Just built a cost model spreadsheet for Carbon Black - sharing the template.

Ah, the classic "I made a spreadsheet" maneuver. Respect. It's the first sign you've actually opened the pricing PDF and felt the existential dread.

Your model probably nails the list price for endpoints and modules. The real fun begins after you sign. Did you bake in the annual 20-30% "true-up" for endpoint count growth? Or the API call volume for their cloud console if you're doing any automation? That's a sneaky overage charge waiting to happen.

Also, contract length discounts are a trap if you're scaling fast. A three-year commit might lock you into a per-endpoint rate that's terrible by year two. I'd add a column comparing annual vs. multi-year, but with a 15% annual growth assumption.

That spreadsheet sounds super helpful, honestly. I'm still new to this too. Could you maybe share a column for the extra logging or data retention tiers? I heard that can bump things up. And did you include support costs? I think they have different support levels. Thanks for making this.

That template is a great first step. I've seen cost models break down when they don't account for the scaling of features like threat intelligence feeds or the EDR module's data ingestion. The per-endpoint cost for the base module might be static, but the telemetry volume per endpoint is variable. If your user base is technically intensive, like developers or data scientists, their endpoint behavior can generate significantly more data, pushing you into a higher data tier.

You should also isolate the cost driver for their cloud-native workload protection if your SaaS stack uses containers or serverless functions. That's a separate SKU with its own scaling logic, based on vCPU hours or function invocations, not endpoints. It's often negotiated separately and can catch teams off guard.

Consider adding a sensitivity analysis for the discount rate. A common mistake is applying the multi-year discount to the first year's endpoint count, rather than modeling the discount against a projected, growing count over the term. The net present value calculation can look very different.

Great point about API calls being a sneaky overage charge. It reminds me of a time when our team automated some alert responses with their APIs and the volume spiked way more than we'd budgeted.

I like the 15% growth assumption idea for comparing contract lengths, though. It makes the multi-year discount math a lot more realistic. Could be a good spot to add a simple data table in the sheet to visualize the crossover point.

This is so cool, I was just about to start building something like this myself! Thanks for sharing.

I was trying to think through the same variables. One thing I'm still fuzzy on is how they count an "endpoint" - is it purely based on the device, or do things like ephemeral cloud instances get counted differently? I'm not sure if that's something that needs its own column in a basic model or if it's more of a contract detail.

Really appreciate you posting this. It's a great starting point.

Oh, the first spreadsheet. A noble effort, but these initial models are always the prelude to the real cost discovery.

You mentioned "modules" and "contract length," but those are the givens. The real cost driver they don't advertise is the variable data tax. Your base module price assumes a "normal" endpoint. Deploy it to a team of engineers compiling code all day, and the telemetry volume per endpoint can triple. Suddenly you're getting a call about moving to a higher data tier, which is just a polite term for a price hike.

Did your simple formulas have a cell for 'endpoint personality type'? Because their pricing does.

That 'endpoint personality type' variable is crucial, and it's often discovered post-sale through a bill shock event. Your example of developers is perfect; I'd add that automated build servers or CI/CD workers are another classic example of a high-volume, high-data 'personality' that can trigger a tier reclassification.

A basic spreadsheet will miss this because the initial quote is blind to workload. The only way to model it is to add a multiplier column for expected telemetry volume per endpoint group, but you'd need internal usage data from a POC that most teams don't capture adequately. It turns a simple per-unit cost model into a forecasting exercise on user behavior.

Absolutely right about the 'endpoint personality type'. The spreadsheet logic breaks down because it assumes a static unit cost, but the vendor sees your endpoint as a variable data pipe. You can't model that without historical telemetry from a POC, which most teams don't have the time or tools to collect properly.

The worst offenders aren't even developers, in my experience. It's headless systems: build agents, render farm nodes, data processing workers. They're silent, they run constantly, and they generate a perfectly uniform, high-volume stream that will absolutely trigger a data tier review. Your 'cost per endpoint' column becomes meaningless if 20% of your fleet falls into that category.

The real question for OP's spreadsheet is whether to even include a per-endpoint column, or to shift the model to cost per gigabyte of telemetry ingested, which is the real unit they're measuring after the first bill shock.

The shift to cost per gigabyte is the logical conclusion, but you'll never get them to quote you that way upfront. Their entire pricing model is built on the fiction of the countable "unit" because that's what procurement understands. Walking into a negotiation asking for a price per GB of telemetry is like asking a car dealer for the price per combustion event.

You can model it internally, though. Take their per-endpoint quote, then during the POC, actually measure the daily telemetry volume from a sample of each "personality" you mentioned. Build your own shadow model with a per-GB column derived from those numbers. When the first true-up bill arrives, you'll have your own data to argue the overage, or better yet, to renegotiate the entire structure before renewal.

It's not about building a better spreadsheet for them. It's about building a parallel reality for your own finance team.

You're spot on about the extra logging tiers and support costs. Those columns are crucial, and I'd argue support is often the hidden multiplier that blows up a simple per-unit model. The "Enhanced" or "Premium" support tiers can add 20-40% to the annual cost, but they're usually priced as a percentage of your license total. That means your support cost inflates automatically if your data volume pushes you into a higher licensing tier mid-contract. It's a double hit.

For logging and retention, I'd add separate rows, not just columns. Think of them as add-on services that scale independently. A 90-day vs. 365-day retention might look like a simple toggle, but the cost isn't linear - it often jumps after the first incremental tier. And if you're archiving for compliance, that's usually a separate, even more expensive SKU.

So yeah, your instincts are good. A simple spreadsheet without those elements is just the sticker price, not the true cost of ownership.

Good on you for starting with a spreadsheet. It's the right instinct. The critical gap in a first-pass model like this is assuming a static cost per endpoint. As others have hinted, that's the vendor's starting fiction.

Your biggest missed cost drivers are the scaling variables that act as multipliers on that base unit:
* **Data tier escalations** based on telemetry volume per endpoint type.
* **Support cost inflation**, as premium support is often a percentage of your license total, which grows if you hit a higher data tier.
* **Non-endpoint SKUs** for cloud workload protection, which scale on vCPU or function invocations.

The template is useful for the list price math, but the real model needs columns for 'Endpoint Group', 'Estimated Daily Telemetry Volume (GB)', and a shadow calculation of an effective price per GB. You'll need POC data to populate it, otherwise you're just modeling the quote, not the eventual invoice.