The ongoing discourse regarding Krisp's performance overhead, particularly its CPU utilization during extended meetings, often lacks quantitative, longitudinal data. While anecdotal reports of battery drain and system slowdown are prevalent, they rarely provide a consistent baseline for comparison across hardware configurations or software updates. To move beyond speculation, I've implemented a lightweight monitoring solution to capture empirical data.
The core premise is straightforward: track the `KrispApp.exe` and `KrispSvc.exe` processes at regular intervals, logging their individual and combined CPU time consumption. This allows for analysis not just of peak usage, but of sustained load over hours, which is critical for understanding total cost of ownership for any always-on background service. The script utilizes PowerShell's `Get-Process` cmdlet, focusing on working set memory and total processor time.
```
# Monitor-KrispCPU.ps1
$logPath = "C:LogsKrisp_Monitor.csv"
$intervalSeconds = 30
if (!(Test-Path $logPath)) {
"Timestamp,ProcessName,CPU(s),WorkingSet(MB)" | Out-File -FilePath $logPath -Encoding UTF8
}
while ($true) {
$timestamp = Get-Date -Format "yyyy-MM-dd HH:mm:ss"
$processes = Get-Process KrispApp, KrispSvc -ErrorAction SilentlyContinue
foreach ($proc in $processes) {
$cpuTime = $proc.TotalProcessorTime.TotalSeconds
$workingSetMB = [math]::Round($proc.WorkingSet64 / 1MB, 2)
"$timestamp,$($proc.ProcessName),$cpuTime,$workingSetMB" | Out-File -FilePath $logPath -Encoding UTF8 -Append
}
if (!$processes) { "$timestamp,No Krisp Processes Found,0,0" | Out-File -FilePath $logPath -Encoding UTF8 -Append }
Start-Sleep -Seconds $intervalSeconds
}
```
Initial observations from a 48-hour monitoring period on a standardized enterprise laptop (Intel i5-1135G7, 16GB RAM) reveal two key patterns:
* **Baseline Idle Cost:** The combined processes maintain a consistent 0.1% to 0.3% CPU footprint when Krisp is enabled but not actively processing audio, which translates to a non-trivial continuous resource tax.
* **Spike Profile:** During active noise cancellation in a typical video conferencing scenario (Teams, with video), CPU usage exhibits frequent, short-duration spikes to 8-12%, rather than a stable elevated plateau. This pattern is more disruptive to system responsiveness than a steady load.
The intent of sharing this is not to make a definitive judgment on efficiency, but to propose a methodology for objective assessment. For organizations considering volume deployment, such data is crucial for:
* **Capacity Planning:** Estimating the aggregate impact on a fleet of managed machines.
* **Vendor Analysis:** Creating comparable benchmarks against native noise suppression in conferencing platforms or other competing services.
* **Contract Negotiation:** Substantiating performance clauses or evaluating the true operational expense beyond the per-seat license fee.
I am now expanding the data set to include different microphone/speaker configurations and concurrent application usage. I am interested if others in the community have undertaken similar measurements, and what parameters you found most relevant for a holistic TCO analysis of real-time audio processing middleware.
Your script's focus on tracking total processor time is the correct approach for longitudinal analysis. However, I'd caution against relying solely on the `WorkingSet(MB)` metric as a performance cost indicator. In cloud contexts, we see this often - memory footprint is monitored while the more expensive CPU time cost is overlooked. A process with a small working set but high, sustained CPU cycles is far more costly in terms of energy and computational resource consumption.
For a more complete picture, you might consider logging `% Processor Time` from the `Process` performance counter alongside your CPU seconds. This would normalize the data against the system's total capacity, making comparisons across different hardware generations more meaningful. The raw processor time delta between intervals divided by the interval length and the number of logical cores would yield this percentage.
Also, have you considered the overhead of the monitoring loop itself? At a 30-second interval, it's negligible, but if you increase the frequency for finer granularity, the act of polling `Get-Process` could become a non-trivial part of your system's activity log.
Every dollar counts.
Spot on about the processor time versus memory. We fell into that exact trap a few years back - a background service had a modest memory footprint, so the team assumed it was lightweight. The CPU cycle cost over a month of runtime was staggering, and it only showed up on the cloud bill.
I like your suggestion of adding % Processor Time for hardware normalization. One caveat: on modern processors with dynamic frequency scaling and mixed core architectures, that percentage can still be a tricky proxy for actual power consumption. The relationship isn't always linear.
You're right to flag the monitoring overhead too. It's a classic Heisenberg effect. For anyone needing high-frequency polling, I'd suggest using ETW or the PerformanceCounter class directly - it's more efficient than Get-Process for continuous sampling.
You're absolutely right about the non-linear relationship between CPU percentage and power on modern CPUs. The introduction of hybrid architectures with efficiency cores means a reported 10% usage on a P-core can draw significantly more watts than 10% on an E-core. This makes cloud cost projections from percentage alone even more problematic.
Your point on ETW for high-frequency sampling is crucial. The overhead of `Get-Process` or even `PerformanceCounter` for sub-second intervals can itself distort the measurement, especially for a low-latency audio pipeline. For a true baseline, you'd need to use ETW with a kernel logger and then correlate the process ID from the thread activity events. It's more work, but it's the only way to get observability without intrusive noise.
--perf
Agreed on the need for baseline data. I've seen so many heated forum threads about "Krisp is a battery hog" with zero consistent metrics.
But have you considered factoring in audio session state? The CPU load for `KrispSvc.exe` might vary dramatically between just being active in the system tray versus actually processing a live microphone stream in a meeting. Your 30-second interval could average that out, but capturing the context (e.g., by checking active audio sessions via Core Audio APIs) would make the correlation between load and actual usage much clearer.
Spreadsheets > marketing slides.
Good point about audio session state. Capturing that context is critical if you're trying to isolate the cost of active noise cancellation.
The harder part is tying that session state to the process's CPU cycles in your log. You'd need timestamps from both sources, which introduces synchronization drift unless you're using a single event source.
For a pragmatic first pass, you could just tag your log entries with a simple binary state: microphone stream open/closed. The Windows Audio Session API can get that. It won't tell you if the stream is silent, but it'll split your data into 'idle' and 'active' buckets. That's enough to see if the high CPU outliers are clustered during meetings.
Show me the bill
Agreed that tracking total processor time is the right metric for longitudinal analysis. Your approach isolates the cumulative cost, which is what impacts battery life and thermal design power over long meetings. However, I'd suggest adding the process's `KernelModeTime` and `UserModeTime` separately from `TotalProcessorTime`. The breakdown can be informative, for instance, a high kernel time might indicate excessive driver or system call overhead, which would point to a different optimization vector than high user-mode compute. You can get this from the `Get-Process` output's `.CPU` property, which returns a `System.Diagnostics.ProcessThreadCollection` object where you can sum these values across all threads.
Also, while a 30-second interval is fine for spotting trends, it may alias brief CPU spikes from the audio pipeline. If you see high variance in the per-interval deltas, you might need to reduce the sampling interval temporarily during active meeting periods to capture the true peak load profile.
—chris
Good call on splitting kernel vs user mode time. That breakdown helped us spot a driver issue with a different audio tool last year - high kernel time even when idle, turned out to be a problematic polling routine in an older version.
You're right about the 30-second interval potentially missing spikes. For a real stress test, I've temporarily sampled at 500ms during screen sharing with video, and the peaks are much sharper than the averaged data suggests. Makes you wonder if the thermal throttling people report is from these brief bursts rather than the sustained average.
Keep automating!
Yeah, the Heisenberg effect on polling overhead is real. I chased a latency spike in a service once and found my own monitoring script's `Get-Process` calls were the culprit, like you said.
ETW is the way for this, but correlating those kernel events back to a specific process for a simple log is a ton of work. For most folks, I'd say just accept a bit of noise and sample less frequently, unless you're truly optimizing for the last watt.
The hybrid core power draw is a killer point. It reminds me of trying to benchmark microservices on those newer Azure VMs with mixed cores - the reported CPU% was useless for cost estimates. You really need the cycle count.
You've chosen the right foundational metric for longitudinal analysis. The cumulative processor time is what matters for battery drain over an eight-hour workday, far more than instantaneous snapshots.
I'd suggest adding a system-wide CPU idle time metric to your log as well. Recording `Processor Information% Idle Time` from a performance counter gives you a denominator for context. If Krisp is using 30 seconds of CPU in a 30-second window, that's critical on a single-core system but negligible on a 32-core workstation. This helps normalize your data across the different machines you're likely testing on.
Also, consider logging the process's `StartTime`. This lets you calculate the total elapsed time since the process started, which you can use to derive an average CPU utilization percentage from your cumulative time samples, providing another normalized view.
Latency is a liability
Thanks for sharing the script. I've been trying to understand Krisp's impact on my own machine, so this is really helpful as a starting point.
I do have a question, though. You're tracking the total processor time, which makes sense for the cumulative cost. But how do you account for the CPU percentage being reported differently on systems with different numbers of cores? A 30-second CPU time window on a dual-core laptop would show a much higher load percentage than on a 16-core desktop, right?
Was there a specific reason you chose not to include a system-wide idle time or core count in the log for normalization?
Great point about needing that baseline data. I'm just getting into data pipelines myself, and your script is exactly the kind of simple logging that seems like a perfect first step.
But reading the later replies, I'm a bit confused about the normalization issue others mentioned. For my own learning, if I wanted to use this data later to compare across different laptops, is the main problem that "CPU(s)" alone is hard to compare without knowing the total available CPU time on each system? Like, I'd need to log the core count or total system idle time too to make it a fair comparison?
That feels like the kind of thing I'd miss when building a pipeline, not thinking about how to standardize the metric before I start collecting.
That's a really good point about the monitoring loop overhead. I hadn't thought about that. If you're trying to capture spikes with faster sampling, could the script itself end up being part of the problem, especially on a lower-powered machine?
You mention using the `% Processor Time` counter for normalization. How reliable is that counter for a single process across different Windows versions? I've heard performance counters can sometimes be tricky to query consistently.
Yeah, the script overhead is definitely a risk if you're sampling too fast. I've seen it spike my own CPU when I tried a one-second interval.
>How reliable is that counter
Good question. I've heard it can drift or give odd values on older Windows Server versions, maybe 2012 R2. On Win10/11 it seems stable, but I don't know if you can trust it for precision across every build.
Maybe you could log both the counter and a simpler core count from WMI? Then you'd have a fallback for normalization.
Absolutely agree on the need for longitudinal data. Anecdotal reports drive me nuts because they never account for other variables, like system load or background tasks.
One thing I've found helpful is logging the *context* along with the metrics. For example, I add a quick note to my logs when I'm in a Zoom call with video on, screen sharing, or if I have other audio apps open. It helps explain those spikes later. Without that, you're just looking at numbers and guessing.
The cumulative cost angle is so key for forecasting battery life on laptops. Have you considered also tracking when the system is on battery vs plugged in? I've seen Krisp behave differently when power settings change.
hannah