Comparison: Noise suppression in OBS vs Krisp for live streaming.

I'm a community manager for a software training company, and I oversee our live webinar setup where about a dozen presenters use OBS. We've tested both RNNoise and Krisp extensively for cleaning up voice audio during streams.

Here are the practical differences I've seen:

1. **CPU Usage**
OBS's RNNoise filter is lightweight. On a modern mid-range CPU, I see a 1-3% impact when active. It's efficient enough to be always-on for our presenters.
Krisp uses more GPU and CPU resources. On the same hardware, the Krisp desktop app can add 5-8% CPU load, and I've seen it spike higher if the system is already under strain from encoding.

2. **Sound Quality and Artifacts**
RNNoise does a decent job on consistent background noise like fans or AC hum. It can sometimes make voices sound slightly "telephone-like" or muffled if you set the suppression too high. Keyboard clicks are reduced but often still audible.
Krisp is generally more aggressive and effective at removing sudden noises like keyboard clacks, chair squeaks, or door slams. The voice quality usually remains clearer, but in some tests we heard a faint, watery "artifact" tail on certain consonant sounds.

3. **Cost and Access**
OBS RNNoise is free and built-in. You add it as a filter on your mic source; there's no account or signup.
Krisp has a free tier limited to 60 minutes per day. Their paid plan for unlimited usage runs about $8-12/month per seat when billed annually. You need to run their separate application.

4. **Setup and Latency**
RNNoise is configured directly in OBS. You adjust one slider; it's very simple. The audio latency it adds is negligible, under 20ms.
Krisp requires installing their app and setting your mic input in OBS to the "Krisp Microphone" virtual device. This adds a small but noticeable processing delay; we measured an extra 40-60ms of latency, which can cause minor sync issues if you're not using a camera.

I'd recommend starting with OBS's RNNoise filter. For a beginner with a simple USB mic, it's free, integrated, and light on your system. The only reason to consider Krisp is if your environment is very noisy (loud keyboard, shared space) and RNNoise isn't cutting it. To decide, tell us your CPU model and whether your main noise issue is a constant hum or sudden, loud sounds.

The point about your PC not being a beast is the key constraint here. While both tools work, your budget for CPU cycles matters just as much as your audio quality goals.

Krisp's higher CPU tax is real, especially on Windows where OBS is already handling encoding. That extra 5-10% load can push a system near its encoding limits, causing dropped frames. For a cloud tutorial stream, a stable video feed is usually more critical than perfect audio suppression.

Since you're starting out, I'd recommend using OBS's RNNoise filter first. It's integrated, free, and good enough for most background noise. Get your stream stable, then experiment with Krisp later if you find the noise reduction insufficient. Stacking both filters is rarely beneficial and just compounds the CPU hit.

Great question on CPU impact, and the existing comments are spot-on regarding general load. However, I'd add a data point on thread contention. On a Windows system, the real issue isn't just the average CPU percentage, but how Krisp's processing thread can interfere with OBS's encoder threads during scenes with high visual complexity. I've observed this cause intermittent audio glitches in my benchmarks, which RNNoise (running as a direct filter) doesn't exhibit.

For keyboard clicks, RNNoise often lets some high-frequency transients through, while Krisp is more aggressive and can sometimes introduce a faint "bubbling" artifact on plosive sounds (like 'p' and 't') in quieter rooms. Your fan noise, being a constant frequency, is where both perform quite well.

Stacking them is functionally redundant and adds latency. The diminishing returns aren't worth the compounded processing hit. Start with RNNoise, profile your system during a test stream with your typical workload, and only consider Krisp if you have clear, consistent CPU headroom above your encoder's usage.

That's a really solid point about thread contention. I've seen similar audio glitches when stress-testing OBS with multiple browser sources and Krisp enabled. It's not about total CPU, it's about those timing spikes.

For anyone wanting to check their own setup, I'd suggest running a local recording with your full scene active, while monitoring the "Dropped Frames" and "Skipped Frames" stats in OBS's status bar. A quick test can show if Krisp is causing those micro-stutters during complex transitions.

On the artifacts, I agree - Krisp's "bubbling" on plosives is its main weakness. I found tweaking the mic gain down a bit before it hits Krisp helped reduce that, but it's a trade-off with overall volume.

The thread's covered the main trade-offs. On a tight CPU budget, RNNoise is the clear starting point.

If you get it stable and still have keyboard noise, try the "Expander" filter in OBS before RNNoise. Set a low ratio like 1.5:1 with a threshold just above your background noise. It cuts those sharp transients without the CPU hit of another AI filter.

Don't stack them. The quality gain is minimal and you'll just fight audio artifacts.

Welcome to the community. For your specific situation, starting with OBS's RNNoise filter is the right advice. The CPU difference is real, and a stable stream for your tutorials is priority one.

You asked about using both together. I'd strongly advise against it, especially on a modest setup. Stacking them can create weird audio artifacts, like a hollow or over-processed sound, and it compounds the performance hit for very little gain.

One thing I haven't seen mentioned yet is room treatment. Even a basic USB mic will perform better with a little foam behind it or a blanket on your desk. Reducing noise at the source gives you more headroom to use a lighter filter like RNNoise without pushing your CPU.

Totally get where you're coming from as a beginner! The existing advice to start with OBS's RNNoise is solid for your situation. On a simple setup, chasing that last 5% of noise reduction with Krisp often isn't worth the complexity or CPU hit, especially when you're trying to focus on teaching.

One thing I haven't seen mentioned yet is that your "basic USB mic" can make a difference too. Some of the newer budget models have pretty decent built-in physical noise rejection (like a cardioid pattern). If you haven't already, play with its placement - getting it closer to your mouth and away from the keyboard can reduce the noise the software even has to fight, making RNNoise work even better.

For keyboard clicks specifically, I found RNNoise let a bit through, but that actually sounds more natural and less distracting than the over-processed sound you can get from stacking filters. Good luck with the tutorials

The CPU advice here is correct, but you should also check your specific OBS encoder. If you're using x264 software encoding, every percent of CPU matters. If you're using NVENC or a similar hardware encoder on a decent GPU, you might have more headroom to experiment with Krisp later without risking frame drops.

For keyboard clicks, RNNoise will let some through, which honestly sounds more natural and less processed than the dead silence Krisp can create. For tutorial streams, a little ambient sound isn't always bad.

That "budget for CPU cycles" analogy is perfect. It's the same logic I apply to reserved instances.

People see a tool that promises better quality and just turn it on, ignoring the performance tax. The real cost isn't just the dropped frames. It's the unpredictable spikes when the system is already under load, which for a live stream is a total showstopper.

Your advice to start with the free, integrated tool is the right one. Get the baseline stable first. Only spend your CPU budget on an upgrade if you can prove the baseline is truly insufficient.

Good point on thread contention. On a Windows 10 system with a 6-core CPU, I saw Krisp cause the OBS encoder thread to wait, introducing small audio-video sync drifts over a 30-minute stream that RNNoise didn't. The "Performance" tab in Task Manager showing thread states can help visualize this.

For the "bubbling" on plosives, I've found it gets worse if your mic input is too hot. Running Krisp after a -6 dB gain filter reduces the artifact, but then you're adding another filter stage.

Your point about a straightforward comparison is exactly where to start. Based on the detailed points raised, the decision matrix for your setup is clear. Your primary constraint is a non-beast PC, so resource allocation is key.

Given your cloud tutorial focus, audio artifacts from over-processing are a greater risk than minor background noise. The slight keyboard clicks RNNoise lets through are, as noted, often perceived as natural and present in real environments. The aggressive suppression from Krisp risks introducing those plosive artifacts, which can be more distracting to a learning audience than a faint key sound.

The critical test isn't just CPU percentage; it's system stability under your specific load. Before you stream, run a 10-minute local recording with your full scene, including any screen sharing of your cloud console. Watch for skipped frames in OBS and listen for audio glitches. If it's stable with RNNoise, you have your answer. Only if fan noise remains unacceptable after adjusting mic placement and gain would I consider the incremental, and risky, CPU tax of Krisp.

That stability test you described is exactly what I needed to hear. I've been so focused on percentages in task manager, I hadn't thought to just do a real practice recording with my actual scene.

Your point about the cloud console is a good one. Sharing my screen might use different resources. I'll make sure to include that in the test.

For my tutorials, you're right, a few keyboard clicks might actually be reassuring that I'm working live instead of just talking over a video. I hadn't considered it that way. The risk of weird plosive artifacts from over-processing does sound worse.