Switched from Krisp to OBS noise gate for streaming - which is better?

I'm a junior cloud admin at a mid-sized marketing firm. I handle our AWS infra, and while I don't stream, I manage a real-time video conferencing service for our clients. We run OBS in production for broadcasting our live events.

My take on this comes from managing the performance side:

**CPU Cost:** This was our biggest factor. In our testing, Krisp's AI noise cancelation added 8-12% CPU load per active mic on our streaming boxes. OBS's RNNoise suppressor added 2-3%, and the gate is negligible. If you're on a single-PC setup, that 6-9% delta can be the difference between dropping frames or not.
**Latency & Transparency:** Krisp introduces a processing delay, roughly 40-60ms in our setup. For a two-way conversation, that can feel off. OBS's gate and suppressor chain felt immediate, which is better for reactive, real-time banter.
**Consistency:** Krisp was a "set and forget" black box that handled random background noises (keyboard, door slams) amazingly. OBS's gate is dumb; it only cares about dB level. The suppressor helps, but we still got occasional thumps through on a high-sensitivity mic.
**Cost:** Krisp's pro subscription runs about $10/month per seat. OBS's solution is free. For our team of 5 regular streamers, that's a $600/year line item we avoided.

I'd recommend sticking with your OBS filter chain. The CPU savings and lower latency are worth it for a controlled environment. If you stream from a noisy coffee shop or have unpredictable background noise, Krisp's AI is still the better tool. Tell us about your typical ambient noise and if you've noticed any frame drops since switching.

Your filter chain looks solid for the trade-off you're targeting. The gate threshold is aggressive, which pairs well with RNNoise cleaning up the residual noise in your open segments.

From a backend perspective, you've essentially moved from a high-latency, high-resource external service to a low-latency, in-process filter pipeline. That's a classic architectural shift. The risk is that a noise gate is a binary state machine; it can clip speech onsets or tails if your room noise floor fluctuates. RNNoise helps, but it's not a full replacement for Krisp's continuous AI model in chaotic audio environments.

Have you logged any CPU usage difference on your streaming rig since the switch? That 6-9% delta user316 mentioned can be critical for encoding headroom.

Yeah, the shift to an in-scene workflow is the real benefit, I think. Once you get the gate dialed in, you're not managing a separate app just for audio.

But you're right to wonder about trade-offs. I've found the gate+suppressor combo can struggle with my mechanical keyboard unless I push the open threshold quite high, which then risks cutting off soft speech. Krisp was better at letting my voice through while nuking the constant keyclacks.

How's it handling your background noise now? Like a fan or AC kicking on?

The mechanical keyboard example is a perfect real-world test case. It highlights the fundamental difference between a threshold-based system and a continuous model.

A noise gate, no matter how well tuned, operates on amplitude. A consistent, sharp sound like a keyclack will frequently trip it open. Your options are indeed to raise the open threshold, which compromises vocal fidelity, or to layer in a suppressor like RNNoise to attenuate that specific frequency range after the gate opens.

Krisp's advantage there isn't just suppression, it's discrimination. It's trained to identify and isolate human vocal patterns versus percussive noises, which is why it can let your voice through while nuking the keyboard. The trade-off, as noted, is the latency and CPU cost of running that model.

For a fan or AC, the results are mixed. A constant, tonal hum is easier for RNNoise to target and subtract. However, if the fan has a variable speed causing amplitude fluctuations, it can again trigger the gate. Have you considered placing a dedicated high-pass filter before the gate in your chain? It can help by removing low-frequency rumble that contributes to the overall amplitude, making the gate's decision more focused on your vocal range.

That's a clean technical explanation, but it glosses over the privacy question inherent to using Krisp. "Trained to identify and isolate human vocal patterns" means your voice data is being processed by a model, likely in the cloud. For a hobby streamer, maybe it's fine. For anything resembling a business broadcast or internal comms, that's an external data processor you're introducing into your pipeline, with its own logging and retention policies.

The resource and latency trade-offs are real, but the compliance and data sovereignty implications of an AI audio service are the silent third rail in this discussion.

Oh wow, this is a great breakdown of your filter chain, thanks for sharing the specifics! I'm just getting into streaming for my small team's project updates, and I've been wrestling with this exact choice.

Seeing your Noise Gate thresholds is super helpful, because I've been scared to set mine that high. I'm in an apartment with street noise, so I've been keeping my gate super wide open at like -50 dB, but then my keyboard is crazy loud. I guess I need to find a middle ground like you did.

The resource usage part is what really caught my eye, though. I'm on a kinda old laptop, so even a few percent CPU matters. Have you actually noticed your computer running cooler or your stream being more stable since you switched from Krisp? That would be a huge win for me.

The "transparency" you like is the real benefit. Krisp is a black box, you throw audio in and hope for the best. With OBS's chain, you can see and control every stage. That said, your noise suppression at -20 dB is doing the heavy lifting now, not the gate.

I'd argue you haven't fully replaced Krisp, you've offloaded its core job to RNNoise. The gate is just a traffic cop. The trade-off is that RNNoise is dumber. It can't tell a keyboard from a consonant, it just murders frequencies. You'll get more artifacts on plosive sounds (p, t, k) than Krisp gave you.

Run a test: record a segment with constant background noise, like a fan, and speak softly. That's where your current stack will struggle. Krisp would probably handle it cleaner, for a CPU and privacy cost.

That's a very logical filter chain you've built, and moving to an in-process workflow is a smart architectural choice for control. Your gate threshold spread is narrower than many people start with, which is good for avoiding clipped syllables.

I agree completely on the transparency point. Working directly with the raw audio signal path in OBS gives you direct observability into what each stage is doing, which is absent with a third-party service like Krisp. It's the difference between monitoring system metrics through an agent versus having direct access to the host's telemetry.

However, your suppressor doing the "heavy lifting" at -20 dB is the key detail. That's quite an aggressive attenuation. It will handle constant noise like a fan, but you might introduce artifacts on sibilant sounds or plosives that Krisp's model would have processed more cleanly. Have you run a recording test with deliberate plosives ('p', 't', 'k' sounds) to check for distortion after the suppressor? That's the type of trade-off you're making for that lower CPU footprint.

You've hit on the core architectural choice here: moving from an external service to an integrated, observable pipeline. Your filter chain looks well-structured. That -26 dB open threshold is a solid middle ground; it's aggressive enough to gate out a lot of background noise but should avoid clipping most speech onsets.

I'd add one specific tuning consideration for that Noise Gate, based on your transparency goal. The 25ms attack time might be just a bit slow if you have quick, plosive speech sounds. You might find that the very start of words like "pat" or "kick" get slightly softened. Try a test recording with the attack dialed down to 10-15ms and see if it feels more responsive without introducing a click. It's a subtle tweak that can make the gate feel even more "in-line" and transparent.

Have you considered adding a high-pass filter before the gate? It can help by rolling off low-frequency rumble (like AC or traffic), so your gate is only making decisions on the vocal frequency range. That could let you tighten your thresholds even further without sacrificing clarity.

Good call on the attack time tweak, that's a detail I always forget to test. Faster attack can definitely help with those sharp plosives feeling snappier.

The high-pass idea is smart, it pre-filters the noise floor the gate has to deal with. I've used that trick for people on discord with fans in the room, it lets you set the gate threshold higher without clipping your own voice.

For plosive clicks though, sometimes a separate pop filter or a de-esser plugin after everything works better than pushing the gate too fast.

Your point about the signal flow transparency is well-taken, and your chain demonstrates a disciplined approach to audio processing. A mental model of the pipeline as a serial filter chain is exactly correct.

However, I'd examine the order of your Noise Suppression and Compressor. Placing the compressor after the suppression is standard for leveling the cleaned signal, but it can exaggerate the artifacts introduced by the aggressive -20 dB RNNoise attenuation, particularly on sibilance. The compressor will raise the level of those already-processed sounds.

Consider an alternate topology: Noise Gate -> Compressor -> Noise Suppression. This allows the compressor to act on a more dynamic signal before suppression, potentially reducing the workload on the RNNoise filter and yielding a smoother output. The trade-off is that the suppressor then processes a already-compressed signal, which may alter its effectiveness. It's an architectural change worth A/B testing with your specific voice and noise profile.

You're right to question the filter order. Moving the compressor ahead of the suppressor is a classic studio technique to tame peaks before they hit a noise reduction unit. In a digital chain, the risk is that the compressor raises the floor of the noise itself before suppression, making the suppressor's job harder.

A practical test would be to measure the output RMS and peak levels in both configurations with a consistent noise source. If the noise floor post-compressor is elevated, the suppressor's attenuation might need to be increased, which could loop back to creating more artifacts.

It's a balancing act between processing a dynamic signal versus a compressed one.

Transparency's overrated. You traded a black box for a stack of dumb knobs you'll spend hours tuning. That -20 dB suppressor is just a cruder, local version of what Krisp does in the cloud. You'll see the CPU savings, but wait until a truck drives by and RNNoise turns your voice into a robot.

Oh that filter chain layout is a great idea! I'm new to this and I've just been setting filters by trial and error in the UI.

> "transparency"

This is what got me to switch from a managed ELT tool to building my own Airbyte pipelines. Seeing the raw config is so much better than a magic "make it clean" button. Same principle, right?

Quick question about the gate thresholds: is there a reason you went with a 14 dB difference between close and open (-40 and -26)? I'm still figuring out how wide that window should be. Too narrow and it sounds choppy?