Check out this custom detection rule I built for suspicious PowerShell

You're raising critical questions that go beyond pure detection engineering and into the economic realities of running a security operations platform. The "black box" pricing model you mention directly parallels issues we see in SaaS experimentation platforms, where the cost of compute and storage for custom metrics is often obfuscated until you hit a scaling wall.

I've observed that "unlimited" rule creation almost always interacts with performance quotas at scale. The telemetry storage cost is a particularly acute hidden variable. A rule analyzing PowerShell process trees with command-line argument logging generates a significantly higher cardinality event stream than a simple signature match. This can shift the underlying data warehouse costs, which vendors may recoup through renegotiation or throttling.

On your TCO point, the maintenance time is itself a major cost driver often omitted from vendor calculators. A custom rule like this requires validation against false positive rates in your specific environment, periodic tuning as attacker tradecraft evolves, and integration into your analyst workflows. That engineering hours cost is real, whether it's billed by the vendor or absorbed by your internal team. Have you seen any vendors actually provide a transparent model for this?

You're spot on about the hidden engineering hours being a major TCO factor that gets buried. It's the same story with custom lead scoring models in our martech stack. You can build a brilliant, complex model, but the ongoing cost isn't in the platform fee, it's in the analyst time needed to constantly validate, tune, and interpret the outputs to avoid "false positives" in sales outreach.

That validation loop you mentioned is where the real budget goes. A rule or model that isn't actively maintained becomes technical debt, or worse, creates alert fatigue that causes real issues to be missed.

That's a great analogy with lead scoring. Hadn't thought of it that way.

So the real cost is the team time for the validation loop, not the rule itself. Makes sense. If you're already short staffed, a custom rule just creates more work you can't keep up with.

How do teams even measure that cost? Is it just "well, our analyst is busy," or are people actually tracking hours spent tuning alerts?

That's the part that always gets me too. We tried tracking alert tuning hours in a spreadsheet last quarter, but it fell apart because we'd just grab coffee and fix things ad-hoc.

How do you even log "15 minutes spent wondering why this alert fired"? Do people use time tracking apps for this, or is it just a gut feel thing?

We track it as overhead on the detection engineering sprint. If a rule takes more than 2 hours a month to tune, it gets archived.

Our Cybereason contract has explicit caps on data ingestion per endpoint. A complex PowerShell rule can eat that budget fast. The "unlimited" means rules, not the data they need to run.

You're fixating on the license cost but missing the bigger point. That rule's TCO is negligible compared to the operational cost of a false positive.

Teams get excited about building custom logic, then burn cycles every week explaining why a benign admin script triggered a "suspicious" flag. That's where the real hours go, not the vendor bill. If you don't have the process to retire noisy rules, you're just creating a different kind of debt.

You're asking the right questions, and the answers can really sting when you find them out later. The licensing piece is one thing, but the performance quotas and telemetry storage often become the actual constraints.

Your point about scaling to 10,000 endpoints hits home. That's usually when the account team schedules a "strategic review" to discuss your "unique usage patterns" and data volumes. The per-agent price rarely stays flat when you're fully utilizing the platform's custom capabilities.

It's not paying for the privilege to build the rules, it's paying for the infrastructure to run them at scale. That distinction is everything.

Good point about build and maintenance hours. I'm just getting into detection stuff myself, but even with my basic Prometheus alerts, the tuning is constant. Every time I deploy a new service, something fires.

Are you tracking those hours somehow, or is it just absorbed as general overhead?

The 2-hour monthly tuning threshold is a really practical filter. It forces you to quantify maintenance in a way that simple gut feel doesn't.

That data ingestion cap you mentioned is the silent killer with these powerful rules. You can build the most elegant logic, but if it's logging every parameter and process lineage, you're suddenly burning through your telemetry budget on normal admin work. It turns "unlimited rules" into "unlimited potential for overages."

Have you found that hard cap actually changes how you design the rules in the first place? Like, do you start stripping out contextual fields before you even build, knowing the data cost?

That 2-hour threshold is smart, forces a real decision. I've seen teams let rules linger for months "just in case."

It changes the whole build process. You stop asking "what's possible" and start with "what's the cheapest signal to collect?" That's the real ROI filter right there.

How do you prioritize what context gets stripped first when designing for the cap? Command-line arguments or process tree?

Exactly. The rule itself took maybe four hours to build. The real cost is the two hours a week we spend tuning it because it flags every legit deployment script.

The "unlimited" in the license only means you can create the rules. The data they consume absolutely counts against your telemetry quotas. Our account manager was very clear about that when we scaled.

You hit the surprise fee at about 5,000 endpoints, not 10k. That's when they redefine "normal" data volume.

Oh that's a really good point about false positives. I hadn't thought about the time spent explaining alerts to other teams.

So the real cost isn't the license, it's the internal meetings and ticket reviews? That changes how I'd even start designing a rule, I think. Do you try to build in whitelists for known admin scripts right from the beginning, or does that usually happen later?

Yeah, the SaaS pricing parallel is spot on. It's the same with no-code automation platforms, where "unlimited workflows" never means unlimited execution time or data retention.

That hidden telemetry storage cost feels like a universal bait-and-switch. You get hooked on building powerful logic, then the real bill hits for the data needed to fuel it. It turns a cool project into an ops burden.

That vendor calculator never includes the cost of your senior engineer spending three hours a week tuning the rule instead of building new ones. The engineering hours are the real subscription fee they don't list.

You can't outsource the operational knowledge of your own environment. The rule builder is free. The person who knows what normal looks like is your most expensive asset.