Summary This source did not publish a separate summary. Review SKILL.md before using the skill.
AssemblyAI Cost Tuning
Overview
Optimize AssemblyAI costs through model selection, feature-aware billing, and usage monitoring. AssemblyAI charges per audio hour with add-on pricing for intelligence features.
Prerequisites
Actual Pricing (Pay-As-You-Go)
Speech-to-Text (Async)
Model Price per Hour Best For Best (Universal-3)$0.37/hr Highest accuracy, production Nano $0.12/hr High volume, cost-sensitive
Streaming Speech-to-Text
Model Price per Hour Universal Streaming $0.47/hr
Audio Intelligence Add-Ons
Feature Additional Cost per Hour Speaker Diarization $0.02/hr Sentiment Analysis $0.02/hr Entity Detection $0.08/hr Auto Highlights Included Content Safety $0.02/hr IAB Categories $0.02/hr Summarization Included (uses LeMUR) PII Redaction $0.02/hr PII Audio Redaction +processing time
LeMUR
Model Price per Input Token Price per Output Token Default ~$0.003/1K tokens ~$0.015/1K tokens
Instructions
Step 1: Cost Estimation Calculator interface CostEstimate {
baseTranscriptionCost: number;
featuresCost: number;
totalCost: number;
breakdown: Record<string, number>;
}
function estimateTranscriptionCost(
audioHours: number,
options: {
model?: 'best' | 'nano';
speakerLabels?: boolean;
sentimentAnalysis?: boolean;
entityDetection?: boolean;
contentSafety?: boolean;
iabCategories?: boolean;
piiRedaction?: boolean;
} = {}
): CostEstimate {
const model = options.model ?? 'best';
const baseRate = model === 'best' ? 0.37 : 0.12;
const baseCost = audioHours * baseRate;
const breakdown: Record<string, number> = {
[`transcription (${model})`]: baseCost,
};
let featuresCost = 0;
if (options.speakerLabels) {
const cost = audioHours * 0.02;
breakdown['speaker_labels'] = cost;
featuresCost += cost;
}
if (options.sentimentAnalysis) {
const cost = audioHours * 0.02;
breakdown['sentiment_analysis'] = cost;
featuresCost += cost;
}
if (options.entityDetection) {
const cost = audioHours * 0.08;
breakdown['entity_detection'] = cost;
featuresCost += cost;
}
if (options.contentSafety) {
const cost = audioHours * 0.02;
breakdown['content_safety'] = cost;
featuresCost += cost;
}
if (options.iabCategories) {
const cost = audioHours * 0.02;
breakdown['iab_categories'] = cost;
featuresCost += cost;
}
if (options.piiRedaction) {
const cost = audioHours * 0.02;
breakdown['pii_redaction'] = cost;
featuresCost += cost;
}
return {
baseTranscriptionCost: baseCost,
featuresCost,
totalCost: baseCost + featuresCost,
breakdown,
};
}
// Example: 100 hours with Best model + diarization + sentiment
const estimate = estimateTranscriptionCost(100, {
model: 'best',
speakerLabels: true,
sentimentAnalysis: true,
});
// Result: $37 (transcription) + $2 (speakers) + $2 (sentiment) = $41
Step 2: Model Selection Strategy import { AssemblyAI } from 'assemblyai';
const client = new AssemblyAI({
apiKey: process.env.ASSEMBLYAI_API_KEY!,
});
// Use Nano for high-volume, cost-sensitive workloads
// - 3x cheaper than Best ($0.12 vs $0.37)
// - Good enough for search indexing, keyword detection
const cheapTranscript = await client.transcripts.transcribe({
audio: audioUrl,
speech_model: 'nano',
});
// Use Best for critical, accuracy-sensitive workloads
// - Medical transcription, legal proceedings, compliance
// - Supports word_boost for domain terminology
const accurateTranscript = await client.transcripts.transcribe({
audio: audioUrl,
speech_model: 'best',
word_boost: ['specialized', 'domain', 'terms'],
boost_param: 'high',
});
Step 3: Feature Budget — Only Enable What You Need // EXPENSIVE: All features enabled ($0.37 + $0.16 = $0.53/hr)
const expensive = await client.transcripts.transcribe({
audio: audioUrl,
speech_model: 'best', // $0.37/hr
speaker_labels: true, // +$0.02/hr
sentiment_analysis: true, // +$0.02/hr
entity_detection: true, // +$0.08/hr
content_safety: true, // +$0.02/hr
iab_categories: true, // +$0.02/hr
});
// CHEAP: Only what's needed ($0.12 + $0.02 = $0.14/hr)
const cheap = await client.transcripts.transcribe({
audio: audioUrl,
speech_model: 'nano', // $0.12/hr
speaker_labels: true, // +$0.02/hr
// Skip features you don't use
});
Step 4: Usage Tracking class AssemblyAIUsageTracker {
private totalAudioHours = 0;
private totalCost = 0;
private transcriptionCount = 0;
track(audioDurationSeconds: number, model: 'best' | 'nano', features: string[]) {
const hours = audioDurationSeconds / 3600;
this.totalAudioHours += hours;
this.transcriptionCount++;
const estimate = estimateTranscriptionCost(hours, {
model,
speakerLabels: features.includes('speaker_labels'),
sentimentAnalysis: features.includes('sentiment_analysis'),
entityDetection: features.includes('entity_detection'),
contentSafety: features.includes('content_safety'),
iabCategories: features.includes('iab_categories'),
piiRedaction: features.includes('redact_pii'),
});
this.totalCost += estimate.totalCost;
return estimate;
}
getSummary() {
return {
totalAudioHours: this.totalAudioHours.toFixed(2),
totalCost: `$${this.totalCost.toFixed(2)}`,
transcriptionCount: this.transcriptionCount,
avgCostPerTranscription: `$${(this.totalCost / this.transcriptionCount).toFixed(4)}`,
};
}
}
Step 5: Cost Reduction Strategies Strategy Savings Trade-off Use Nano instead of Best 68% cheaper Slightly lower accuracy Disable unused features Up to $0.16/hr Missing insights Cache transcript results Eliminate re-fetch costs Stale data risk Use LeMUR instead of per-feature AI Often cheaper for summaries Different output format Pre-filter audio (skip silence) Proportional savings Requires preprocessing Batch with webhooks No savings, but better throughput More complex architecture
Step 6: Budget Alerts const MONTHLY_BUDGET = 100; // $100
const tracker = new AssemblyAIUsageTracker();
// After each transcription
const estimate = tracker.track(transcript.audio_duration ?? 0, 'best', ['speaker_labels']);
const summary = tracker.getSummary();
if (parseFloat(summary.totalCost.replace('$', '')) > MONTHLY_BUDGET * 0.8) {
console.warn(`Budget warning: ${summary.totalCost} of $${MONTHLY_BUDGET} used`);
// Send alert to Slack, email, etc.
}
Output
Accurate cost estimation with feature-level breakdown
Model selection strategy (Best vs. Nano)
Feature budgeting to eliminate unnecessary costs
Usage tracking with budget alerts
Cost reduction strategies ranked by impact
Error Handling Issue Cause Solution Unexpected high bill Entity detection enabled everywhere Audit features per endpoint Nano accuracy too low Wrong model for use case Switch critical paths to Best Budget exceeded No monitoring Implement usage tracker + alerts Double billing Re-transcribing same audio Cache transcript IDs, check before submitting
Resources
Next Steps For architecture patterns, see assemblyai-reference-architecture.
