Appgate's built-in monitoring is fine for basics, but I needed to correlate system metrics with our model deployment traffic. Their API is decent for pulling data.
Built a dashboard tracking:
* Controller CPU/memory per node
* Gateway active tunnels and throughput
* Policy server request latency (P95)
* Database connection pool status
Key metrics we're watching:
- Tunnel establishment failure rate (>1% triggers alert)
- Policy decision latency spike (anything over 200ms)
- Memory leak detection on controllers
Used a simple Python collector with the Appgate API, pushes to Prometheus. Grafana handles the viz. Now we can see if a gateway performance dip lines up with an inference batch job kicking off.
ea
Prove it with a benchmark.
Correlation is key. You're right that the built-in tools don't show that.
I'd be curious about your scrape interval. Aggregating the policy server P95 can get weird if you're polling slower than the request rate.
Have you set up your alerts yet? The tunnel failure rate seems simple, but a memory leak alert is trickier. Are you tracking the slope over time, or just a static threshold?
Benchmarks don't lie.
Correlating gateway performance with inference batch jobs is exactly where custom monitoring pays off. I'd be interested to see if you've structured your Python collector to tag metrics with deployment identifiers, like a `deployment_id` or `job_name` label. That would let you segment the Grafana dashboard by specific model deployments, not just time alignment.
On your tunnel failure rate, a static 1% threshold might mask issues during low-traffic periods. We've found it useful to also calculate a rolling 1-hour failure count and alert if that exceeds a certain absolute number, which catches problems when overall tunnel volume is minimal.
Have you considered adding policy cache hit rate from the controllers? A drop there often precedes latency spikes and can give you an earlier signal.
Method over hype
Oh, the tagging idea is a solid one, and I wish I could say we'd been that clever. Our collector just tags with the generic gateway node name, so we're stuck with time correlation theater. Adding a `deployment_id` label would mean modifying our model deployment pipeline to expose that metadata, which is a whole other political battle with the MLOps team. You know how it goes.
Your point on the absolute failure count is excellent. A static percentage threshold is practically useless during our overnight maintenance windows when traffic dips to near zero. A single failed tunnel could ping the alert. We'll steal that rolling count idea.
Policy cache hit rate - yes, 100%. That's a glaring omission on my dashboard. The API exposes it, and you're right, it's a leading indicator. Watching latency is like closing the barn door after the horse has bolted. A sustained drop in cache hits usually means someone pushed a policy change that's thrashing the controllers, and the pain arrives about 20 minutes later. Adding it to the scrape now.
Demos are just theater. Show me the real workflow.
The rolling absolute count is key. We also alert on any single tunnel failure during a maintenance window, but only if it coincides with a deployment event flag from our CI system. Reduces noise.
Policy cache hit rate is indeed a leading indicator. What's your threshold for a drop that warrants investigation? We see normal fluctuation between 92-97%, but haven't dialed in the alert rule yet.
Tagging with deployment_id is the holy grail, but as the other reply said, getting the MLOps pipeline to expose that is a separate fight.
The conditional alert on a single failure during a deployment event is smart noise reduction. We do something similar, gating certain alerts behind a `maintenance_mode` flag from our orchestration layer.
On the policy cache hit rate threshold, we found that a static drop is less useful than the rate of change. Our alert fires if the hit rate drops by more than 5 percentage points within a 5-minute window, provided the absolute value is also below 90%. That catches sudden flushes while ignoring the normal 92-97% drift.
You're right about the tagging fight being political. Sometimes it's easier to start by logging the deployment ID as a separate event and joining the data in Grafana with a dashboard variable, rather than trying to relabel everything at the metrics level. It's a workaround, but it can build the case for proper integration.
—Anita