As a cost analyst, my primary lens for evaluating any tool is its total operational expenditure, particularly within complex, resource-intensive environments. I’ve been tasked with providing a financial and operational assessment of our current application security tooling, and Checkmarx is a candidate for our primary code scanning solution. However, our codebase is a single, large monorepo housing over 200 microservices and libraries, with a combined history exceeding five years.
The marketing materials and sales engineering demos are, unsurprisingly, optimized for greenfield repositories. I am seeking concrete, production-tested insights from teams operating at a similar scale. My preliminary analysis raises several cost and performance concerns that standard pilots fail to capture:
* **Scan Initialization & Incremental Analysis:** How does the engine handle the first full scan of a massive codebase? Is the time-to-first-result measured in hours or days? More critically, for subsequent scans, does the incremental analysis truly function as advertised, or do merge conflicts and deep histories force frequent near-full re-scans, consuming excessive compute cycles?
* **Infrastructure & Hidden Operational Costs:**
* For the self-managed (on-premises) deployment, what are the true resource requirements for the management console and scan engines? I'm particularly interested in sustained CPU/memory consumption under load, not the minimum specs for a proof-of-concept.
* For the SaaS offering, is the pricing model based on lines of code, scans, or concurrent users? Have you encountered steep nonlinear cost increases as the monorepo grew? Are there data egress or storage fees for retaining historical scan data?
* **Pipeline Integration Overhead:** In a monorepo with high commit frequency, can the scanner be integrated into CI gateways for pull requests without becoming the critical path bottleneck? What is the average scan latency for a typical diff, and what compute resources (e.g., powerful CI runners) are required to keep it under, say, ten minutes?
* **Noise-to-Signal Ratio & Triage Costs:** The financial impact isn't just license fees; it's the engineering hours spent triaging findings. In a large, polyglot monorepo, how effective is the out-of-the-box configuration at suppressing false positives or irrelevant issues in legacy components? Have you had to invest heavily in creating and maintaining custom query filters or rulesets?
My objective is to build a realistic TCO model that includes not just the licensing line item, but the associated cloud/compute costs, storage, and the often-overlooked productivity tax on development teams. Any data points on your scaling journey, performance tuning, or unexpected cost drivers would be invaluable.
-- Liam
Always check the data transfer costs.
Good to see a focus on operational costs from the start - that's often where the real pain points emerge.
On your first question about scan initialization and incremental analysis: in my experience with large legacy repos, the initial full scan is a multi-day event that requires significant resource planning. The incremental scans *can* work well for routine commits, but you're right to be skeptical. Major merges or refactoring across shared libraries often triggered what felt like partial re-scans that still took hours. The compute cost of those "in-between" scans added up.
I'd suggest asking them for concrete metrics on scan time variance based on change type, not just best-case scenarios. The delta between scanning a single service change versus a core library update is massive.
Stay factual, stay helpful.
Multi-day initial scans? Sounds about right. The real joke is the "partial re-scans" you mentioned. With a monorepo, touching a shared lib can light up the whole dependency graph. Their "incremental" logic often fails to grasp that, and you end up paying for a 70% scan anyway. Asking for variance metrics is smart, but good luck getting a straight answer. Their data tends to vanish when the sales cycle ends. 😏
Just my two cents.
Your focus on the initial scan is spot on. In a monorepo that size, the setup and first full scan isn't just a long runtime - it's a major infrastructure project. You need to plan for dedicated, high-memory nodes, and even then, I've seen the engine struggle with the sheer number of dependency resolution paths.
The bigger hidden cost, though, is the incremental promise. When you update a shared library, it often invalidates the cache for everything downstream. You're not just scanning that lib; you're paying for the re-scan of all 200 services that *might* be affected. The variance between a simple service commit and a core library change can be 100x in compute time, and that's where your OpEx gets unpredictable.
✌️
For the initial scan, days is the correct unit. We had to throw a 64-core box with 256GB RAM at it for a week straight, and it still choked on some of the older dependency graphs. Your skepticism about the incremental analysis is the key.
The compute cost comes from the "near-full re-scans" you're worried about. It's not just merge conflicts, any change to a foundational package triggers a domino effect. Their engine claims to understand your monorepo structure, but in practice, it re-scans anything with a transitive dependency on the changed file. With 200 services, that's most of them.
So you're not paying for incremental scans. You're paying for 80% rescans on a regular basis, and the sales deck never mentions that the incremental toggle is basically a suggestion to the engine. Your OpEx will be a rollercoaster tied directly to how often your platform team updates the shared libs.
null
Wow, that's a really sobering point about the cache invalidation. When you say the variance can be 100x, does that mean your CI/CD times become completely unpredictable? That sounds like a nightmare for planning sprints.
So the "incremental" feature is sort of a best-effort thing, not a guarantee? That seems like a huge distinction they gloss over. I'm curious, has anyone found a way to structure the monorepo or configure the tool to actually limit that cascading re-scan effect, or is it just a fundamental limitation?
The hidden cost isn't just compute time. It's engineering morale. When a shared library update blows the CI pipeline for half a day because the security scan snowballs, you're not just paying for cloud resources. You're paying developers to sit and wait, or worse, to find ways to skip the scan to hit a deadline. That's a real risk.
Trust, but audit.
Nail on the head. That morale tax is real and compounds. We tracked pipeline stall times for a quarter, and the biggest driver of dev complaints wasn't the 12-hour initial scan - it was the unpredictable 4-hour "incremental" scan that blocked a hotfix.
Once people start looking for ways to bypass the scan, you've lost the security value entirely. You're then paying for the tool, the compute, and the risk.
Data doesn't lie, but dashboards sometimes do.
Absolutely spot on with focusing on the operational costs beyond the pilot. The pilot is just the first chapter of a very long and expensive book.
> does the incremental analysis truly function as advertised
In my setup, the answer is a firm no, and that's where the cost ballooned. The engine's dependency graph resolution for a monorepo isn't granular enough. We saw that a version bump in a low-level logging utility would trigger a rescan on 80% of our services, because everything transitively depended on it. Our "incremental" scans were consuming nearly the same resources as a full scan, multiple times a week.
This forced us to build a complex pre-scan filtering layer using the API to essentially manually define scan scope based on changed files, which then became its own maintenance burden. You're not just buying a scanner; you're buying the platform to build your own scanner logic on top of it.
null
Exactly. Tracking the stall times is the only way to get real data, because everyone handwaves it as "engineering overhead".
That bypass behavior you observed is predictable. Once scans exceed a certain pain threshold, teams will engineer around them. I've seen it manifest as:
* Creating micro-PRs to stay under file change limits
* Splitting libraries out of the monorepo just to avoid the scanner
* Flagging builds as "security reviewed" with a rubber stamp
You end up with a shadow workflow that defeats the entire point of the tool. The cost isn't just the stalled pipeline, it's the institutionalized risk.
Your fancy demo doesn't scale.
You've hit on the core of it. The compute cycles for those frequent near-full rescans are just the start of the OpEx. The real budget killer is the engineering time to manage the unpredictability.
When a scan snowballs, it's not a passive cost. Someone's paged to scale up runners, someone else is debugging why the cache invalidated, and a whole team is blocked from deploying. That's three separate labor costs on top of the cloud bill for the oversized VM.
Your point about standard pilots failing to capture this is key. They'll scan a snapshot of one service, not simulate a week of commits across a shared library. You need to explicitly test that scenario, because the variance is where the financial risk lives.
ship it
Yeah, the pilot being a snapshot is the real trap. If they can't simulate a core library update during the eval, you won't see the cost explosion until it's too late.
I'm just getting into this, so maybe this is naive, but what did you ask for in your test? A demo scanning one service is useless. Did you force them to show a shared lib change and measure what really got rescanned?
Containers are magic, but I want to know how the magic works.
Yes, we made that exact test the central part of our evaluation. We provided a snapshot of the monorepo, then gave them two sequential commits: one to a leaf service, and one to a core utilities library. We required them to show the scan logs and resource utilization for each, comparing the claimed "incremental" scope against the actual files processed.
The result was revealing. The leaf service change triggered a scan of about 50 modules, which was acceptable. The core library change, however, invalidated the cache for over 300 modules. Their dashboard still labeled it an "incremental scan," but the engine had effectively done a full rescan. That's the data point you need to demand: the transitive dependency impact map for a change at the center of your dependency graph. Without it, you're evaluating a different product than the one you'll run.
Plan the exit before entry.
Wow, 200 microservices in one repo? I can't even imagine the first scan time. That must take forever.
This is a bit basic, but how do you even measure the initial scan OpEx? Is it just the compute cost for the duration, or do you factor in the delay for the security team getting those first results? A day of delay feels like a cost too.
You've correctly identified the two critical failure modes, but you're missing the third leg of the stool: false positive triage overhead. Even if you solve for initial and incremental scan time, the sheer volume of findings from a 5-year monorepo with 200 services will be staggering. The operational cost of manually reviewing and suppressing thousands of duplicate or irrelevant issues across hundreds of projects is a massive, recurring time sink that scales directly with your repo size. Your financial assessment needs a line item for security engineer hours per quarter dedicated solely to managing the noise from this tool.
FinOps first, hype last