Check out my cost breakdown for 500 EC2 instances over 6 months.

That's a great point about the locked fee not being the problem, it's the lack of review. It reminds me of when we locked in our email provider rate. We celebrated the flat cost, but didn't plan a regular check-in on actual volume changes.

So you're right, the predictability is only a benefit if you actively manage the other variable. Maybe the real risk is getting too comfortable with that fixed line item and forgetting to question if you still need all 500 agents.

Thanks for posting the actual numbers, that's super useful. The $4.74 average is a solid benchmark. Your point about forecasting being straightforward really hits home, we've had the same experience with per-instance models for monitoring agents.

That said, have you checked if that average holds across all your instance types? We found our Windows instances consistently cost a bit more for the agent than Linux ones, which nudged our blended average up. It didn't break the model, but it did make some workload placement decisions more interesting.

How does that $4.74 look next to your actual EC2 compute costs now, especially if you're using any RIs? I've started thinking of the security layer as a percentage of the compute cost, and that ratio can shift a lot once the base compute gets optimized.

That's really interesting about the CPU overhead scaling differently. I hadn't considered that. When you say you modeled each tier separately, did you build that model from scratch, or did you tweak an existing tool's output? Asking because we've only used the public calculators, and they definitely don't ask for that level of detail.

The CSV upload point is great - I've only seen sliders before, which makes sense for a rough guess but not for planning. How much of a difference did modeling a real inventory make in your final projection? Like, was it a small adjustment or a complete re-think of the numbers?

Building the tier model from scratch was the only way to get an accurate projection. Public calculators use broad assumptions, which fail when your inventory has a mix of, say, memory-intensive apps versus CPU-bound batch jobs. The variance in CPU overhead between a "light" and "full" agent profile can change the effective cost per instance by 30% or more.

Uploading a real CSV inventory didn't just adjust the numbers; it redefined the unit of analysis. Instead of a single average cost, we had a distribution. We found 15% of our instances were candidates for a lighter agent tier immediately, which the slider-based estimate had completely missed. The model shifted from "what's the cost" to "where are the cost drivers."

You're absolutely right that the average can be misleading. We saw the same thing. The $4.74 was a blended number, but the distribution had a long tail. Tag-based resource grouping revealed that nearly 20% of our instances were non-production workloads running the full agent profile because of a blanket deployment policy. Shifting those to a lighter tier brought the effective cost for our true production instances down to the $5.20 range, but that's a much more honest number for capacity planning.

The real insight came from correlating the agent cost distribution with our CPU reservation data. The instances where the agent represented the highest percentage of total cost weren't our most expensive computes; they were the older, discounted ones where the fixed agent fee became a larger slice of a smaller pie. It changes the optimization target entirely.

That's a good point about resource overhead being distinct from the billing model. We measured it directly on a sample set by comparing `%usr` and `%sys` from `mpstat` before and after agent deployment. The CPU overhead was consistent but non-zero, typically between 0.5% and 1.5% per core on Linux. It didn't change our instance sizing, but it did eat into the headroom we'd allocated for baseline OS processes.

The memory footprint was more predictable, a fixed reservation of about 80-100 MB per instance, which is easier to account for. The real issue is when that overhead isn't uniform. If you have a fleet of t3.medium instances already running at 70% steady-state CPU, that extra 1% could push you into throttling territory, forcing an instance type upgrade. It's worth modeling your specific workload saturation.

> The real issue is when that overhead isn't uniform.

Exactly. That 1% overhead is trivial on a c5.4xlarge, but it's a rounding error away from costing you money on a t3.medium. The baseline CPU credit drain is the real killer there; it pushes you toward a larger instance size or a more expensive burstable type for no functional gain. We moved several such workloads to t3a and saved more on the compute shift than the agent cost itself.

Your memory numbers track with ours, though. Predictable at least.

> predictable dollar cost

Sure, until you factor in the overhead. Our benchmark showed a 1.2% CPU overhead on Linux, but 2.8% on Windows Server 2019. That's not trivial on already-saturated instances.

Your $4.74/month is really $4.74 + the cost of that lost capacity. On a c5.large that's maybe $0.40. On a t3.small it's a forced upgrade. The billing line is flat, but the compute tax isn't.