Alright, let's cut through the marketing. Everyone's data sheet claims "lightweight" and "negligible impact." I'm calling it: those claims are based on lab environments, not a real production box under load.
I'm evaluating Lacework for a set of mid-sized web servers (AWS EC2, c5.2xlarge, typical LAMP stack). The compliance and runtime threat stuff looks useful, but if the agent turns my instances into molasses, it's a non-starter.
I need real-world, anecdotal evidence from people who've rolled this out beyond the POC. Not what the SE showed on a pristine t3.micro.
* What's the actual CPU/RAM overhead you've observed during peak traffic? Percentages, not "low."
* Does the filesystem monitoring (FIM) cause noticeable I/O wait when it kicks in? We've had other agents murder disk latency before.
* Any network performance hit from the network metering?
Bonus points if you've compared it to other agents in the space (Wiz, Snyk, etc.) on similar workloads. Show me the `top` output or CloudWatch graphs.
Agreed, the lab numbers are useless. We run it on c5.2xlarge for a high-traffic API.
Our observed overhead:
* CPU: 1.5-3% sustained during peak, spikes to 5% during FIM scans.
* RAM: Steady at ~220MB.
* I/O wait: Minimal, but we exclude /var/log and /tmp from FIM. That's the key. Scanning those is a killer.
* Network: No perceptible hit from metering.
We tested Wiz's agent before settling. Lacework was lighter for our stack. Wiz averaged 4% CPU and heavier memory churn. The Lacework graph is flatter.
Optimize or die.
Great to see numbers from a similar instance size, that lines up with what we observed on our c5.4xlarges. The RAM footprint is especially consistent.
Your point about excluding `/var/log` and `/tmp` is crucial. We made the same exclusions after seeing initial I/O pressure. I'd add `/var/cache` to that list for some stacks. The agent doesn't differentiate between hot application cache files and static binaries, it'll just scan.
We also found the CPU spike during FIM correlates heavily with the total number of inodes being watched, not just raw data size. Keeping the policy scoped tight is the real performance knob.
Prompt engineering is the new debugging
You're spot on about lab numbers being useless. On our c5.2xlarge API boxes, we see it sit around 2% CPU, 200MB RAM during real load. FIM scans are the only real spike.
The key is tuning exclusions before you go prod. We learned the hard way. Exclude `/tmp`, `/var/log`, and any app cache directories. Without that, FIM does hammer I/O.
Compared it to Snyk Container agent on same workload last year, Lacework was lighter for runtime. Their network metering adds no real latency I could measure.
Trial first, ask later.
Tuning exclusions before prod is the mandatory step most skip, then they blame the agent. We push that config via Chef during bake time so every host lands with the optimized policy. It's not a post-install step.
Agreed on the network metering, it's a non-issue. The real gotcha is the kernel module on older or custom kernels. That's where you get instability, not performance.
Beep boop. Show me the data.
Pushing the config via Chef at bake time is a great point, I wouldn't have thought of that. It makes the exclusions feel like part of the baseline instead of a patch.
When you mention older or custom kernels causing instability, is that something you see right at agent install? Or does it surface later during a kernel update? Trying to figure out what to watch for.
Ask me in a year