Hyper-Distributed AI demands an ‘Offramp to edge’ – here’s why

Supporting the “Offramp to edge”

With the rise of AI workloads, compute and data must exist near or at the edge to support the demand. Enterprises are increasingly choosing to keep interactions close to users and data, run the right inference near the source, and escalate only when they need depth or scale. The new operating model for the network must then keep pace, supporting onramps and offramps from anywhere to anywhere, working as a single, policy-driven fabric.

Why “Offramp to edge” matters now

The shift towards “Offramp to Edge” is critical now due to several converging factors centered on performance, compliance, and operational reliability.

• Latency and Experience – For modern applications like real-time assistants, computer vision, and complex control loops, performance is dictated by latency. These systems are hypersensitive and fundamentally require secure connectivity to inference that is located physically near the event or user. This proximity is essential to deliver the instant responses required for a satisfactory real-time experience.
• Data Locality and Sovereignty – In an increasingly regulated landscape, data locality and sovereignty are paramount. Specific features, vectors, and operational data generated in a region must remain within that region to comply with regulations. The network architecture needs to be designed to honor that requirement by default, ensuring that sensitive data is processed and stored locally at the edge.
• Resilience and Autonomy – Operational reliability demands that edge sites and partner domains maintain full functionality even when the main backbone network experiences outages or “hiccups.” This need for resilience and autonomy means that edge infrastructure must be capable of independent operation and then be able to synchronize intelligently with the central cloud once connectivity is restored.

The overarching strategy needs to treat the cloud as depth and scale, utilizing its massive resources for less time-sensitive, heavy-duty tasks, while simultaneously treating the edge as proximity and responsiveness, leveraging its nearness for immediate, low-latency actions. The core technical challenge and solution lie in stitching these two domains together with deterministic networking to ensure a seamless and predictable flow of data and services.

Traditional networks can’t keep up

While AI infrastructure is exploding inside the enterprise technology stack, the network remains relatively averse to generative AI adoption in NetOps. This makes it difficult to support a hyper-distributed system from any network.

According to Gartner, less than 1% of enterprises have adopted Agentic NetOps, a concerning statistic given that over 50% of computing is expected to transition to the edge by 2029. This lack of foresight leads to several issues:

• Lack of Agility: Building a resilient, redundant, and elastic network fabric for an AI-centric world is impossible without adapting to rapid changes. Relying on physical appliances or routing traffic through bottlenecks creates friction and delays.
• Not Future-Proof: Enterprise networks must keep pace with the growing number of AI agents and workloads across various environments, from the edge to the data center to the cloud. Without a scalable architecture, companies will face frequent and costly updates.
• High Operational Complexity: With network outages potentially costing up to $500,000 per hour, AI’s demands will only intensify these stakes. Network operations teams require a new approach to meet these demands without incurring increased operational expenses.
• Security Confidence Gap: The combination of users, models, data stores, and tools moving through a multi-cloud environment creates new security challenges. Most enterprises lack the maturity to effectively counter AI-enabled threats and establish zero-trust policies, leaving their AI pipelines vulnerable.

To break this bottleneck, enterprises need an AI-native, policy-driven fabric that connects clouds, data centers, partners, and the edge without hardware or software rollouts. NetOps must shift from device configurations to outcome-based intent, with zero-trust built in and elastic capacity on demand. The result is secure and predictable delivery that makes multi-tenant AI operations routine, giving enterprise AI teams the hyper-agility to place and protect models and data wherever they run.

The AI era doesn’t replace the cloud – it adds the edge. The right strategy is not to choose, but to bind onramp and offramp into a single, deterministic, zero-trust fabric. It requires a fundamental rethinking of network strategy that emphasizes locality, predictability, and a future-proof architecture tailored to the demands of the AI era. When you have a network that supports a hyper-distributed environment, making compute and data clusters feel local everywhere, and your teams can act fast with confidence and develop enterprise AI without friction.

About the author

Sukruth Srikantha is VP of Solutions Architecture at Alkira. Alkira is the leader in AI-Native Network Infrastructure-as-a-Service. We unify any environments, sites, and users via an enterprise network built entirely in the cloud. The network is managed using the same controls, policies, and security systems network administrators know, is available as a service, is augmented by AI, and can instantly scale as needed. There is no new hardware to deploy, software to download, or architecture to learn. Alkira’s solution is trusted by Fortune 100 enterprises, leading system integrators, and global managed service providers. 

Article Topics

AI networking  |  AI/ML  |  Alkira  |  edge AI  |  edge computing  |  edge networking  |  zero trust networking