How Network Operations Teams Use InfluxDB to Solve Network Monitoring Gaps

By Mike Devy / Patrick Oliver Developer
Feb 05, 2026

Organizations are starting to question whether the value they get from traditional Network Monitoring Systems (NMS) justifies the budget they’ve locked into them.

On the technical side, network operations teams are dealing with more complexity than ever. Environments are dynamic, traffic patterns shift quickly, and the cost of outages keeps rising. Meanwhile, many traditional platforms haven’t kept pace. Their data pipelines and discovery workflows lag behind how modern networks actually behave. At the same time, pricing and licensing changes are making NMS and Network Performance Management (NPM) solutions even more costly. SolarWinds is a clear example: after its acquisition by Turn/River and shift to a subscription-based licensing model, users have reported a price increase of over 100%.

This is exactly where one of our largest enterprise customers found themselves, anonymous here due to regulatory requirements. They found that their NMS had blind spots that no amount of tuning could fix. Rather than continue pouring budget into SolarWinds to chase diminishing returns, they reallocated spending to implement a network monitoring solution built around InfluxDB. It closed the gaps immediately, restored the visibility they needed for day-to-day reliability, and gave the organization room to decide what comes next.

Below are a couple of the main networking monitoring challenges this team faced, why their NMS couldn’t address them, and how they used their InfluxDB-centric solution to close their network monitoring gaps.

Network spike detection

The operations team kept seeing Virtual Fabric Drops (VFDs) and intermittent link flaps on a 400 Mbps data center interconnect, but nothing in their NMS showed utilization anywhere near the levels that should trigger them. In fact, it appeared the link never broke ~365 Mbps.

The underlying issue was short, high-intensity traffic spikes that the NMS could not capture. With a five-minute polling interval, each window was averaged into a single utilization value. Spikes that lasted only a few seconds never aligned with the polling timestamps and were smoothed into what looked like normal traffic.

The team identified the real pattern only after collecting 1-second metrics from their Arista switches with Telegraf and storing them in InfluxDB. At that resolution, the spikes were obvious and lined up exactly with the VFD events. Their Cisco switches, limited to 30-second polling under SolarWinds, simply couldn’t provide the granularity needed to reveal this behavior.

CPU monitoring granularity

The operations team was seeing intermittent performance issues on a Palo Alto firewall, but nothing in their monitoring system indicated CPU saturation. Throughput and latency symptoms suggested load problems, yet the reported CPU utilization stayed around 50%, well below any alarm thresholds.

The underlying issue was the way the NMS collected and reported CPU metrics. The firewall has separate data-plane and control-plane CPUs, and the platform’s default behavior was to average them. In the incident in question, the data-plane CPU was at 99% while the control-plane CPU sat at 2%, and the averaged value masked the data-plane saturation entirely. As a result, the primary indicator of forwarding stress never surfaced.

When the team pulled per-CPU metrics into InfluxDB using Telegraf, the data-plane spikes were immediately visible and aligned with the observed performance degradation. From there, they set independent alerts for each CPU so data-plane saturation would be detected directly. While the NMS could have been customized to approximate this view, InfluxDB provided the necessary granularity by default, making the issue straightforward to diagnose and monitor going forward.

Dynamic VIP monitoring

The team noticed that Virtual IP (VIP) metrics were incomplete or out of date, and some newly created services weren’t showing up in their monitoring at all. The gaps appeared random, but they pointed to a visibility issue rather than an application problem.

The root cause was straightforward. Their NMS couldn’t automatically discover or track new VIPs as they were created, moved, or retired. Each VIP had to be added manually, and anything not configured manually wasn’t monitored. In a dynamic environment, that meant missing data and inconsistent coverage.

Once the team switched to an InfluxDB-centric approach, the issue went away. Telegraf pulled VIP information directly from their AVI load balancer, and each VIP, along with its metrics, was written to InfluxDB as soon as it became available. Monitoring kept pace with the environment without any manual steps. This was especially useful in deployments where VIPs changed frequently, reducing overhead and ensuring complete, up-to-date visibility across the entire set of VIPs.

How InfluxDB+ addresses NMS observability gaps

Most NMS platforms miss the same categories of data: short-lived spikes, per-component metrics, dynamic objects like VIPs, and anything outside their predefined device models. An InfluxDB-centric stack fills those gaps without replacing your existing tools.

Key Components of the Stack

• Telegraf — Collects high-resolution metrics from devices across the network.
• InfluxDB 3 Enterprise — Ingests telemetry at scale and provides fast queries for both recent and historical data.
• Grafana — Visualizes the data and supports operational dashboards and alerting.

Telegraf acts as the universal collector. It pulls metrics, every second or faster, from routers, switches, firewalls, load balancers, storage systems, and virtual infrastructure using SNMP, gNMI, and vendor APIs. It captures interface counters, per-CPU usage, packet drops, latency, queue depth, and other operational signals. Telegraf streams all of this telemetry—thousands of series from across the environment—directly into InfluxDB at full fidelity.

InfluxDB 3 is the core of the stack. It ingests high-resolution telemetry at scale and provides fast access to the recent data needed for dashboards, alerts, and operational workflows. At the same time, it retains full-fidelity history at low cost, giving teams a single place to analyze both real-time conditions and long-horizon trends. The processing engine supports real-time evaluations and, when paired with tools like Grafana, it delivers continuous, high-resolution visibility across the entire environment.

Future proofing your network monitoring stack

If there’s one lesson from this customer’s experience, it’s that network monitoring is shifting fast. Networks are more distributed, more dynamic, and far more dependent on real-time signals than traditional NMS platforms were built to handle. Polling cycles, rigid device models, and closed data pipelines simply can’t deliver the visibility modern operations teams need.

InfluxDB 3 + Telegraf gives operations teams a way to work past those constraints. New devices, protocols, and metrics can be onboarded immediately, without waiting for vendor updates. And because the platform stores full-fidelity telemetry inexpensively, teams keep both the real-time signals they need for operations and the long-term history required for deeper analysis.

That combination of real-time visibility into high-resolution telemetry and cost-effective retention supports the broader remit of modern network operations teams. They are responsible not only for day-to-day reliability but also for the long-term work that depends on complete data: capacity planning, drift detection, anomaly identification, and cross-system correlation.

In short, if you are running into similar visibility gaps or preparing for a more complex environment, you have options. InfluxDB can fill specific weak spots, operate alongside your existing NMS as a high-resolution telemetry layer, or replace the legacy platform entirely. Unlike traditional NMS tools, it doesn’t lock you into a fixed model or licensing scheme. The stack scales with your network instead of constraining it.