As enterprises find ever more use cases for generative AI (genAI) and agentic AI, their ability to achieve optimal business outcomes from these use cases will depend on the strength of their hybrid multicloud networks.
Typically, these workloads demand higher-bandwidth, low-latency connectivity for centralized application delivery (LLM development), and AI at the edge (inferencing). If IT leaders fail to design a network that can handle the large data volumes and unique traffic patterns of AI workloads, they risk slowing down their AI initiatives.
The complexity of hybrid multicloud networks
Hybrid multicloud networks were never simple. Connecting and securing disparate environments often relies on multiple solutions, some of which are costly while others lack interoperability and observability. Network teams for years have been fighting the battle of trying to reduce connectivity costs without sacrificing resiliency, security, and performance.
Enter AI, which exacerbates existing hybrid multicloud networking challenges, including:
1. Increased data gravity
GenAI, for example, thrives on vast quantities of data. AI applications not only need access to centralized datasets but also generate substantial new data for continual refinement. The result is a web of interdependencies where data locality and proximity to GPU resources matter as much as, if not more than, raw capacity. Moving data between clouds and on-premises systems to support AI processing adds another layer of logistical complexity.
2. Latency sensitivity
GenAI workloads, such as real-time inference or retrieval-augmented generation (RAG) systems, are latency sensitive. Whether generating text-based customer support responses in real time or delivering information about an account to a salesperson during a meeting, these workloads leave no room for delays. Edge infrastructure can offer some relief by reducing latency for AI inference, but integrating edge-based AI deployments into an already-fragmented hybrid infrastructure is no small feat.
3. Skyrocketing costs
Running AI workloads involves scaling GPU and network resources extensively, especially when training or fine-tuning models across multicloud environments. Higher bandwidth requirements, frequent data transfers, and inadequate routing mechanisms can drive up network costs. At the same time, overprovisioning bandwidth results in enterprises paying for services they don’t use.
4. Security and compliance challenges
GenAI intensifies concerns around data privacy and compliance. Sensitive proprietary data must often be used to train and refine models, and this data typically straddles public and private cloud environments. This dynamic raises questions about how to securely handle and process data while meeting regional regulatory requirements. These risks are amplified if traditional network management tools lack robust security layers.
Future-proofing multicloud networks for genAI
It’s highly recommended to re-architect hybrid multicloud network to prepare for the growing demands of genAI. IT leaders should consider deploying software-defined networking platforms to centralize management and enable seamless orchestration. Organizations should also deploy edge infrastructure to deliver latency-sensitive AI applications, placing inference and limited training workloads near users.
A hybrid approach, combining private data repositories for sensitive training with multicloud flexibility for non-proprietary workloads, offers a practical path forward. Partnering with cloud-neutral platforms, such as Equinix, can help enterprises overcome connectivity challenges while maintaining security, scalability, and AI readiness.
GenAI has the potential to revolutionize industries by improving customer experiences, boosting employee productivity, and streamlining and automating business processes, but only if enterprises are ready to address the new demands AI puts on the network . The time to start is now.
Learn about building a hybrid multicloud networking strategy that sets you up to thrive in this new world.
