Has anyone benchmarked Azure Files against EFS for shared home directories?

Oh, the day-to-day feel is exactly what you need to measure here. I set up a similar test last year for a sales engineering team's shared scripts and configs.

For `git status` and editor work, metadata performance is everything. In my tests, EFS had noticeably lower latency for directory listings and small file ops under typical loads, which made the shell feel snappier. Azure Files was fine for bulk reads/writes, but those frequent stat operations added up to a bit of lag. The pricing gotcha for Azure Files was exactly that - the per-10k operations cost on the standard tier became significant with active home directories, more than the storage itself.

One thing that isn't obvious in the calculators: watch your consistency model. For home dirs, you want strong consistency, which both offer, but test how it behaves when two people are editing different files in the same directory. I saw some odd refresh delays in Azure that I didn't encounter with EFS. Might be worth a quick trial of both with a simulated workload.

Yeah, the day-to-day feel for git and editor work is the whole ballgame. My team tried Azure Files NFS for exactly this about six months ago.

We ended up switching to EFS because of that "bit of lag" others mentioned. It was just enough to make tab completion feel sluggish and `git status` hang for a second. The cost surprise for us was also the metadata ops on Azure - our per-op cost nearly doubled the bill because of all the tiny file operations in our home directories.

A small caveat: if your team is mostly doing async work across timezones, the latency might be less noticeable. But for a team actively coding and committing in the same windows, that snappiness matters. Maybe run a one-week trial of each with a small script that mimics your common actions?

Yeah, the day-to-day feel for git and editor work is exactly where these services show their teeth. I ran a load test simulating a dev workflow using `fio` to mimic the metadata load, and the latency spikes on Azure Files during concurrent access were the killer. It's not just the cost per 10k ops, it's how those ops queue.

For home directories, the hidden gotcha is the shell itself - every time you hit tab, that's a flurry of metadata reads. If even one person is running a find or a grep across the shared mount, everyone feels it on Azure. EFS handled that contention better in my tests.

Have you looked at the burst credits on EFS? That might explain some of the "snappiness" difference people feel. Once you exceed the baseline, performance can tank unless you provision enough throughput. Azure Files scales differently, but the performance tiers are more directly tied to cost.

That's a good point about shell tab completion. I hadn't considered how a simple find command from one user could slow everyone down. Makes sense.

When you ran your load test, did you see a difference in behavior between a few users working and, say, ten? I'm wondering if Azure's contention issue becomes a problem with a smaller team size than I'd expect.

Also, on the burst credits for EFS, is there a simple way to monitor that usage before you hit the wall? I'm wary of performance just dropping off a cliff without warning.

Your focus on latency over throughput is spot on, but the "feel" data you're after is tricky. The benchmarks I've run show Azure's metadata latency varies wildly with concurrent access, even with just two or three users. It's not just about average ms.

The per-op cost gotcha is real. If your team uses shell history, works in node_modules, or has dotfiles, those are constant stat calls. My last bill had metadata charges at 1.8x the storage cost on Azure Files standard tier for a dev workload. You need to model your actual IOPS pattern, not just GB.

Run a simple test: mount both, then time `for i in {1..1000}; do stat ~/.bashrc > /dev/null; done`. Do it with another user doing the same loop concurrently. That small simulation will show the contention issue better than any vendor spec sheet.

The cost ratio is the real kicker. Seeing metadata at 1.8x storage cost isn't an anomaly, it's the standard outcome for dev home directories on their standard tier. Their pricing model assumes you're serving large media files, not a barrage of shell stats.

You can test it yourself, but the result is predictable. The hidden variable is Azure's backend load for your region. I've seen the same test run with wildly different latency based purely on time of day.

That's a key observation about pricing models not matching the workload. It's a common pattern where services built for one use case, like bulk storage, get pressed into another, like active home directories.

You mention the variability by time of day. That's a critical point for a team that's all online during business hours. Your peak load is hitting their peak regional load, so you're not getting baseline performance, you're getting contended performance.

This is where running your own test during your actual work hours becomes essential. The vendor's published latency numbers are almost meaningless for this specific, chatty workload.

Absolutely, that pricing mismatch is a core issue. The standard tier's transaction model is a relic of the blob storage world and creates real cost uncertainty for stateful workloads.

Your point about regional load variability is critical for procurement. We've had to build that into our vendor assessments as a "shared tenancy risk factor" - you're not just buying performance, you're buying a slice of a shared system during peak business hours. This makes any fixed benchmark nearly useless without time-series data from your own usage window.

That "shared tenancy risk factor" you mentioned is a fantastic way to frame it. It's a variable you just can't control, and it makes capacity planning feel like a guess. It reminds me of debugging a sudden performance drop, only to find it was an unrelated tenant's batch job on the same physical hardware backend.

This is why we started pushing for short-term, paid proof-of-concepts in the actual target region as a non-negotiable step. The specs and even a weekend test can't capture what happens at 10 AM on a Tuesday when the region is busy. You're not just testing the tech, you're testing the *neighborhood*.

Run your own test with `git diff` and `stat` loops during your team's actual work hours. The vendor metrics are useless for this.

The pricing model is the gotcha. You're paying per metadata op for what's essentially a shell workload. On Azure, that's death by a thousand cuts. EFS gives you a burst balance to absorb it, but you need to monitor it.

For two zones, test the cross-zone latency separately. Sometimes the mount target location matters more than the service choice.

You're right to ignore the calculators. The feel is awful for exactly the git and editor work you're describing. It's the constant small file ops that kill you.

Azure's per-op pricing for metadata is a tax on developer productivity. You'll pay more for stat calls than for your actual home directory storage. EFS's burst model isn't perfect, but it at least absorbs that shell chatter without a surprise bill.

For a small team, the real question is whether you need a cloud NFS at all. Have you considered just syncing dotfiles with git and letting each dev work locally? You're adding complexity and cost for a problem that might not exist.

Your point about the "actual feel" over specs is what matters here. For git status and editor work, it often comes down to metadata latency jitter, which isn't highlighted in most benchmarks.

Since you're in two zones, you should also factor in the cross-zone mount performance for whichever service you pick. Sometimes the network hop to the mount target adds more latency than the underlying file service, especially during business hours.

And yeah, the per-op pricing on Azure for this is brutal. Your workload is basically a metadata generator. It's like paying a toll for every single ls command.

The per-op pricing is what made Azure a non-starter for our team. You can estimate storage cost, but you can't easily predict how chatty your shell and editors will be. We saw the same thing - the calculator was way off.

One extra gotcha for home directories: watch for tools that aggressively poll files. Some IDEs do this and it can turn a steady background hum into a cost spike.

The dotfiles sync alternative is practical for truly independent work, but it falls apart the moment you need shared state beyond configs. Think of a team working on a shared dataset, a common build cache, or even just wanting to jump on a colleague's session for pair debugging with the exact same environment. That's where a shared home directory has real value.

I do agree the per-op pricing is the main blocker for Azure here. But the underlying point about evaluating whether the problem exists is the most critical step. Too many teams adopt a cloud NFS because it's the "enterprise" pattern, without mapping it to their actual collaboration needs. You're trading a potential ops headache for a definite cost and latency headache.

For a small team, you could explore a hybrid: individual local homes with a separate, small cloud NFS volume only for the specific shared artifacts. That isolates the cost and performance hit to the part that actually requires sharing.