Juniper Networks vs. Cisco

Juniper’s wireless solution scales for all business sizes, boasting features such as single-click activation and comprehensive lifecycle management. Zero Touch Provisioning (ZTP) is implemented across devices with template-driven configurations, facilitating easy customization through site variables.

Cisco Meraki's infrastructure relies on virtual controllers that are hosted in co-located data centers. Switching between Catalyst and Meraki personas is a lengthy manual process.

Cisco's wireless solution operates mainly on-premises, lacking a cloud component for SDA. With a centralized controller, questions arise regarding monitoring options for Catalyst Access Points. Additionally, adoption comes with the requirement of expensive DNAC or DNAS licensing.

Showcase adaptability with elastic vertical and horizontal scaling capabilities. Deploying Juniper Mist APs reduces the need for additional costly hardware investments.

Cisco Meraki's infrastructure leans towards complexity and lacks elasticity. It relies on virtual controllers hosted in co-located data centers. To scale the Meraki setup effectively, separate servers, such as Aruba ClearPass, become necessary, potentially adding complexity and cost to network expansion.

Cisco's system is non-elastic, necessitating additional controllers as APs are added or redundancy is required.

Juniper’s Mist Access Points excel in delivering unparalleled end-to-end visibility across wired, wireless, and NAC domains. Using AI-driven insights and advanced analytics, Mist APs present a unified perspective, enabling administrators to oversee the complete network and promptly address issues.

Cisco Meraki provides insights through a cloud-managed dashboard but lacks comprehensive visibility across sequential events.

Administrators must navigate multiple, distinct products to diagnose and troubleshoot network challenges, which adds to the complexity and challenges of efficient network management.

Harness the power of AI-driven RF optimization using reinforcement learning. AI-based reinforcement learning refines channel and power settings, continuously maximizing the User experience (SLE) and reducing interference in real-time.

Basic RRM functionality, with the ability to monitor DFS failure patterns. While AP settings are retained post power failures, changes are restricted during 'busy hours', potentially compromising peak performance.

Operating with an older RRM system, Cisco APs lack AI or machine learning enhancements. The 15-year-old algorithm focuses on AP inter-communication, optimizing channel and power settings based on an AP interference graph. RRM adjustments occur on a static, periodic basis when network load is at its lowest.

Built on cloud-native Network Access Control (NAC) architecture, Mist Access Points stand at the forefront of modern network architecture. By embedding geographic redundancy and affinity into their framework, Juniper Mist APs deliver robust, adaptable, and geographically harmonized network solutions.

Cisco Meraki Access Points (APs) are not built on a cloud- native Network Access Control (NAC) architecture.

Designing and deploying Network Access Control (NAC) infrastructure is influenced by client device demands and the need for redundancy and geo-affinity. Additionally, feature or security updates necessitate downtime planning and manual execution across all servers in the cluster.

Experience the future of network management with Juniper's Marvis®, the virtual network assistant. Utilizing Supervised Machine Learning, Marvis swiftly performs root cause analyses, seamlessly supporting wireless, wired, and WAN at every site. Forget pulling logs; troubleshoot directly via WebUI or API. With six years of advanced learning and a robust data science toolbox, Marvis revolutionizes networking.

Tethered to conventional network management methods, relying heavily on dashboards and logs for insights and troubleshooting.

No virtual assistant for network management. Users navigate complex dashboards and sift through logs, resulting in longer resolution times, especially compared to cutting-edge AI-driven solutions.