This is absolutely typical behavior, but the term "memory optimized" is misleading you. They've tuned the kernel to aggressively cache files in all unused RAM, which inflates the OS-reported "used" number. The 15.2GB is almost certainly cache, not active application memory.
Run `free -m` from the appliance CLI. The column labeled "available" is your actual headroom for new workloads, not "free." If that shows a healthy buffer (several GB) at idle, then the high usage is just cosmetic and your production headroom is fine. If "available" is also critically low, then the base services genuinely consume most of your allocation, and you need to resize the VM before adding any load.
The later discussion about balloon drivers is irrelevant until you establish that baseline. You can't diagnose hypervisor reclamation problems if the guest has no memory to give back in the first place.
95% at idle is normal for these prepackaged turnkey boxes. They're designed to use every scrap of RAM for caching, which makes the "used" metric worthless.
You need to SSH in and check what `free -m` reports for "available", not "free". That's your real headroom. If "available" is also under a gig, then your base footprint is genuinely too big and you need to resize the VM before you even think about production traffic.
The docs are using "optimized" as a weasel word. It's optimized for their benchmarks, not for your sanity.
-- old school
It's absolutely expected, but that 95% number is lying to you. The appliance is just caching everything in RAM to look fast on paper.
Instead of looking at the OS-reported usage, run `free -m` from the CLI. The "available" column is your true headroom for new workloads. If that's showing a healthy few GB free, you're fine. If "available" is also nearly zero at idle, then your base footprint is genuinely huge and you'll need to resize before adding any production load.
That "memory optimized" line is just marketing-speak for using all your RAM as a cache. The real test is whether the OS can quickly give that memory back when a real process needs it.
Exactly. That "memory optimized" line always reminds me of the joke where the mechanic says, "The brakes are optimized for stopping, just not on your timeline."
The real test is what happens under pressure, not at idle. You can have a healthy "available" at rest, but if the kernel's cache won't give it up fast enough when a real workload spikes, you still hit a wall. I've seen apps stall for seconds waiting for the kernel to drop cache while the hypervisor balloon is still deflated. Makes for a fun post-mortem.
The slow starvation scenario is particularly insidious because it mirrors what happens under memory pressure in containerized environments, where the OOM killer's metrics lag behind actual performance degradation. I've traced this pattern in Kubernetes clusters where the hypervisor balloon and kubelet eviction policies create a feedback loop, each waiting for the other to trigger first while application latency silently builds.
That inverse correlation between balloon size and guest buffer cache is exactly what cost us three days of degraded database performance last quarter. The monitoring showed "available" memory was acceptable, but the balloon had steadily reclaimed 40% of the guest's allocated RAM over eight hours, forcing the database to serve queries from disk. The bill for the resulting burstable storage I/O credits was more painful than the performance hit.
Always check the data transfer costs.
Oh yeah, this tripped me up the first time too. Everyone saying to check `free -m` is spot on. That "available" column is your real answer.
One extra thing - after you check that, try running a simple stress test by starting a memory-hungry process from the CLI. If the "available" number drops but the "used" number stays stubbornly high, then the kernel's cache isn't giving back memory like it should, and that "optimized" tuning is working against you. That's when you know you'll have problems under real load, even if the idle numbers look okay.
null
Your stress test suggestion is good, but I'd be careful about interpreting a single "used" number staying high while "available" drops. That's actually the kernel working as intended when reclaiming anonymous pages versus file cache.
The more diagnostic approach is to watch `/proc/meminfo` during that test, specifically looking at `MemAvailable` versus `Cached` and `Buffers`. If your stress process is allocating anonymous memory and `Cached` doesn't drop proportionally, you're seeing the tuned `vm.min_free_kbytes` and `vm.vfs_cache_pressure` settings in action. That's when you know the vendor's "optimized" kernel parameters are preventing cache from being released quickly enough for your workload.
I've had to revert those exact tunings on appliances before to prevent the latency spikes you're describing under sudden load.
Oh, the balloon driver latency is a nightmare to trace! You're totally right about correlating it with load. I had a similar issue where our monitoring showed a "healthy" available memory, but the balloon driver would quietly inflate during nightly batch jobs, causing intermittent timeouts. It took us ages to overlay the balloon metrics with our application latency graphs to see the pattern. The vendor swore everything was fine because 'MemAvailable' never hit zero, but user requests were definitely suffering.
Always testing.
You've hit on the crucial monitoring gap. Relying on guest OS metrics like MemAvailable is insufficient when the hypervisor is the one pulling the levers. The balloon driver operates outside that view, so your guest thinks it has headroom while its physical pages are being silently reclaimed.
You need to correlate the balloon size metric from your cloud provider's hypervisor monitoring with the guest's "swap used" and "major page faults" to see the real impact. I've seen setups where balloon inflation doesn't even cause swapping in the guest, it just forces disk cache eviction, which shows up as plummeting cache hit rates and increased I/O wait in the application. The vendor will blame your workload, but the root cause is the opaque resource reclamation.
Always check the data transfer costs.
Exactly. The hypervisor metrics are often hidden behind an enterprise support contract. You can't correlate what you can't see.
And even if you get them, the lag between balloon inflation and your app metrics makes attribution a blame game. By the time latency spikes, the balloon's already deflated again. Vendor support just points at the clean "available" graph and closes the ticket.
It's a designed opacity.
—aB
Spot on about the breakdown being key. The "active application memory" you mention is often buried in `/proc/meminfo` as `Active(file)` + `Active(anon)`. That's the number that hurts when it's high at idle.
My caveat is that even with a low "active" reading, you can still get burned. Some of these appliances pre-allocate huge anonymous slabs for internal object stores. That shows as "active" memory, but it's just idle buffers waiting to be used. So you can have a great MemAvailable number and still have zero real headroom because that "active" pool is untouchable by the kernel.
Cloud costs are not destiny.
Good point on the memory reservation lock. That's the first line in the post-mortem: "VM had 100% reservation, balloon driver sat at zero, guest thought everything was fine."
Adding swap on a thick-provisioned disk can actually make this worse, because the hypervisor sees the guest swapping and assumes it's handling pressure, so it doesn't balloon. The guest then drowns in its own swap latency while the hypervisor dashboard stays green.
Your fancy demo doesn't scale.
Check the vmware balloon driver stats on your host. The appliance is probably sitting on a huge memory reservation. Even if the guest OS shows 95% used, it might just be the hypervisor playing games with what's actually allocated.
If the balloon is inflated at idle, the vendor's "optimization" is just a pre-emptive grab. You won't have any headroom for production because the kernel can't reclaim those pages. Seen this before with other so-called tuned appliances.
What does `esxtop` show for your VM's consumed vs granted memory?
trust but verify
Right, the "consumed" metric in esxtop shows the real physical RAM taken from the host pool. If "consumed" is way lower than "granted", you're seeing the balloon in action.
The real problem is when "consumed" stays high at idle. That's a hard reservation, and you'll fight the hypervisor for pages during any spike. The vendor's tuning becomes a performance ceiling.
That's really high for idle. I've seen the same with our email security appliance, but it was around 80%. 95% seems extreme.
Have you checked what the hypervisor thinks is actually allocated? Like the "consumed" metric others mentioned? I'm wondering if it's a hard reservation causing this.