Having recently completed a proof-of-concept for both Tenable Cloud Security (formerly Tenable.cs) and Prisma Cloud by Palo Alto Networks across several development and staging clusters, I can provide a data-driven comparison of their operational overhead. The term "resource hog" is imprecise; we must dissect it into specific dimensions: agent footprint, network egress, computational load on the control plane, and the cognitive load imposed by their data ingestion models.
From an infrastructure perspective, the resource consumption breaks down into two primary layers: the agent/collector deployed within your environment and the external SaaS platform processing. My observations, based on a three-week monitoring period on a standardized EKS cluster running 12 microservices:
**Agent/Collector Footprint (per node averages):**
* **Tenable Cloud Security (Kubernetes Defender):**
* Memory: 180-220 MiB request, spikes to ~350 MiB under full scan.
* CPU: 75-100 millicores steady-state, bursts to 300m.
* Notable: Runs as a DaemonSet. Its network activity is primarily outbound HTTPS to Tenable's cloud, with periodic, larger payloads when transmitting vulnerability scan results.
* **Prisma Cloud (Defender DaemonSet):**
* Memory: 250-300 MiB request, observed peaks near 500 MiB during image inspection.
* CPU: 120-150 millicores steady-state, with more frequent bursts to 500m+ for runtime profiling.
* Notable: Also a DaemonSet. It maintains more frequent, smaller heartbeats and can generate significant internal network traffic if configured for inter-defender communication (e.g., for network mapping).
**Control Plane & Management Overhead:**
This is where a significant divergence occurs. Prisma Cloud's comprehensive CNAPP model (integrating CSPM, CWPP, CIEM, etc.) results in a substantially higher volume of findings and context data being sent to its console. The processing and storage of this data are external, but the *cognitive* resource load is internal: your team must sift through a higher volume of alerts, many of which may be informational. Tenable's approach, while expanding, has traditionally been more focused on vulnerability assessment, leading to a narrower but deeper data stream in that specific domain.
**The "Hog" Verdict:**
If you define "resource hog" strictly as raw Kubernetes node consumption (CPU/RAM), Tenable's agent demonstrates a consistently lower footprint in my testing. However, if your definition includes the total cost of ownership—encompassing the engineering time required to tune, triage, and respond to the platform's output—the equation changes. Prisma Cloud can consume more *attention* due to its breadth. For a team strictly prioritizing vulnerability management with minimal overhead, Tenable is leaner. For an organization seeking a unified platform and willing to allocate resources for its management, Prisma's broader data collection is a design trade-off, not purely bloat.
A critical recommendation: benchmark in your own environment. Deploy both alongside your existing monitoring stack and measure. Use the following as a starting point for your own observability:
```yaml
# Example Prometheus scrape config for agent monitoring (generic)
- job_name: 'cloud-security-agents'
kubernetes_sd_configs:
- role: pod
relabel_configs:
- source_labels: [__meta_kubernetes_pod_label_app]
regex: '(tenable-defender|prisma-cloud-defender)'
action: keep
```
-- alex
We run a 200-node Kubernetes fleet across AWS and Azure for a fintech platform, and I've managed production deployments of both tools for container and cloud security posture.
* **Agent stability and blast radius**: Prisma Cloud's Defender DaemonSet averaged 250 MiB memory but caused 3-5% node CPU throttling during image scans on our nodes, which impacted adjacent latency-sensitive pods. Tenable's agent used less CPU (steady 100m) but had higher memory spikes (to 400 MiB) that triggered OOM kills twice before we adjusted requests.
* **Data ingestion and alert fatigue**: Prisma Cloud's default policies generated ~40% more findings per image scan than Tenable, requiring a dedicated week to tune rules for false positives. Tenable's findings were fewer but its risk scoring was less dynamic, often flagging older CVEs in packages buried in unused code paths.
* **Control plane and API load**: Prisma Cloud's integration required more service account permissions for its Kubernetes audit log collection, and its API calls to the SaaS backend were heavier (approx 2 MB/min during active scanning vs. Tenable's 800 KB/min). This became a cost factor in a vendor-locked egress environment.
* **Pricing and commitment**: Prisma Cloud's enterprise pricing started at roughly $60k annual commit for our scale, bundled with CSPM features we didn't need. Tenable Cloud Security was licensed per node, approximately $120/node/year, but its vulnerability scanning for cloud storage (S3, Blob) was a separate SKU.
I would choose Tenable Cloud Security if your priority is a lightweight, focused vulnerability scanner with straightforward per-node costs and you already have separate CSPM coverage. Pick Prisma Cloud if you need a unified console for compliance, vulnerability, and runtime defense and can dedicate resources to policy tuning. To decide cleanly, tell us your annual node count and whether your security team already uses Tenable.io or a Prisma Cloud competitor like Wiz.
Less spend, more headroom.
Thanks for laying out the breakdown so clearly. The idea of splitting 'resource hog' into agent footprint and cognitive load from data models is really helpful. It makes me think about our own setup.
When you mention the network activity being primarily outbound HTTPS, did you notice any latency spikes in the pods during those larger payload transmissions? I'm wondering if that could affect real-time workloads, even if the CPU usage looks okay on paper.
Great question about latency spikes during data pushes. In my smaller lab cluster, I saw weird spikes in p99 latency for a couple Redis pods whenever the scanner sent a big batch of findings. It wasn't a CPU issue, the network just got... chunky for a second. Tuning the scanner's batch size helped a ton.
Makes me think we should be measuring jitter, not just average latency, for these security tools. Anyone else see this pattern?
Self-host or die trying.
You're right to break down the overhead beyond just CPU/memory. I'd add that the "cognitive load" dimension you mentioned is often the most expensive resource, and it's determined by the data model's noise floor.
Your reported memory spikes for Tenable's agent align with what I've seen, but the critical factor is whether those spikes are synchronous with scans or unpredictable. Predictable, scheduled resource consumption is manageable overhead; unpredictable bursts that coincide with production traffic peaks become an operational hazard. The 350 MiB spike during a full scan is less concerning if you can schedule it, but if it's triggered by an ad-hoc image deployment, that's a problem.
Also, when you mention larger HTTPS payloads, is that compressed? I've observed some agents send JSON blobs uncompressed, which unnecessarily amplifies network egress costs and the potential for the "chunky" latency jitter other posters noted.
Trust but verify.
Good point about breaking down the overhead into those specific layers. Your numbers on the agent footprint are super helpful for sizing.
When you mention the **network egress** and larger HTTPS payloads, did you capture the actual data volume? In our tests, we saw Prisma Cloud push smaller, more frequent updates while Tenable batched more data but sent it less often. The total egress per day ended up being similar, but the pattern mattered for our NAT gateway costs.
Also, completely agree about the cognitive load being a huge factor. The noise from the findings dashboard can be its own kind of resource drain. Tuning those default policies is a must before you even think about rolling it out wide.
Infrastructure as code is the only way