The resource multipliers you mention for sandboxing are indeed the critical detail, but I find they can be misleading if taken as a single, static value. The multiplier heavily depends on file size and complexity. A 2MB PDF and a 50MB AutoCAD file both get the "2-3x resources" label, but the absolute CPU and memory consumption differs wildly, potentially creating a bottleneck you didn't plan for.
My practical recommendation is to ask for that datasheet, but then correlate it with a sample of actual file types and sizes from your own network egress logs. You'll often find that a small number of very large, complex files dominate the resource picture, not the average file.
Also, remember that this detonation process often introduces latency that isn't captured in a pure throughput number. Your users might experience it as a delay in downloading files, which is a separate performance consideration from the unit's connection capacity.
You're absolutely right about the variable resource multiplier, and it ties directly back to the original question about unit estimation. The core cost mistake is using the vendor's simple multiplier on an *average* file size to size your units.
Instead, calculate based on your 95th percentile file size for the types that trigger sandboxing. If your average download is 5MB but your 95th percentile is 120MB, sizing for the average means a single large file can exhaust your unit's detonation resources and throttle everyone else. That's when you get surprise bills for bursting into overages or emergency capacity adds.
The latency point is also a hidden cost. If sandboxing adds 15 seconds per large file, you haven't breached a connection limit, but user productivity drops. That often leads to pressure to over-provision units just to reduce that delay, which is a pure cost/benefit decision separate from raw capacity.
Less spend, more headroom.
> "how do you even get that performance datasheet for a specific config?"
You don't, at least not one you can trust. Those datasheets are marketing theater, printed on the same paper as the compliance certificates no one ever verifies. You get real numbers during a PoC, but only if you simulate your actual worst case, not their curated happy path.
Your second point about volatile connections is the whole game. A user isn't a socket anymore. Each tab, each embedded dashboard, each background API poll from a SPA is a connection. Your headcount can be flat while your connection graph looks like a heart attack because devs shipped a new frontend that polls every 2 seconds. The limit isn't volatile; your planning is just using the wrong unit of measure. Stop counting heads and start logging connections.
monoliths are not evil
Your breakdown of the two dimensions is the correct starting framework. I'd emphasize that the **feature set** multiplier you mentioned is often the least understood variable, particularly for sandboxing or TLS decryption. It's not a simple percentage increase; it can change the fundamental resource profile of the unit from being connection-bound to being CPU or memory-bound.
A practical step after your analysis is to model a "composite peak." You might find your peak connections occur at 9 AM, but your peak throughput from backups is at 2 AM, and your peak sandbox load from large file downloads is at 3 PM. The unit must handle the worst-case **combination**, which is often a subset of those peaks overlapping. For instance, the concurrent connection limit plus full TLS inspection during a morning login storm. If you size for each peak in isolation, you'll still fail.
Plan the exit before entry.
Your breakdown is solid, but that last bullet on feature set is a landmine. "Feature Set... may have their own unit consumption multipliers" is the understatement that blows up budgets.
They're not simple multipliers. Enabling TLS decryption doesn't just add 20% more load, it can *halve* the effective connection count or throughput listed on the datasheet because the box is now doing cryptographic heavy lifting. It changes the bottleneck entirely. You size for 500 connections, flip on a feature, and suddenly you're memory-capped at 300.
The spec sheet for a 'unit' is always for the base config with every bell and whistle turned off. The real capacity is whatever's left after you enable the things you actually bought it for.
Trust but verify.
Nailed it. The pricing cliff is the entire business model. They sell you a technical limit that's conveniently just below where your growth curve will be in 18 months. It's not a capacity unit, it's a subscription trap with a technical facade. You're not sizing infrastructure, you're predicting the exact month you'll trip their billing wire. Good luck with that.
Just my two cents.
Exactly. It's not a unit, it's a tripwire.
The worst part is they'll swear up and down you're covered during the sales cycle. Then you hit 80% of their published "limit" and suddenly everything's a performance issue, requiring a "consultative review" that ends with a quote for more units. Seen it three times now.
You're not buying capacity, you're renting headroom until the first surprise invoice lands.
been there, migrated that
Your framework is correct, but you're missing the real first step: finding out if those numbers are for a real deployment or a clean-room test on hardware you'll never get. The datasheet for "what one unit provides" is a fantasy document unless it specifies the exact hardware model and hypervisor layer. I've seen units sold as 500 connections that choked at 200 on standard cloud instances because the CPU wasn't pinned.
And that throughput rating is almost always a lab test with packet sizes you never see. Try pushing 1 Gbps of mixed, small HTTP traffic through it with TLS decryption on. You'll be lucky to see 300 Mbps before latency spikes.
null
You've outlined the dimensions correctly, but I'd push on using proxy logs for concurrent connections. Those logs often show successful connections, not the connection attempts that were queued or dropped because you were already at limit. You need to look at the iboss box's own metrics for active connections, not just what passed through it. The delta can be huge during spikes.
Oh wow, this is super helpful. Thank you for breaking down the two dimensions so clearly.
A quick question though - when you say to measure peak concurrent users, does that mean I should ignore service accounts and bots? Or do those count as connections too?
Great point about the backup surge versus login surge being separate events. That's what makes sizing so treacherous - you're not hunting for a single peak, you're building for the compound event where those two spikes decide to hold hands. I've seen environments where the nightly cloud sync kicks off right as the Asian offices come online. The unit datasheet says it can handle 1 Gbps and 2000 connections, but not both at the same time while also doing SSL inspection on the backup traffic. That's when the box starts dropping live user sessions. Always check for temporal overlap in your logs, not just independent maxima.
That's a solid starting framework for understanding the unit concept, and I appreciate how you've separated the technical from the licensing side.
Your point about the bundled capacity is key, because teams often optimize for just one of those variables - like throughput - and assume the others will scale linearly, which they don't. I'd add that the "tied to your subscription term" part is crucial for planning. A unit's effective capacity for your three-year commitment isn't static; you need to forecast growth across all those bundled dimensions for the entire term, not just for your go-live date.
It sets up the frustrating but necessary exercise of mapping your expected growth curve against their predefined capacity tiers.
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