ExtremeCloud IQ vs NVIDIA Config Manager

ExtremeCloud IQ provides machine-learning-driven unified management for access points, switches and SD-WAN. It offers real-time and historical views of client and device health with built-in remediation tools. Automatic configuration policies, near zero-touch deployment workflows and Instant operations support onboarding and provisioning. Enhanced WiFi Maps help with network planning and optimization. The product supports OpenAPI-standard APIs for provisioning, management and customization. It can manage Cisco and HPE Aruba switches, while other third-party devices receive basic setup and monitoring. Deployment options include public cloud, private cloud and ExtremeCloud Edge; the product page describes 21 global points of presence across AWS, Microsoft Azure and Google Cloud Platform. Licenses are pooled and portable across devices, locations and users, covering wired and wireless devices. Pilot includes guest management, wireless intrusion prevention and location services; CoPilot adds anomaly detection and experience monitoring. Pricing is on request, with a 90-day trial and no free plan.

NVIDIA Config Manager is an open-source platform for bootstrapping and operating NVIDIA AI-factory network infrastructure. It is deployed with Helm on Kubernetes, with an installer that supports interactive and headless command-line workflows. Documented network tasks include configuration backup and deployment, cable and hardware validation, password rotation, reprovisioning, and operating system or firmware upgrades.

Core components include workflow orchestration, template rendering, PostgreSQL-backed configuration storage, zero-touch provisioning, and Kea DHCP configuration generation. Nautobot is the default bundled DCIM provider; other providers can connect through a provider-neutral SDK. Browser authentication uses an OIDC proxy, while API clients use bearer tokens. Approval workflows and an audit trail cover day-two changes. Airgapped bundles can include charts, images, manifests, and an offline installer. Standard deployment requires Kubernetes access, kubectl, Helm 3.x, Python 3.13 or later, and uv. The project describes the software as Developer Preview and not recommended for production use.