MONITOR THOUSANDS OF CAMPUS DEVICES FROM THE EDGENetdata runs directly on every campus device, giving your IT team per-second visibility into compute, connectivity, processes, and OS health across thousands of school safety devices, gateways, and shared endpoints. Catch a failing device before it goes dark, on a predictable per-node budget.
Edge-resident intelligence, tiny footprint, and a distributed design built for thousands of devices on constrained school networks.
Every campus device collects and stores CPU, RAM, disk, network, and process metrics at 1-second granularity. See a failing safety device the moment it starts degrading.
Netdata Agents process data locally. Parents aggregate per campus or district. Only metadata reaches Netdata Cloud. Constrained school networks stay uncongested.
Eighteen unsupervised ML models train per metric on every device, scoring anomalies in real time. Detect OS faults, connectivity drops, and process crashes before a device goes dark.
Auto-discovery detects 800-plus integrations with no manual setup. Deploy across thousands of endpoints with a single command. New devices appear in dashboards immediately.
Simple per-node pricing with P90 billing protection means monitoring 5,000 devices costs what you expect. Agents are free and open source. No data-volume penalties.
Agents buffer locally during network partitions and backfill Parents when connectivity returns. Monitoring continues even when a school WAN link drops.
Trusted by IT and operations teams managing distributed fleets worldwide
1-second granularity on every device
Learn about edge fleet monitoring
90% cost reduction vs traditional monitoring
See the cost model
Zero data loss during network partitions
Understand edge resilience
One platform replaces ~7 tools
Explore troubleshooting features
80% faster MTTR with AI-powered RCA
Discover Anomaly Advisor
Architecture matters when you monitor thousands of devices
The right architecture for distributed device fleets is not a centralized database that charges by volume. It is edge-resident intelligence that keeps data local and budgets predictable.
Capability
Netdata
Centralized SaaS / Traditional Tools
Data Location
Where telemetry is processed and stored
✅ Edge-Resident
Data stays on each device, processed locally
⚠️ Centralized
All telemetry shipped to a central cloud or database
Metric Granularity
Resolution of collected device data
✅ Per-Second
1-second collection and visualization on all metrics
⚠️ 10-60 Seconds
Coarse intervals miss transient device failures
Network Resilience
Behavior during campus WAN outages
✅ Store and Forward
Agents buffer locally, Parents backfill on reconnect
❌ Data Loss Risk
Blind spots when devices lose connectivity
ML Anomaly Detection
Per-device behavioral baselining
✅ 18 Models Per Metric
Trained locally on each device, zero configuration
❌ Limited or Cloud-Based
Centralized ML or manual threshold tuning
Pricing Model
How costs scale with fleet size
✅ Per-Node, Predictable
P90 billing, no data-volume penalties, agents free
❌ Volume-Based
Costs explode with per-metric or per-ingest pricing
Setup Complexity
Effort to deploy across thousands of devices
✅ Zero-Configuration
Auto-discovery, automated dashboards, one-line install
⚠️ Manual and Complex
Dashboards, collectors, and pipelines to build
Log Management
Accessing device logs at fleet scale
✅ Direct Journal Access
systemd-journal queried locally, no log pipeline
❌ Expensive Pipeline
Ship, index, store, and query in a separate stack
Resource Footprint
Overhead on resource-constrained devices
✅ Under 5% CPU, 150MB RAM
Designed for edge devices and constrained hardware
⚠️ Heavy Agent
Significant CPU and memory overhead per host
Data Sovereignty
Control over device telemetry location
✅ On-Site by Design
Metrics and logs never leave your infrastructure
⚠️ Vendor-Controlled
Data stored in vendor cloud, residency concerns
Eighteen unsupervised ML models train locally on each campus device, scoring anomalies per second with near-zero false positives. No cloud round-trip, no data egress, no manual tuning.
Detect a failing safety device before it goes dark
Learn about ML anomaly detectionThe outcomes that matter when monitoring thousands of campus safety devices.
Per-second metrics and ML anomaly detection surface degrading devices before outages.
Per-node pricing means monitoring 5,000 devices costs what you expect.
Agents and Parents keep monitoring alive during school network outages.
Every device metric collected at 1-second granularity with zero sampling.
All telemetry remains on your devices and infrastructure by design.
Zero-config auto-discovery and one-line install across thousands of endpoints.
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