assemblyai-performance-tuning

name	assemblyai-performance-tuning
description	Optimize AssemblyAI API performance with caching, parallel processing, and model selection. Use when experiencing slow transcriptions, implementing caching strategies, or optimizing throughput for batch transcription workloads. Trigger with phrases like "assemblyai performance", "optimize assemblyai", "assemblyai latency", "assemblyai caching", "assemblyai slow", "assemblyai batch".
allowed-tools	Read, Write, Edit
version	1.5.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","ai","speech-to-text","assemblyai","transcription","performance"]
compatibility	Designed for Claude Code

AssemblyAI Performance Tuning

Overview

Optimize AssemblyAI transcription performance through model selection, parallel processing, caching, and webhook-based architectures.

Prerequisites

• assemblyai package installed
• Understanding of async patterns
• Redis or in-memory cache available (optional)

Latency Benchmarks (Actual)

Async Transcription

Audio Duration	Approx. Processing Time	Notes
30 seconds	~10-15 seconds	Includes queue time
5 minutes	~30-60 seconds	Scales sub-linearly
1 hour	~3-5 minutes	Depends on queue load
10 hours	~15-30 minutes	Max async duration

Streaming

Metric	Value
First partial transcript	~300ms (P50)
Final transcript latency	~500ms (P50)
End-of-turn detection	Automatic with endpointing

Model Speed vs. Accuracy

Model	Speed	Accuracy	Price/hr
nano	Fastest	Good	$0.12
best (Universal-3)	Standard	Highest	$0.37
nova-3 (streaming)	Real-time	High	$0.47
nova-3-pro (streaming)	Real-time	Highest	$0.47

Instructions

Step 1: Choose the Right Model

import { AssemblyAI } from 'assemblyai';

const client = new AssemblyAI({
  : process..!,
});


 accurate =  client..({
  : audioUrl,
  : ,
});


 fast =  client..({
  : audioUrl,
  : ,
});