We often discuss the licensing cost of SAST tools, but the operational overhead—the compute resources and engineering time consumed during a scan—is a significant, recurring line item that directly impacts your cloud bill. For a project of approximately 500,000 lines of code (LOC), what is the actual resource footprint of a full Checkmarx SAST scan?
Based on analysis across several client environments, a full scan at this scale typically requires the following infrastructure for the engine itself, excluding the central management server:
* **Compute:** A dedicated machine or container with 4 to 8 vCPUs and 8 to 16 GB of RAM. The scan is CPU-intensive during the analysis phase.
* **Time:** Scan duration can range from 20 to 90 minutes. The variance depends heavily on code complexity, the number of rules enabled, and the underlying hardware (CPU clock speed, storage I/O).
* **Storage:** Temporary workspace needs can be 2-3x the size of the source code. For 500k LOC, allocate 500 MB to 1 GB of fast local storage.
The critical cost multiplier is concurrency. If you have 10 development teams needing to scan pull requests daily, you cannot simply multiply the above. You must provision a scalable pool of engines. A common configuration using Kubernetes might look like this:
```yaml
apiVersion: apps/v1
kind: Deployment
spec:
replicas: 3
template:
spec:
containers:
- name: checkmarx-engine
resources:
requests:
memory: "12Gi"
cpu: "4"
limits:
memory: "16Gi"
cpu: "6"
```
This translates to a sustained resource reservation of 12 vCPUs and 36 GB of RAM, plus management node costs. If scans are run on-demand in a cloud environment, this becomes a direct compute cost (e.g., `c5.xlarge` or `c5.2xlarge` instances). The engineering time to maintain this cluster, handle queuing, and troubleshoot failed scans adds further operational tax.
My primary question for the community is about optimization: What strategies have you found effective to reduce this overhead without compromising security posture? Specifically:
* **Incremental/Delta Scans:** How reliable are they for PRs, and what is the actual reduction in scan time and resource use?
* **Scheduling:** Are you running full scans during off-peak hours to leverage lower spot instance or compute reservation pricing?
* **Rule Tuning:** Has aggressively disabling low-severity or contextually irrelevant rules for your project yielded measurable performance gains?
The goal is to move from viewing SAST as a fixed cost to managing it as a variable, optimized operational expense.
Less spend, more headroom.
Your numbers are a solid baseline, but they miss the critical variable of incremental analysis. Modern SAST engines don't always perform a full parse on every scan. If the Checkmarx engine is configured for incremental/delta scans on pull requests, the resource footprint for a typical PR scan can drop by 80-90% compared to your full-scan figures. However, that assumes a clean, recent baseline scan exists; if the incremental state is corrupted or missing, it falls back to a full scan, causing a massive, unpredictable spike in that pipeline's resource consumption. This inconsistency makes cloud cost forecasting difficult.
Also, your point on concurrency is where the real bottleneck forms. It's not just 10 teams needing scans, it's 10 teams potentially triggering scans at the same time post-standup. If your scanning infrastructure uses a fixed pool of VMs, you'll see queueing delays that effectively increase "engineering time consumed," which you mentioned. The operational cost then shifts from pure compute to delayed feedback loops.
What's your source for the 2-3x storage multiplier? In my tests with a similar-sized Java codebase, the temporary workspace for Checkmarx was closer to 5x the source size during the intermediate AST representation phase. That could push temporary storage needs for 500k LOC toward 2-3 GB, which matters if you're using fast, provisioned SSD storage in the cloud.
numbers don't lie