I've been conducting a detailed analysis of our security stack's TCO, with a particular focus on our iboss cloud gateway deployment. Having now modeled costs out to projected growth over the next three years, I've arrived at a concerning conclusion: iboss's pricing appears to be deliberately structured in a way that becomes disproportionately, and perhaps unfairly, expensive as you scale user count and data volume. I'm posting here to see if my findings align with the experience of other large-scale adopters or if my architecture team has fundamentally misunderstood the licensing model.
Our initial deployment for 5,000 users was straightforward—a per-user, per-year subscription. The pain point emerged during our annual true-up and expansion to 10,000 users. We encountered not just linear cost doubling, but several multiplicative factors:
* **Data Processing Fees:** The per-user cost doesn't cap data consumption. Our research & engineering teams, who routinely transfer large datasets, trigger "cloud data processing" fees that are opaque and billed separately. Our monthly bill shows line items like `Extended Cloud Security - Data Volume Overage` which are impossible to forecast accurately.
* **Feature Module Silos:** Scaling required "advanced" threat protection and DLP, which are not included in the base per-user fee. These are licensed as separate add-on modules, again at a per-user rate. The effective cost per fully-featured user is thus: `Base User Fee + (Module A Fee * User Count) + (Module B Fee * User Count)`. This creates a deceptively low entry point that balloons.
* **Lack of Enterprise Discount Tiers:** Unlike other platforms (e.g., AWS, traditional firewalls) where unit cost decreases with committed volume, iboss's pricing schedule we were presented with remained stubbornly linear. There is no incentive for large-scale commitment beyond a marginal discount, which is negated by the data overage risk.
I've built a simplified model to illustrate the divergence from a pure linear model. Assume a base fee of `$X/user/year`, and two add-on modules at `$Y` and `$Z` per user.
```python
# Simplified Python to illustrate cost scaling
def calculate_iboss_cost(base_rate, module_rates, users, data_overage_factor=1.0):
"""Calculates total cost. data_overage_factor is a multiplier >= 1.0 representing unexpected data fees."""
effective_per_user = base_rate + sum(module_rates)
base_cost = effective_per_user * users
total_cost = base_cost * data_overage_factor
return total_cost
# Scenario: Growing from 1k to 10k users
users = [1000, 5000, 10000]
base_rate = 50
module_rates = [15, 10] # Two common add-ons
for u in users:
# Assume overage factor increases with scale due to unpredictable data fees: 1.0, 1.1, 1.25
overage = 1.0 + (u / 10000) * 0.25
cost = calculate_iboss_cost(base_rate, module_rates, u, overage)
cost_per_user = cost / u
print(f"Users: {u:5d} | Total Cost: ${cost:,.0f} | Cost per User: ${cost_per_user:.2f}")
```
The output would show the *cost per user* actually increasing with scale in this model, which is counter-intuitive and punitive. In practice, our data overage factor is not a smooth function but a step-change based on opaque thresholds.
My core question to the community is this: have others successfully negotiated a pricing structure that includes **hard caps on data processing fees** or **true enterprise tiers** with declining unit costs? Or has the move to a per-user, usage-sensitive model simply made large-scale, data-intensive deployments financially untenable compared to alternative architectures (e.g., on-prem proxy clusters combined with cloud-native CASBs)? I am particularly interested in reproducible benchmarks or contract clauses that have mitigated this. The lack of predictable billing at scale is a significant architectural and business risk.
Trust but verify.
Oh, the `Extended Cloud Security - Data Volume Overage` line is a familiar ghost in the bill. It's not just you.
We saw a similar curve with another vendor who used a clean per-user fee for the core service, then attached nebulous, usage-based add-ons for anything that actually scaled - like data scanning or API calls. It makes forecasting a nightmare. You end up having to police internal teams' data usage, which feels like you're optimizing for cost avoidance, not security posture.
Have you looked at whether their "Enterprise Agreement" tier actually flattens this, or does it just move the overage goalposts? Sometimes the premium tier just includes a data pool that you can still exceed.
Still looking for the perfect one
Your observation about the multiplicative factors is precisely where the model shifts from predictable to punitive. The core issue isn't the per-user fee, but the decoupling of that fee from the actual resource consumption metrics that scale independently - namely data egress and processing cycles.
In my experience, this creates a perverse incentive to limit security efficacy. You mentioned your research teams transferring large datasets. Under this model, fully enabling DLP or advanced threat scanning on those large data flows becomes a direct and significant cost liability, not just a security decision. The `Extended Cloud Security - Data Volume Overage` is essentially a tax on using the product's deeper features at scale.
This architectural mismatch is common. You're buying a user-based license for a product that incurs infrastructure-based costs (compute, bandwidth) on the vendor's side. Their solution is to offload that variable cost back onto you through opaque surcharges, making true-up periods a financial surprise rather than an administrative exercise. Have you attempted to model the cost per gigabyte processed, back-calculated from your overage invoices? That figure is often startling when compared to standard cloud data transfer or compute pricing.
—BJ
You're right that moving to an Enterprise Agreement often just relocates the problem. We negotiated a flat-fee EA with iboss two years ago, predicated on a generous data pool. The initial forecast looked solid, but our forensic logging and full-content inspection features caused us to exceed the pooled data volume by 40% in the first quarter. The overage rates, while discounted, still applied and were significant.
This creates the exact operational burden you mentioned. We had to implement internal chargeback reporting and thresholds, which shifted our team's focus from threat detection to consumption monitoring. The billing model effectively penalizes the use of the advanced security features you're ostensibly paying for in the premium tier.
Data over dogma
That point about policing internal usage hits home. We had to do the same thing, and it creates real friction with the research and development teams who just see it as us blocking their work.
You asked if the Enterprise Agreement flattens it. In our case, it didn't. It just changed the argument from per-unit overage to quarterly true-ups based on that pooled data volume. The goalposts absolutely moved, and the forecast was never right. You start making decisions about what traffic to inspect based on the bill, which feels like a security failure in the making.
Data is sacred.
Yep, that's the worst part. You're not optimizing for security, you're optimizing for the vendor's revenue model. It puts your team in a position where you have to justify security spending against other departments' budgets.
We saw the same thing trying to roll out full TLS inspection to our engineering clusters. The data volume cost projection made it a non-starter, so we had to create a whitelist. Defeats the whole purpose.
Has anyone successfully pushed back on this in contract negotiations, like getting them to tie overage rates to a percentage of the base commit?
Always optimizing.
Pushing back on the overage structure is tough, but you can make some headway. We didn't get them to tie it to a percentage of the base, but we did get them to agree to a predictable cap. We negotiated a fixed overage rate for the contract term, regardless of how many times we exceeded the pool. It's not perfect, but at least the finance team can model the worst-case scenario now.
For us, the real win was forcing them to clarify exactly what increments constitute "data volume." That alone saved us from some nasty surprises.
Trust the trial period.
The clarification of data volume increments is a critical, and often overlooked, negotiation point. In our last audit, we discovered that their default calculation included protocol overhead and retransmitted packets in the "scanned" volume, which artificially inflated the metered usage by nearly 18%. Getting that defined in the contract annex was essential.
While a fixed overage rate cap provides predictability, it doesn't address the core incentive problem. You're still financially penalized for enabling the security features you bought the platform for. We've found that the only effective counterweight is maintaining a viable technical alternative, like a proof-of-concept for ZTNA or SSE, to introduce genuine negotiation leverage. Without that, you're just haggling over the terms of the penalty.
infra nerd, cost hawk
Your analysis correctly identifies the non-linear cost scaling, particularly with the multiplicative factors. The jump from 5,000 to 10,000 users is the precise inflection point where the decoupling of user count from resource consumption becomes financially material.
You mentioned the impossibility of forecasting the `Extended Cloud Security - Data Volume Overage`. This opacity is structural. The per-user fee creates an illusion of predictability, but the variable costs are tied to the very metrics you can't control in a growing business, like data egress volume from research teams. Your three-year TCO model is likely underestimating the impact if it assumes current data transfer patterns remain static; they rarely do.
This forces an architectural decision you didn't sign up for: you must now model and constrain data flow as a primary cost variable, not a byproduct of business activity. Have you attempted to correlate those overage line items directly to specific business units or data pipelines to quantify the security tax on their operations?
Data doesn't lie, but folks sometimes do.
Spot on about the architectural decision. The moment you start diagramming data flow for finance instead of security, you've lost.
>quantify the security tax
Tried that. Presented a report showing marketing's video uploads cost more in DLP overages than their entire software budget. Guess which activity got restricted first? Hint: it wasn't the ad spend.
These models don't just penalize scale, they actively incentivize weaker security. You buy the premium features, then can't afford to turn them on.
CRM is a means, not an end.
You've hit on the exact issue that makes long-term planning so difficult. The "illusion of predictability" from the per-user fee is real.
My team ran into the same forecasting black hole with those data processing fees. We had to build our own internal shadow billing system just to try and predict the monthly `Extended Cloud Security` line item, which feels backwards. Even then, a surprise project from our data science team could blow the model up.
It forces you into a corner where you're not buying security capability, you're buying a metered allowance.
✌️
Building a shadow billing system is the logical endpoint of this pricing model, and it's a massive, unrecoverable overhead. We did the same for forecasting, but then used the data to prove a point.
Our internal tracking showed that the top 2% of users (mostly in R&D moving large datasets) generated over 60% of the metered data volume. That single data point shifted the conversation from a vague "high usage" problem to a specific architectural one. It allowed us to justify a segregated, non-inspected network path for that traffic, which cut the overage fees dramatically but is, as others have noted, a security compromise.
The "metered allowance" framing is correct. You're not procuring a security control; you're pre-paying for a resource quota, with security as a conditional feature.
Your analysis isn't wrong, but you're still thinking like a customer trying to understand a billing sheet, not an architect seeing the trap. That "impossible to forecast" line item is the entire point.
The jump from 5k to 10k users isn't just a cost inflection point. It's the moment your traffic patterns become heterogeneous enough that you can't model them as an "average user" anymore. The per-user fee is a clever anchor that makes finance think it's a simple SaaS subscription, while the data processing fees are where they capture the real cost of your business's unique shape, which you can't control.
You'll exhaust yourself trying to model the three-year TCO. The model they sold you on is invalid the second you onboard a team that doesn't fit the "office worker" profile. Have you started getting pressure from leadership to "optimize" those research data transfers yet? Because you will.
monoliths are not evil
You're describing a measurable, perverse incentive, and it's worse than just "weaker security." It creates a measurable, internal security debt. That DLP overage report you built is now a quantifiable metric for *not* inspecting traffic. The finance team, by choosing to restrict the video uploads, has formally assigned a dollar value to the risk of a data leak from that channel. That's a policy decision made on cost, not risk, but it's now enshrined in your operational data.
The next time there's an incident, you'll have a perfect audit trail showing security was degraded for a known cost saving. That shifts liability in a way most companies aren't prepared to handle.
p-value < 0.05 or bust
That's a chilling point I hadn't considered. It formalizes the trade-off, doesn't it? That internal report becomes a liability record.
So when finance acts on it, they're not just cutting a cost, they're making an official risk acceptance decision. Without realizing it, maybe. Does your infosec team have a process to formally document these cost-vs-risk choices?