Hi everyone — I’ve been running Zscaler ZPA for a couple of years now, primarily to secure access for our remote and hybrid teams. While the Admin Portal gives a good high-level view, I found myself wanting more granular, historical, and real-time visibility into connector health, especially since our team relies heavily on several internal apps. I wanted to catch degradations before they turned into user helpdesk tickets.
So, I built a custom Grafana dashboard that pulls data from the ZPA APIs, and it’s been a game-changer for our ops peace of mind. I thought I'd share the approach here for anyone looking to move beyond the built-in monitoring.
My goal was to track not just "up/down," but trends and performance that could indicate looming issues. Here’s what the dashboard surfaces:
* **Connector Status & Resource Utilization**: Live view of CPU, memory, and disk usage across all connectors, with alerts if any single metric crosses a threshold for more than 10 minutes.
* **Session & Throughput Trends**: Graphs for active sessions and data throughput per connector, which helps us right-size capacity and spot unusual patterns (like a sudden drop that might mean a routing issue).
* **App Segment & Segment Group Health**: We monitor specific critical application segments to ensure their availability and see which connector they’re using. This was crucial for troubleshooting a recent issue where an app was slow only for certain regions.
* **API Latency**: We even graph the response time of the ZPA API calls themselves—if our monitoring starts getting slow, it’s a canary for potential wider issues.
To build it, I used the ZPA Admin API (v2) with a read-only service account. The data is polled every 5 minutes by a lightweight Python script that transforms the JSON responses and writes them into a Prometheus time-series database. From there, Grafana does the rest. The key was focusing on the metrics that actually predict problems for *us*—not just collecting everything.
The biggest win has been proactive notifications. We’ve caught memory leaks on connectors and identified under-provisioned instances well before users noticed. It also settled a debate about whether we needed more connectors in Europe—the data showed we were well within limits.
If you're considering something similar, my advice is to start with one or two key metrics that keep you up at night. For us, it was connector memory usage and app segment status. You can always expand from there.
I’m curious—has anyone else built custom monitoring around ZPA? What metrics did you find most valuable? I’d love to compare notes and maybe pick up some new ideas to add to our board.
~Jane
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What's the alert threshold? "Crosses a threshold" is the key phrase. Without specifics, it's just a light that turns red.
And how are you defining "unusual patterns" in throughput? Automated anomaly detection or just someone eyeballing it?
If it's the latter, you'll miss the slow creep.
If it's not a retention curve, I don't care.
Pulling metrics directly from the ZPA APIs is a solid move. The Admin Portal's aggregation often smooths over per-connector spikes. Are you also capturing the standard deviation of resource utilization over a rolling window? A 10-minute threshold on a raw value can miss the gradual resource exhaustion patterns that cause intermittent latency.
The session and throughput graphs are crucial, but the value is in correlating them. Have you set up a panel that plots active sessions against per-session average throughput on the same time axis? A divergence there, where sessions stay high but per-session throughput plummets, is often the first sign of a bottleneck at the connector's upstream link or a misbehaving backend application segment. It turns two operational graphs into a single diagnostic one.
--perf
This is exactly the kind of monitoring I'm trying to get better at. The Admin Portal alerts always felt too late.
How are you handling the API authentication and data collection? Are you using a script to poll and push to a time-series DB, or something more direct? I'm weighing the setup complexity against just relying on the portal.
You're right about the threshold problem. We started with static limits but got burned by legitimate traffic surges tripping false positives. So I bolted on a simple anomaly detection script that uses a rolling 7-day median and flags anything beyond two standard deviations. It's not fancy ML, but it catches that slow creep where resource utilization inches up 5% a day for a week.
The eyeball method is a trap. For throughput, we're looking at the rate-of-change more than the absolute value. A panel that graphs the first derivative of throughput over a 5-minute window will spike before the throughput graph itself looks bad.
Nice approach focusing on trends over simple state. The ten-minute threshold is a good start, but you'll need to tune it per metric. Memory can spike briefly and recover, but a sustained high CPU plateau often warrants a faster alert.
Have you considered exporting these thresholds as configurable variables in the dashboard? That lets each team set their own sensitivity based on their connector's typical workload. A finance app connector might need tighter tolerances than one serving static documentation.
Also, for the session graphs, are you tracking failed session attempts separately? A drop in active sessions could mean less traffic, but a rise in failed attempts with steady traffic points to a different problem.
Commit early, deploy often, but always rollback-ready.
It's great you're pulling directly from the API, but "alerts if any single metric crosses a threshold for more than 10 minutes" has me skeptical. Did you derive those thresholds from actual baseline behavior, or are they just vendor defaults or educated guesses? A static threshold, even with a 10-minute buffer, is how you end up alerting on Tuesday's normal workload or missing the Wednesday midnight creep.
The real test is whether your dashboard can tell you why the threshold was crossed, not just that it happened. Are you correlating those CPU spikes with a specific application segment's session volume at that exact time? Otherwise, you've just built a prettier, delayed version of the same red light.
Data skeptic, not a data cynic.
You make a good point about static thresholds. That's my main worry with setting this up - I don't have enough historical data yet to know what normal looks like for our connectors.
Are you suggesting we just run it for a few weeks in "monitor-only" mode to collect a baseline before enabling alerts? That seems sensible, but I'm concerned about missing a real issue during that blind period.
I'm right there with you on moving beyond simple up/down. The historical data is what's really valuable. Once you have a few weeks of trends logged, you can start to see what "normal" looks like for your specific environment, which makes those alerts infinitely more meaningful.
One thing I'd add is that graphing the *ratio* of session attempts to successful sessions over time has been our earliest indicator for several weird issues, like DNS hiccups or subtle policy misconfigurations. A throughput dip might come later.
What time-series database are you using to store the polled API data? I went with Prometheus, but I've seen others use InfluxDB.