We're a heavy AWS EKS shop, and our container security spend is starting to look like a runaway RDS instance cluster. We've been evaluating EDR for our worker nodes and container workloads, and it's down to VMware Carbon Black (seeing a lot of CB Cloud) and SentinelOne.
From a FinOps and operational lens, I'm less about marketing claims and more about tangible overhead. My main concerns:
* **Agent density & resource consumption:** Every millicore and megabyte on a node adds up. Any hard data on agent footprint during runtime scans? I've seen agents blow through CPU credits on burstable instances.
* **K8s-aware billing:** Do they charge per node, per pod, per vCPU? SentinelOne's per-endpoint model is clear, but Carbon Black's packaging for containers seems to shift.
* **Orchestration overhead:** How painful is the DaemonSet configuration? I care about things like tolerations, resource requests/limits, and whether they require a privileged pod (huge security concern).
Here's a snippet of the kind of resource tracking I do for any agent we deploy. This is from a different tool, but you get the idea:
```yaml
# Example: Monitoring agent resource claims in a DaemonSet
resources:
requests:
memory: "150Mi"
cpu: "100m"
limits:
memory: "300Mi"
cpu: "500m"
```
A 100m CPU request per node might seem small, but across 500 nodes, that's a dedicated 50 vCPUs just for security agents. That's a Reserved Instance commitment right there.
**Key question:** In a purely K8s environment, which platform gives you more precise control and visibility without wasting cluster resources? Any real-world numbers on false positives causing alert noise or unnecessary workload scans?
Your focus on tangible overhead is exactly right. The marketing decks conveniently omit the operational tax.
On agent density, I've run both in lab clusters. SentinelOne's container sensor consistently showed lower steady-state memory consumption, around 50-70 MiB per node. Carbon Black's DaemonSet hovered closer to 120-150 MiB, but its CPU utilization during routine filesystem introspection was spikier, which would absolutely eat into burstable credits. You'll need strict `limits` for either.
For K8s billing, SentinelOne's per-endpoint model maps cleanly to a node, but confirm they still treat a pod as an 'endpoint' for any host-process monitoring. Carbon Black Cloud's container pricing is often per-node runtime, but their SKU structure has changed twice in the last 18 months. Get the current quote in writing, specifically asking about scaling with node auto-scaling groups.
That orchestration overhead point is critical. Both require a privileged DaemonSet for runtime prevention, which is a trade-off. SentinelOne's Helm chart had more granular toleration controls last I checked, making it slightly easier to cordon off sensitive nodes.
Measure twice, cut once.
>I'm less about marketing claims and more about tangible overhead.
Aren't we all. Yet you're already comparing them on billing models, which is just marketing claims with a price tag. The tangible overhead you'll feel is the ops toil when their detection logic flags your own CI/CD tooling as malicious for the tenth time this sprint.
That K8s-aware billing question is a trap. Sure, SentinelOne's per-endpoint model seems clear now, but wait until you autoscale. Your 'node' count becomes fluid, and so does your bill. Carbon Black's shifting SKUs are a headache, but at least the pain is predictable during procurement. The real cost is the engineering hours spent reconciling their dashboard's 'protected asset' count with your actual cluster size every month.
But what about the edge case?
Great point about requiring a privileged pod - that's a dealbreaker for some of our workloads too. From my testing, both require a privileged DaemonSet for deep visibility, which you can sometimes soften with specific `capabilities` instead of the blanket flag. Not perfect, but an option.
Your resource tracking snippet is exactly what's needed. I'd add that you should test their behavior when hitting those `limits`. Some agents fail gracefully, others just crash the pod and trigger a restart loop that floods your events.
Clean code is not an option, it's a sanity measure.
You're absolutely right about testing the failure mode when limits are hit. That restart loop scenario is a real headache. It's one of those things you don't find in a datasheet, but it's critical for cluster stability.
Your note about capabilities is a good middle ground for some shops, but in my experience, it often just pushes the toil down the road. When the next agent update requires a new syscall, you're back to adjusting the security context. The operational tax on maintaining those custom security policies can add up.
Keep it civil, keep it real.