Finally. Wireless the way it should be.

- Scale from the largest to the smallest enterprise businesses for rapid updates
- Single click activation for streamline rollouts
- Wired, Wi-Fi and WAN Assurance for full lifecycle management
- ZTP Configuration across AP, Switch and WAN gateway
- Template Driven
- Use Site variables to easily customize as neede

- Virtual controllers hosted in co-located data centers
- Catalyst persona APs can be switched to Meraki persona only through a time consuming manual process
- Meraki persona APs can be switched back to Catalyst persona only through a time consuming manual process

- On-premises with no cloud offering for SDA
- Uses a centralized, proprietary controller
- Are you going to use Meraki to monitor your Catalyst AP’s?
- Expensive DNAC or DNAS licensing required for

- Elastic vertical and horizontal scale
- No expensive hardware required

- Complex and non-elastic
- Virtual controllers (Containers) hosted in co-located data centers
- Requires separate servers to scale (Aruba Clearpass, etc)

- Non-elastic with more controllers required as you add APs or require redundancy

- Client visibility across wired, wireless, and NAC
- Complete visibility from onboarding to sequences of events

No

- No end-to-end client-event visibility and no sequence of events across wired, wireless, and NAC.
- When troubleshooting client connectivity experience issues, customers need to look into debug Live Logs on ISE for authentication failures and troubleshoot network separately in a different product (WLC, DNAC, etc.)

- Based on reinforcement learning:
- Optimizes channel/power with AI-based reinforcement learning
- AI continuously maximizes User experience (SLE) and minimizes interference in real-time
- Adapts dynamically on an ongoing basis while network under load learning from client experience
- Learns and deprioritized triggered DFS channels to boost network uptime
- Coverage SLE is an ongoing 'Site Survey'

- Basic RRM
- will monitor DFS failure patterns
- AP's remember their settings through power failures
- Won't make changes in 'busy hours'

- Basic RRM. No AI/ML, requires several days of tuning.
- 15-year old algorithm
- Based on how APs hear each other
- Optimizes channel/power based solely on AP interference graph
- RRM is performed on a static, periodic basis when the load is low

- Automated event correlation using machine learning across wireless/wired device domains
- Provide real actionable intelligence

- Basic root cause analysis (suggestions) based on event logs

- Limited RCA
- Requires expensive DNA appliances

- Proactively captures packets when an error event occurs in real-time
- Eliminates need to reproduce issues as every failure has a PCAP starting before the failure and playing though it
- No more sending out tech folks with sniffers *after* the problem has happened

Manual

- Intelligent Packet Capture
- First a client needs to file a ticket
- Then the client will be tagged to collect data going forward
- Not at all automatic

- Proactively identifies anomalies and uses data science tools to determine root cause
- Leverages both Wired and Wireless SLEs for anomaly detection
- 3rd generation algorithm with ARIMA boosts efficacy
- Anomaly detection performed across Wi-Fi, LAN, WAN, Security
Domains

- 1st generation anomaly detection algorithm
- Will go through a weeks worth of data to find some basic anomaly

- 1st generation anomaly detection algorithm
- Limited anomalies detected
(DHCP, AAA, Association, Throughput)
- Requires Cisco DNA appliances (3+)

- Juniper Mist Access Assurance provides automatic scaling, service geo-affinity for optimal latency and service redundancy. In addition, periodic hitless feature and security updates to Access Assurance happen automatically and do not require downtime. Customers need not worry about client scale, redundancy, geographic redundancy and affinity.

Not available

- Customers need to design and deploy NAC infrastructure depending on the number of client devices, design and deploy NAC and network infrastructure for redundancy, geo affinity. Furthermore, any feature or security update requires downtime planning and manual execution for every server in the cluster.

- Continuous learning through Supervised Machine Learning
- Performs root cause analysis for most detected network issues
- Supports wireless, wired and WAN at a site level
- Troubleshoot issues instead of pulling logs
- Can be accessed through WebUI or API
- Built on 6 years of continuous learning and rich data science toolbox

- Dashboard
- No virtual assistant

- Dashboard
- Chatbot rumored but not productized nor available to customers in beta