---
title: "Voice AI Latency Under Load: CallSphere <1s vs Vapi Spikes"
description: "CallSphere targets sub-1-second voice latency via OpenAI Realtime + server VAD. Vapi reports multi-second spikes under load. Architecture deep dive."
canonical: https://callsphere.ai/blog/voice-ai-latency-under-load-callsphere-vs-vapi
category: "Technical Guides"
tags: ["Voice AI Latency", "OpenAI Realtime", "Vapi Alternative", "CallSphere vs Vapi", "Server VAD", "Voice Architecture"]
author: "CallSphere Team"
published: 2026-04-17T00:00:00.000Z
updated: 2026-05-06T17:51:06.470Z
---

# Voice AI Latency Under Load: CallSphere <1s vs Vapi Spikes

> CallSphere targets sub-1-second voice latency via OpenAI Realtime + server VAD. Vapi reports multi-second spikes under load. Architecture deep dive.

## TL;DR

CallSphere targets **sub-1-second end-to-end voice latency** by running directly on the OpenAI Realtime API over WebSocket with **server-side VAD**, **PCM16 24kHz audio**, and a single low-jitter pipeline. Vapi.ai claims ** A2[Telephony 50-100ms]
    A2 --> A3[STT 150-300ms]
    A3 --> A4[Network to LLM 50-150ms]
    A4 --> A5[LLM 200-500ms]
    A5 --> A6[Network to TTS 50-150ms]
    A6 --> A7[TTS 200-400ms]
    A7 --> A8[Telephony 50-100ms]
    A8 --> A9[User hears reply]
    end
    subgraph CallSphere
    B1[User speaks] --> B2[Telephony 50-100ms]
    B2 --> B3[OpenAI Realtime 300-600ms]
    B3 --> B4[Telephony 50-100ms]
    B4 --> B5[User hears reply]
    end
```

## Why PCM16 24kHz Matters

Audio format choice matters more than most teams realize. Vapi's default pipeline often re-encodes audio between providers — Twilio mu-law in, Deepgram PCM, ElevenLabs MP3 streaming out, telephony mu-law back. Each re-encode adds buffer delay (typically 20-60 ms) and a small quality hit.

CallSphere uses **PCM16 at 24kHz** end-to-end inside the Realtime session. PCM16 is a raw, uncompressed format, so there is no codec dwell time. 24kHz is high enough to preserve consonant clarity (which dominates intelligibility) without the bitrate overhead of 48kHz studio. The result: lower buffering latency and crisper audio, especially on flaky networks.

## Why Server-Side VAD Matters Under Load

Voice Activity Detection (VAD) is what tells the platform when the user has stopped speaking. Two approaches exist:

- **Client-side VAD**: the browser or telephony layer detects silence and forwards a "user done" signal. Cheap, but jittery.
- **Server-side VAD**: the model itself decides when the user is done based on the audio stream. More accurate, harder to implement.

OpenAI Realtime ships **server-side VAD** as a first-class feature. CallSphere uses it, which means turn boundaries are detected by the same model that will respond, with no extra coordination overhead. Vapi-based stacks typically rely on the STT vendor's VAD signal, which adds round-trip and is more sensitive to background noise.

Under load, server-side VAD also avoids a class of bug where two specialist vendors disagree about whether the user has stopped speaking. CallSphere does not have to mediate that disagreement — there is only one decider.

## Head-to-Head: Latency Architecture

| Dimension | CallSphere | Vapi |
| --- | --- | --- |
| Vendor hops in path | 1 (OpenAI Realtime) | 3-4 (STT + LLM + TTS) |
| Audio format | PCM16 24kHz end-to-end | Re-encodes between vendors |
| VAD location | Server (OpenAI Realtime) | Usually STT-side |
| Median latency target | <1 second | <500ms claimed |
| P99 under load | 800-1500 ms | Multi-second reports |
| Recovery from vendor outage | OpenAI fail-over | Each vendor independent |

## Why Concurrency Concentrates The Difference

The latency difference is largest under concurrency. When 50 calls are happening simultaneously:

- **Vapi**: each call hits 3-4 vendor APIs. If any vendor's queue is hot, all calls suffer. Tail latency expands.
- **CallSphere**: each call is one OpenAI Realtime session. The Realtime API has its own concurrency limits, but they are predictable and controllable.

For a small business with 5 simultaneous calls, both architectures perform well. For a 100-call concurrent inbound spike, the architectural differences dominate the user experience.

## Practical Steps to Test Latency Yourself

If you are evaluating voice AI platforms, run a load test that mirrors production:

1. Place 20 simultaneous calls.
2. Record the time from user-end-of-utterance to agent-first-word for each turn.
3. Plot the distribution. Look at P50, P95, and P99.
4. Repeat at peak hours of the platform's region (typically US business hours UTC-7 to UTC-4).

You will see the architectural truth quickly. Marketing claims do not survive a real load test, but architecture does.

## FAQ

### Is <500ms latency on Vapi a fair claim?

The number is achievable in best-case isolated tests with the right vendor combination. Sustained P99 under concurrent load tells a different story.

### Why does CallSphere not advertise 300ms?

Honest measurement under load matters more than a glossy headline number. CallSphere's <1-second target is a real production figure measured over real customer calls, not a marketing artifact.

### What about WebRTC? Does it help?

Yes for some verticals. CallSphere's Real Estate platform uses WebRTC for browser-to-agent calls, which removes the telephony hop entirely. See the [features page](/features) for the WebRTC architecture details.

### Does the OpenAI Realtime API have its own latency issues?

It can spike during model rollouts. CallSphere version-pins the model (gpt-4o-realtime-preview-2025-06-03) to avoid surprise drift, and falls back to the previous version automatically during incidents.

### How does this affect international calls?

Both platforms are sensitive to geography because OpenAI's regional endpoints matter. CallSphere routes traffic through the closest healthy Realtime endpoint to keep the budget tight.

### Can I see latency metrics for my deployment?

Yes. CallSphere ships per-call latency metrics in the analytics dashboard. [Book a demo](/demo) and we will show you a live latency timeline for a real call.

## Ship a Voice AI Stack That Stays Fast Under Load

[Schedule a CallSphere demo](/demo) and run your own load test. The architectural difference shows up in the first 10 calls.

---

Source: https://callsphere.ai/blog/voice-ai-latency-under-load-callsphere-vs-vapi