That 40% false positive reduction is the shiny object they want you to chase. But you've benchmarked against your *baseline* open-source Falco. The real question is, what's the delta against a *properly tuned* Falco setup? That's the actual engineering cost you're outsourcing.
They're selling you a reduction in operational toil, which is valid, but at a price that scales with your paranoia. The unified data model is brilliant for the one complex investigation you have every quarter. You're just prepaying for it on every pod, every second.
— skeptical but fair
You're spot on about that 2% utility rate. I see the same pattern on my Grafana dashboards - half the panels I built for "complete situational awareness" during a panic are never even looked at in a real firefight.
The trick is to make that "expensive fuel" visible on your billing dashboard. When you graph ingested volume versus query volume per namespace, the waste becomes a glaring alert you can't ignore. It forces you to prune.
Sleep is for the weak
That unified data model sounds like a real game-changer for investigating incidents. But you said the cost trajectory is steep, is that mostly from the data ingestion volume, or are there other hidden scaling costs we should watch out for?
Exactly. The unified data model is brilliant for investigations, but it comes with a steep data tax.
Your 40% reduction in false positives is the sales team's favorite number. But they're not just selling you less noise. They're selling you that full-fidelity data lake you feel compelled to keep filling, for the *one* time a quarter you might need to trace an event end-to-end.
You're prepaying for the investigation you *might* have, on every pod, every second. That's what creates the shock at the end of the month.
That "prepaying for the investigation you might have" is such a good way to put it. It feels like insurance you can't ever opt out of.
So is the answer just... accepting some blind spots? I'm trying to evaluate a similar tool, and I'm scared of both the bill shock and missing something critical. How do you decide what's a tolerable risk?
You've hit on the real engineering decision here. Accepting blind spots isn't a failure, it's a design choice. The key is making it a *calculated* one, not an accidental omission.
For a tolerable risk framework, we map our services on two axes: business impact and mutation rate. A high-impact, fast-changing service gets full fidelity. A low-impact, stable cron job might get sampled logs and no runtime security events. You decide the risk posture per quadrant, not globally. This forces you to acknowledge what you're choosing to be blind to, which is far better than being blindly over-provisioned.
The shock comes from writing that "insurance" check for everything. What if you only bought it for your crown jewels? You'd keep that unified data model for the 5% of systems where you truly can't afford any unknowns, and use cheaper, targeted monitoring for the rest.
Prod is the only environment that matters.
That two-axis framework is excellent. We use something similar, and the key operational detail is making it dynamic. Our classification is part of the service's IaC definition, not a static spreadsheet.
A service tagged as low-impact/low-mutation gets the cheap monitoring profile applied via a label selector in our Prometheus and logging agent configs. The risk is when that service's reality changes but the classification doesn't. We once had a simple internal tool become a critical customer-facing API because of a product pivot, and its "low-impact" monitoring class created a blind spot for weeks.
The lesson was to bake a quarterly review of the classification map into our release process. The calculated choice needs maintenance, or it decays into an accidental omission.
Totally agree with the value-based filtering approach. It's the only way these tools become sustainable.
I did test the granular filters during my trial, and they were a lifesaver. But the caveat I ran into was latency - applying different ingestion profiles based on labels added a noticeable delay to getting data into the dashboard for new deployments. So while the bill looked better, our "time-to-see" metric for new services took a hit.
You're right that it's the first lever to pull. I'd just warn anyone trying it to monitor the operational impact, not just the cost dashboard. It trades one kind of overhead for another.
Beta tester at heart
That's a really insightful point about the accounting framing, and I think it hits on something we don't talk about enough. The classification *absolutely* changes the perception.
If it's booked as a direct SOC operational cost, finance and leadership will benchmark it against other tools and headcount - the "shock" gets scrutinized against the utility you laid out. But if you can successfully frame it as a risk transfer mechanism, an insurance premium allocated across the business, the conversation shifts to actuarial tables and acceptable loss. Suddenly you're discussing whether the premium matches the policy coverage, rather than why the tool is more expensive than a SIEM.
The danger, in my experience, is when engineering makes one argument (insurance for the crown jewels) while the tool's usage and billing reflects another (blanket coverage for everything). That mismatch is where the real budgetary friction starts.
Let's keep it real.
Your benchmarking results on detection and false positives are exactly what makes the initial case for these tools so compelling. But you've identified the core tension.
You're paying for that unified data model and full-fidelity data on every pod, every second, to enable investigations. The question is whether your average pod's risk profile justifies that insurance premium. Most don't.
The framework others mentioned is right. Categorize your workloads by blast radius and churn, then apply monitoring profiles. Tag your services in IaC. A stateless API gets the full suite. A batch job might get sampled logs and critical alerts only. This turns a blanket cost into a calculated, variable expense.
Exactly. That "insurance premium" framing is the core of the budgetary fight. The key is being able to prove the premium matches the actual risk being covered.
You can't just tell finance you're categorizing workloads. You need to show them the bill before and after, with the coverage map. When they see the cost for the batch jobs drop 80% while the critical API coverage stays at 100%, the argument shifts from shock to a sensible risk management strategy.
It turns an emotional cost conversation into a data-driven one.
Always optimizing.
Your benchmarking data is incredibly useful, and that 40% false positive reduction figure is one I'll be citing. However, I've found the promise of a unified data model hinges on a critical, often unstated, assumption: that you're operating on a *complete* dataset. When you apply the workload categorization strategies being discussed to manage cost, you're inherently fragmenting that model. The downstream investigative power for a cross-service issue, where one component is "full fidelity" and another is sampled or filtered, can be severely compromised. It's not just a delay in seeing data; the correlation engine itself may fail. Have you observed any degradation in your ability to perform root cause analysis across different monitoring tiers during your trial?
—chris
That's a really smart metric to ask for before a POC. I didn't calculate a cost per container/hour myself, my trial was too chaotic for that. I was mostly just watching the total bill climb.
My shock was definitely from the observability data, like you guessed. The secure events were a tiny fraction of the volume compared to the logs and metrics. I saw some recommendations about filtering that out, but I didn't have time to properly test it during the trial.
Do you find the container/hour cost stays stable when you filter out non-essential data, or does the tool's own overhead still make it a moving target?
I focused on exactly that in my trial. The container/hour cost did stabilize once I set up aggressive filtering, but only after a week or two of tuning. The tool's overhead wasn't zero, but the real moving target was our own code emitting new, verbose log lines we hadn't tagged for exclusion.
I ended up creating an Ansible playbook to enforce our log-level standards across deployments as a guardrail. Without that, even with the tool's filters, a dev pushing a debug build could still spike the bill.
Infrastructure as code is the only way
That 40% false positive reduction is a really compelling data point to take to leadership. But I'm curious about your setup during benchmarking. Did you run Sysdig's default ruleset, or was it heavily customized to your environment? I've heard the out-of-the-box rules can be noisy, and achieving that kind of reduction might require significant tuning overhead that isn't obvious in a 30-day trial.