watching expensive hardware sit idle while engineers wrestle with manual configurations. For many, the answer may determine their competitive position in the AI race ahead.
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