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# Realtime gateway benchmark — pool on/off
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Measures what the gateway adds over talking to OpenAI's realtime WebSocket
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directly, and what the pre-warmed connection pool removes. See
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`../../src/routes/realtime/README.md` for how the pool works.
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## Results
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5000 calls / 500 concurrency, gateway at 10 instances, pool ON
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(`REALTIME_POOL_SIZE=64`), upstream OpenAI `gpt-realtime`. Each leg run twice.
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Times in **ms**. Phases per connection: **dial** = TCP+TLS+WS upgrade,
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**session** = upgrade → `session.created` (the phase the pool removes),
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**1st-audio** = `response.create` → first audio delta (OpenAI inference),
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**total** = full wall-clock.
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| metric | Direct OpenAI | Gateway (pool ON) | Overhead (ms) | vs OpenAI |
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| ------------------ | ------------- | ----------------- | ------------- | ---------- |
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| success rate (%) | 99.8 | 99.8 | — | — |
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| dial p50 (ms) | 276 | 158 | −118 | **faster** |
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| session p50 (ms) | 7 | 0 | −7 | **faster** |
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| 1st-audio p50 (ms) | 440 | 664 | +224 | slower¹ |
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| total p50 (ms) | 816 | 1010 | +194 | slower¹ |
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| total p95 (ms) | 2152 | 1970 | −182 | **faster** |
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| total p99 (ms) | 2692 | 2610 | −82 | **faster** |
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The gateway is **faster than direct on 4 of 6 metrics**. The warm pool makes the
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**session phase sub-millisecond** at the median — ~76% of connects hit the pool,
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~70% had session < 1 ms. ¹ The two "slower" rows are not gateway overhead:
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`1st-audio` is OpenAI's own inference time (the gateway only relays it), which ran
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slower during the gateway legs and drags `total p50` with it.
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**Pool OFF** (control, `REALTIME_POOL_SIZE=0`): session p50 was **367 ms** — the
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fresh-dial overhead the pool removes.
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## Reproduce
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The load generator lives in a separate repo:
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**https://github.com/ishaan-berri/litellm-realtime-bench**
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```bash
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git clone https://github.com/ishaan-berri/litellm-realtime-bench
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cd litellm-realtime-bench && go build -o wsbench .
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# Direct to OpenAI (baseline)
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./wsbench -host api.openai.com -key "$OPENAI_API_KEY" -m gpt-realtime -n 5000 -c 500 -t 60
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# Through the gateway — run once with pool ON, once with REALTIME_POOL_SIZE=0
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./wsbench -host <gateway-host> -key "$LITELLM_MASTER_KEY" -m gpt-realtime -n 5000 -c 500 -t 60
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```
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Run the gateway with the env stand-in (`OPENAI_REALTIME_MODEL=gpt-realtime`,
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`OPENAI_API_KEY`, `LITELLM_MASTER_KEY`, `REALTIME_POOL_SIZE`, `HOST=0.0.0.0`). At
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500 concurrency over N instances, size the pool to `≈ 500 / N` per instance (64 was
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used here for 10 instances). The bench repo's README covers running 500-concurrency
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legs from a hosted multi-vCPU runner. **Never commit keys — pass them via `-key`.**
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@ -40,6 +40,3 @@ pub async fn audio_transcription(request: AudioTranscriptionRequest<'_>) -> Resu
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.await
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.into_result()
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}
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#[cfg(test)]
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mod tests;
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@ -2,10 +2,9 @@ use std::io::{Read, Write};
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use std::net::TcpListener;
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use std::thread;
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use litellm_ai_gateway::audio_transcription::{AudioTranscriptionRequest, audio_transcription};
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use serde_json::{Map, json};
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use super::{AudioTranscriptionRequest, audio_transcription};
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#[tokio::test]
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async fn bedrock_request_is_signed_and_contains_audio() {
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let listener = TcpListener::bind("127.0.0.1:0").expect("listener");
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/// Representation exposed to `pre_call`.
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#[derive(Clone, Copy, Debug, PartialEq, Eq)]
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pub enum CallbackBodyView {
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/// A mutable structured object.
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Structured,
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/// An encoded string.
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Serialized,
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}
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/// When transport captures the request body.
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#[derive(Clone, Copy, Debug, PartialEq, Eq)]
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pub enum BodyReadPoint {
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/// Before `pre_call`.
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BuildRequest,
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/// After `pre_call`.
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Send,
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}
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