Visit the operations team of almost any AI cluster and you will find open-source tools doing most of the monitoring. Zabbix watches network devices, servers and facility equipment. Prometheus scrapes metrics from Kubernetes, nodes and services. NVIDIA's DCGM exporter feeds GPU telemetry into Prometheus. NetBox holds the record of sites, racks, devices, cables and IP addresses. Each tool is good at its job, widely understood and free to run. The trouble sits between them: four views of the same hardware, with different names for the same server, separate alerting, and no shared understanding of what is connected to what.
What each tool does well
Zabbix is strong at polling devices that cannot run modern exporters: switches, PDUs, UPS systems, cooling units and BMCs, over SNMP, IPMI, agents and scripts. Its templates and triggers are mature, it stores long history, and many facilities and network teams already know it.
Prometheus is strong at high-frequency metrics from software and from Linux hosts. Exporters exist for almost everything, the query language is flexible, and it fits naturally with Kubernetes, where many AI workloads run. Alertmanager handles routing and grouping of alerts.
DCGM, through the dcgm-exporter, publishes GPU health and performance metrics in Prometheus format: utilisation, memory use, temperatures, power, clock throttling, ECC and remapping counters and interconnect errors.
NetBox is a source of truth for the physical and logical estate: sites, rooms, racks and rack positions, devices and their components, cables, power feeds, IP addresses and VLANs. It describes what should exist and how it is connected.
Where the gaps appear
Identity is the first gap. The same server can be "gpu-node-117" in Prometheus, "10.40.3.117" in Zabbix, a serial number in the BMC and "R07-U22" in NetBox. Linking an alert to the right physical machine takes a lookup, and lookups at 3 a.m. go wrong.
Alerting is split. Zabbix triggers and Prometheus alerts go to different channels with different severities and escalation rules. One physical fault can raise alerts in both, handled by different people.
Topology is missing from alerts. NetBox knows that a server hangs off a particular PDU and switch port, but neither Zabbix nor Prometheus uses that knowledge when deciding whether forty alerts share one cause.
Facility and GPU data live apart. The cooling unit's fan alarm in Zabbix and the thermal throttling in DCGM data describe one problem, but nothing joins them.
Business context is absent. Neither monitoring tool knows which tenant, project or service each GPU belongs to, so usage and incidents cannot be reported by customer.
A worked example of one server
| Tool | How it names the server | What it knows |
|---|---|---|
| NetBox | Device "gpu-r07-u22", serial ABC123, rack R07 position 22 | Location, power feeds A and B, switch ports, owner tenant |
| Zabbix | Host "10.40.3.117 (BMC)" | BMC sensors, PSU status, fan speeds, SNMP data from its PDU |
| Prometheus | Instance "gpu-node-117:9100" | OS metrics, Kubernetes pods running on it |
| DCGM exporter | Hostname "gpu-node-117", GPUs 0 to 7 | GPU health, errors, temperatures, utilisation |
Four records, three naming schemes, one machine. Integration starts by making every record carry a shared identifier.
Patterns that work
Use NetBox as the identity spine. Give every device a stable name and serial in NetBox, and propagate them outward: as labels on Prometheus targets, as host names or tags in Zabbix, and as metadata on GPU metrics. Several community plugins and scripts generate Prometheus target lists and Zabbix hosts from NetBox, which keeps them in sync.
Enrich metrics with location and ownership. Once every metric carries the device name, adding rack, row, power feed and tenant from NetBox becomes a join. Dashboards and reports can then be cut by any of them.
Route alerts through one place. Send Zabbix and Alertmanager alerts into a single pipeline with shared severity definitions, deduplication and on-call rules.
Correlate with topology. Use the connections recorded in NetBox to group alerts: if a PDU alarms and twenty servers on that PDU report power supply alerts, that is one incident with one probable cause.
Keep the tools patched and private. Monitoring systems hold credentials and network maps, and their web interfaces run on common stacks such as PHP. Security advisories for those stacks appear regularly. Keep frontends off the public internet, restrict access, and apply updates on a schedule.
Plan retention and scale. A large GPU cluster produces many millions of time series once every GPU, port and sensor is collected at high frequency. Prometheus on its own keeps a limited local history, so most sites add long-term storage for it and agree which metrics are kept at full resolution and which are downsampled. Zabbix history and trend settings need the same decision. Agreeing these limits up front avoids discovering, during an incident review, that the data from three weeks ago is gone.
Build or adopt
Teams can build this integration themselves with scripts, plugins and a correlation layer, and many do. The cost is ongoing: every upgrade of any tool can break a script, and the knowledge often sits with one or two people. The alternative is a platform that connects to the existing tools, keeps them in place for the teams that like them, and adds the shared identity, correlation and reporting on top.
Either way, the goal is the same: when something breaks, one person can see the GPU, the server, the rack, the power and cooling around it, and the tenant affected, in one view and within a minute.
CloudSino iDCOS accepts alarms from the monitoring systems you already run through API, SDK, SNMP trap, email and syslog, and links them to resources and configuration items in its CMDB. DCOS adds the out-of-band hardware layer, GPU data comes in through NVIDIA DCGM, and SmartBSM correlates the combined alarms toward a root cause. Your teams keep their existing tools. Ask us to connect it to a test copy of your stack.
Frequently asked questions
How do I connect Zabbix, Prometheus, DCGM and NetBox?
Start by making NetBox the source of device identity. Give each device a stable name and serial there, then push it out as Prometheus target labels, Zabbix host names or tags, and metadata on DCGM exporter metrics. With a shared identifier in every record, you can add rack, power feed and tenant to any alert and route all alerts through one pipeline.
How do I get NVIDIA GPU metrics into Prometheus?
Run NVIDIA's dcgm-exporter on each GPU server. It exposes DCGM data in Prometheus format, including utilization, memory use, temperatures, power, clock throttling reasons, ECC and row remapping counters and interconnect errors. Prometheus scrapes it like any other exporter, and Alertmanager can route the alerts. Label the targets with device names that match your inventory.
Should I use Zabbix or Prometheus for data center monitoring?
Many AI sites run both. Zabbix suits devices that cannot run exporters, such as switches, PDUs, UPS units, cooling equipment and BMCs, polled over SNMP or IPMI, with long history. Prometheus suits high-frequency metrics from Linux hosts, Kubernetes and services. The real work is giving both a shared device identity and a single alert pipeline.