Anyone tested Krisp with K8s voice bots? Real-world latency issues?

So, the marketing copy for Krisp talks a big game about eliminating background noise in any communication scenario. Naturally, my first thought when evaluating any "AI-powered" cloud service these days is: "Great, let's strap this onto something already complex and see where it breaks." In our case, that something is a Kubernetes-based voice bot infrastructure handling customer service calls.

We've been prototyping a migration from a simpler, albeit more expensive, managed telephony provider to a self-hosted Asterisk/FreeSWITCH setup on K8s, with our bot logic in Python pods. The idea of using Krisp's API to clean up inbound customer audio (traffic noise, kids screaming, etc.) before our NLP processes it seemed, on paper, like a cost-saver versus building our own noise suppression models. But the latency implications are... interesting, to say the least.

Here's the naive architecture we started with:

```yaml
# Simplified Pod spec snippet for our bot worker
containers:
- name: bot-processor
image: our-bot:latest
env:
- name: KRISP_API_KEY
valueFrom:
secretKeyRef:
name: krisp-secret
key: apiKey
- name: AUDIO_PROCESSING_ENDPOINT
value: "https://api.krisp.ai/v2/noise/cancel"
```

The flow is: audio chunk received -> HTTP POST to Krisp API with chunk -> wait -> receive cleaned chunk -> feed to STT/NLP. The problem isn't the API call itself, it's the cumulative effect. A real-time audio stream for telephony uses chunks every 20-40ms. Adding a synchronous HTTP hop, even to a geographically close region, introduces a variable latency that murders your real-time feel. You start getting into jitter buffer hell.

I've run some **utterly unscientific but telling** tests from a pod in us-east-1 to Krisp's nearest endpoint:
* P95 latency for a ~50ms audio frame: ~120ms.
* That's without any network policy overhead, service mesh sidecars (don't get me started), or node pressure.
* Now multiply that by two if you need to clean the bot's *response* audio before sending it back to the human.

So, my question to the crowd: has anyone actually deployed this in a production, latency-sensitive voice bot scenario on K8s? Not for recorded messages, but for live dialogue. Did you:
1. Bite the bullet and accept the latency, adjusting your bot's response timing to feel more "deliberate" (a.k.a. sluggish)?
2. Implement some insane async batching mechanism that probably broke the real-time stream anyway?
3. Fall back to running a Krisp-like model directly in your pod, trading CPU cost for latency?
4. Or just conclude that the marginal improvement in NLP accuracy from cleaner audio wasn't worth the added complexity and point-of-failure?

I'm particularly skeptical of the "real-time" claims when a third-party API over the public internet becomes a critical path component. The failure modes are delightful: Krisp API has a bad day? Your bots go silent. DNS hiccup? Latency spikes. Your cloud provider's network has a transient issue? Unintelligible responses.

Before I get pressured by the "innovation" team to push this to production, I'd love some real-world war stories. Bonus points if you've done the cost analysis on Krisp API calls per concurrent call vs. just provisioning more robust node pools to handle a local noise model.

-- cynical ops