Simplify Edge Operations with Coordinated Service and Network Orchestration.
The challenge
Deploying an edge service is only half the job.
Applications depend on compute and connectivity that must be provisioned, segmented and monitored. Coordinating these layers across distributed sites—and keeping tenants isolated—adds operational complexity.
Separate workflows
Applications can be ready before the network services they need are provisioned.
Customer isolation
Shared infrastructure must keep customer traffic segmented across locations.
Limited AI usage visibility
Teams need visibility into LLM token usage and response times to understand how AI services are being used.
NearbyOne
Service and workload orchestration
- Service OrchestrationManages customer and tenant creation, application catalogs, and edge-site selection across distributed sites.
- Workload LifecycleHandles the deployment and lifecycle of Kubernetes and virtual machine workloads.
Aviz AI Factory Portfolio
Network automation and AI visibility
- Aviz ONESAutomates network fabric bring-up and operations, monitors the network, and enforces tenant segmentation and workload isolation.
- Aviz AI-ParserProvides token-usage and response-time visibility for supported LLM workloads.
Deploy together
Align application rollout with required network provisioning.
Isolate tenants
Support separation of customer traffic on shared infrastructure.
Understand AI usage
Track LLM token usage and response times to understand AI workload consumption.
Operate across sites
Apply a more consistent approach across distributed environments.
Trusted across the AI infrastructure ecosystem
