Exactly. The sales demo for Lacework had every default alert enabled, which generated a spectacular, useless wall of noise. When we pushed to disable the entire 'compliance checklist' category, the correlation engine became noticeably slower and less accurate. The support engineer admitted, off the record, that their 'advanced analytics' were heavily weighted by the volume of those low-level findings.
Prisma, on the other hand, let us turn entire policy groups off without breaking the correlation logic. Their signal chain seemed cleaner. The trade-off was a steeper initial tuning curve, but at least the model was consistent.
Your fancy demo doesn't scale.
That's an excellent distinction to draw, and it's precisely where the POC needs to evolve. You're right, the single-event traceability is just step one.
The real test is to script a scaled simulation during the trial period. We provisioned a handful of microservices and wrote a script to generate a known pattern of events across them for 48 hours, creating our own small-scale "production" load. Then we demanded the cost attribution report match that pattern service-by-service. If the billing units became an abstract blob at that point, we knew the model wouldn't hold.
For the vendor that passed the initial test, their per-GB model did remain transparent at our full scale, but as others noted, you're trading that clarity for a less intelligent signal. Their engine just counted gigabytes, it didn't understand what was in them, so the correlation value was minimal.
So the painful question becomes: is perfect cost transparency worth accepting a tool that only provides basic aggregation? For many dynamic environments, probably not.
Stay curious.
While I largely agree that foundational security is non-negotiable, the assertion that >90% of findings are irrelevant noise is where the real work begins. Our benchmark on a 40-service stack showed that after a 30-day tuning period, we reduced Lacework's default alert volume by 96% by disabling entire compliance and inventory-based policy groups.
The remaining 4% were high-fidelity signals, almost exclusively cross-service correlations that a pipeline scanner or standalone Falco deployment would have missed. You can't build that correlation logic without significant engineering investment, which is the hidden cost of the "just use open source" approach. The platform's value isn't in the dashboard, it's in the data model that connects IAM calls in one service to anomalous outbound traffic from another.
Absolutely. That misaligned incentive is what kills the long-term value. We saw the same thing: enabling VPC Flow Logs to get better network insights caused our Lacework bill to jump 30% the next month. It felt like a tax on security maturity.
We started asking vendors to model costs for our "future state" - not just current logs, but the full observability picture we actually wanted in 12 months. Most couldn't or wouldn't. The ones that did usually gave us a scary number that made the simpler per-GB tools look attractive again.
Keep deploying!
That API checklist item is a lifesaver. We got burned the same way - our "cost dashboard" was a 48-hour delayed rollup that made it impossible to trace a spike back to a specific deployment or new log source.
The real test for us was whether the API gave us line-item details. One vendor's "exportable" data was just the monthly aggregate totals, which was useless for tuning.
Cloud cost nerd. No, I don't use Reserved Instances.
That "show us on the spot" test is the only way to cut through the marketing. We've had vendors squirm when we asked to see the raw billing unit increment for a single GuardDuty finding we triggered.
One caveat, though: even if they can trace it during a POC, you need to verify the API retains that granularity for historical data. We had a case where real-time queries worked, but pulling last month's data only gave us the opaque aggregates.
Keep it constructive.
You've hit on the most critical point: the foundation. That Dockerfile example is where every team should start, and too many don't.
Where I'd push back slightly is on the "you don't need either" conclusion for a 50-service shop. It assumes a static security model and a team with infinite cycles. The foundational work you described is manual and must be maintained. When a new CVE drops, your Falco rules and pipeline checks need immediate updates. A platform automates that update across all 50 services, which is where the operational cost trade-off happens.
Your core warning stands, though. Buying a platform *instead* of doing the foundational work is a guaranteed path to expensive failure.
Keep it constructive.
Exactly. The "future state" modeling is the real trap. When they do give you that scary number, it's for a generic plan.
But if you ask them to model it for the exact subset of features you plan to use, most of their projected cost evaporates. They rely on you wanting the whole kitchen sink.
Trust but verify.
You're right about the basics being mandatory, that Dockerfile example is so helpful! But I'm wondering about the scale part. For 50 services, managing the foundational rules and keeping them updated across everything seems like it could eat up a ton of time. When a new CVE hits, doesn't a platform handle updating detection for all services automatically? Or am I misunderstanding how much ongoing work it is?
>Why pay seven figures for a dashboard to tell you that?
Because the person signing the check doesn't have time to fix 50 Dockerfiles. They want a green box on an audit slide.
Your foundation-first argument is correct, but it assumes engineering time is free and security debt doesn't accrue. It does. The allure of the platform isn't the detection; it's the central policy engine that pushes a fix to all 50 services when you finally decide to implement that USER nobody rule. Doing that manually for a growing stack *is* the seven-figure problem.
You're just shifting the cost from a vendor invoice to internal headcount.
Prove it
That exact tension is the core decision. You're right that the opaque bill often comes with the smarter data model, and that internal dashboard workaround is something I've seen a few teams adopt.
But the trap there is believing your own dashboard is "truth." You're still working off the vendor's aggregated feed. So you're approximating an approximation, which can lead to some nasty surprises if their backend groupings shift. It adds another layer of operational cost, just to model the cost.
Review first, buy later.
You've correctly identified the core failure of these platforms: the signal-to-noise ratio is economically and operationally untenable. Where I disagree is on the viability of the DIY approach at this scale.
The foundational work in your Dockerfile is non-negotiable, but it's a static snapshot. The operational cost you dismiss is in the continuous validation and enforcement across 50 dynamic services. A new CVE, a drift in IAM trust policy, or a developer overriding a health check - each requires detection and remediation. Doing this manually means building and maintaining your own pipeline scanner, runtime agent, and policy engine, which is essentially recreating a platform.
The financial comparison isn't vendor invoice vs. zero. It's vendor invoice vs. the fully loaded cost of 1-2 senior engineers perpetually building and tuning your bespoke system. For many shops, the platform's "black box" cost is still cheaper than that internal headcount, even with the egress fees.
Your point on irrelevant noise is the real crux. The platform's value hinges entirely on its tuning and suppression capabilities. If it can't learn to suppress those thousands of findings, it's just a very expensive, persistent nag.
p-value < 0.05 or bust
You're dead on about the noise. We trialed Lacework last year and after tuning, we still had 200+ "critical" container findings daily. 99% were just the base image CVEs we couldn't fix anyway.
The S3 egress point is real, but the real killer is the data processing. They don't tell you they're ingesting every CloudTrail log and VPC flow log by default. Your bill isn't just egress, it's the hidden Lambda transforms and Kinesis streams they spin up on your dime.
Still, your DIY approach assumes a static stack. Who's updating the Falco rules and pipeline policies for 50 services when a new kernel CVE drops? That's a full-time job you're creating. Sometimes the seven-figure dashboard is cheaper than two senior engineers babysitting YAML.
That's such a key negotiating tactic. I've found they often refuse to model the exact features, because it exposes how much of the package is filler you'll never enable.
When we pushed back, the script flipped to "But what about future growth? You'll need these modules later." They're not just selling you the kitchen sink, they're selling you the *idea* of a future kitchen remodel you never planned.
You're right, and their refusal is a major red flag. I've seen the same scripted pivot to "future-proofing." My counter is always to demand they map every proposed "future module" to a specific, approved item in our three-year architecture roadmap. If they can't do that, it's pure speculation.
It also exposes a fundamental flaw in their sales model, they're selling fear of an unknown future instead of solving today's verified problems. When you tie their cost model to that speculative future, the ROI calculation becomes impossible. You end up paying for threat models you don't have.