[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F7E6641AFGH1K5SYYJB6M0":3,"skill-same:01M6F7E6641AFGH1K5SYYJB6M0":82},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":64,"ownerHtmlUrl":65,"ownerBlogUrl":6,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6F7E6641AFGH1K5SYYJB6M0","assemblyai-cost-tuning",null,"Optimize AssemblyAI costs through model selection, feature budgeting, and usage monitoring.\nUse when analyzing AssemblyAI billing, reducing transcription costs,\nor implementing usage monitoring and budget alerts.\nTrigger with phrases like \"assemblyai cost\", \"assemblyai billing\",\n\"reduce assemblyai costs\", \"assemblyai pricing\", \"assemblyai budget\".","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fjeremylongshore\u002Fclaude-code-plugins-plus-skills\u002Fassemblyai-cost-tuning\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000040","财务","PROFESSION","适用职业",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"assemblyai cost tuning","assemblyai","tuning","cost","budgeting","billing","io","feature","alerts","optimize","selection","model selection","read","model","text","Budget","analyzing","monitoring","speech","code","2026-11-10 10:12:30",false,"---\nname: assemblyai-cost-tuning\ndescription: |\n  Optimize AssemblyAI costs through model selection, feature budgeting, and usage monitoring.\n  Use when analyzing AssemblyAI billing, reducing transcription costs,\n  or implementing usage monitoring and budget alerts.\n  Trigger with phrases like \"assemblyai cost\", \"assemblyai billing\",\n  \"reduce assemblyai costs\", \"assemblyai pricing\", \"assemblyai budget\".\nallowed-tools: Read, Grep\nversion: 1.0.0\nlicense: MIT\nauthor: Jeremy Longshore \u003Cjeremy@intentsolutions.io>\ntags: [saas, ai, speech-to-text, assemblyai, transcription, cost]\ncompatible-with: claude-code\n---\n\n# AssemblyAI Cost Tuning\n\n## Overview\nOptimize AssemblyAI costs through model selection, feature-aware billing, and usage monitoring. AssemblyAI charges per audio hour with add-on pricing for intelligence features.\n\n## Prerequisites\n- Access to AssemblyAI billing dashboard at https:\u002F\u002Fwww.assemblyai.com\u002Fapp\n- Understanding of current usage patterns\n\n## Actual Pricing (Pay-As-You-Go)\n\n### Speech-to-Text (Async)\n| Model | Price per Hour | Best For |\n|-------|---------------|----------|\n| **Best** (Universal-3) | $0.37\u002Fhr | Highest accuracy, production |\n| **Nano** | $0.12\u002Fhr | High volume, cost-sensitive |\n\n### Streaming Speech-to-Text\n| Model | Price per Hour |\n|-------|---------------|\n| Universal Streaming | $0.47\u002Fhr |\n\n### Audio Intelligence Add-Ons\n| Feature | Additional Cost per Hour |\n|---------|-------------------------|\n| Speaker Diarization | $0.02\u002Fhr |\n| Sentiment Analysis | $0.02\u002Fhr |\n| Entity Detection | $0.08\u002Fhr |\n| Auto Highlights | Included |\n| Content Safety | $0.02\u002Fhr |\n| IAB Categories | $0.02\u002Fhr |\n| Summarization | Included (uses LeMUR) |\n| PII Redaction | $0.02\u002Fhr |\n| PII Audio Redaction | +processing time |\n\n### LeMUR\n| Model | Price per Input Token | Price per Output Token |\n|-------|----------------------|----------------------|\n| Default | ~$0.003\u002F1K tokens | ~$0.015\u002F1K tokens |\n\n## Instructions\n\n### Step 1: Cost Estimation Calculator\n\n```typescript\ninterface CostEstimate {\n  baseTranscriptionCost: number;\n  featuresCost: number;\n  totalCost: number;\n  breakdown: Record\u003Cstring, number>;\n}\n\nfunction estimateTranscriptionCost(\n  audioHours: number,\n  options: {\n    model?: 'best' | 'nano';\n    speakerLabels?: boolean;\n    sentimentAnalysis?: boolean;\n    entityDetection?: boolean;\n    contentSafety?: boolean;\n    iabCategories?: boolean;\n    piiRedaction?: boolean;\n  } = {}\n): CostEstimate {\n  const model = options.model ?? 'best';\n  const baseRate = model === 'best' ? 0.37 : 0.12;\n  const baseCost = audioHours * baseRate;\n\n  const breakdown: Record\u003Cstring, number> = {\n    [`transcription (${model})`]: baseCost,\n  };\n\n  let featuresCost = 0;\n\n  if (options.speakerLabels) {\n    const cost = audioHours * 0.02;\n    breakdown['speaker_labels'] = cost;\n    featuresCost += cost;\n  }\n  if (options.sentimentAnalysis) {\n    const cost = audioHours * 0.02;\n    breakdown['sentiment_analysis'] = cost;\n    featuresCost += cost;\n  }\n  if (options.entityDetection) {\n    const cost = audioHours * 0.08;\n    breakdown['entity_detection'] = cost;\n    featuresCost += cost;\n  }\n  if (options.contentSafety) {\n    const cost = audioHours * 0.02;\n    breakdown['content_safety'] = cost;\n    featuresCost += cost;\n  }\n  if (options.iabCategories) {\n    const cost = audioHours * 0.02;\n    breakdown['iab_categories'] = cost;\n    featuresCost += cost;\n  }\n  if (options.piiRedaction) {\n    const cost = audioHours * 0.02;\n    breakdown['pii_redaction'] = cost;\n    featuresCost += cost;\n  }\n\n  return {\n    baseTranscriptionCost: baseCost,\n    featuresCost,\n    totalCost: baseCost + featuresCost,\n    breakdown,\n  };\n}\n\n\u002F\u002F Example: 100 hours with Best model + diarization + sentiment\nconst estimate = estimateTranscriptionCost(100, {\n  model: 'best',\n  speakerLabels: true,\n  sentimentAnalysis: true,\n});\n\u002F\u002F Result: $37 (transcription) + $2 (speakers) + $2 (sentiment) = $41\n```\n\n### Step 2: Model Selection Strategy\n\n```typescript\nimport { AssemblyAI } from 'assemblyai';\n\nconst client = new AssemblyAI({\n  apiKey: process.env.ASSEMBLYAI_API_KEY!,\n});\n\n\u002F\u002F Use Nano for high-volume, cost-sensitive workloads\n\u002F\u002F - 3x cheaper than Best ($0.12 vs $0.37)\n\u002F\u002F - Good enough for search indexing, keyword detection\nconst cheapTranscript = await client.transcripts.transcribe({\n  audio: audioUrl,\n  speech_model: 'nano',\n});\n\n\u002F\u002F Use Best for critical, accuracy-sensitive workloads\n\u002F\u002F - Medical transcription, legal proceedings, compliance\n\u002F\u002F - Supports word_boost for domain terminology\nconst accurateTranscript = await client.transcripts.transcribe({\n  audio: audioUrl,\n  speech_model: 'best',\n  word_boost: ['specialized', 'domain', 'terms'],\n  boost_param: 'high',\n});\n```\n\n### Step 3: Feature Budget — Only Enable What You Need\n\n```typescript\n\u002F\u002F EXPENSIVE: All features enabled ($0.37 + $0.16 = $0.53\u002Fhr)\nconst expensive = await client.transcripts.transcribe({\n  audio: audioUrl,\n  speech_model: 'best',        \u002F\u002F $0.37\u002Fhr\n  speaker_labels: true,         \u002F\u002F +$0.02\u002Fhr\n  sentiment_analysis: true,     \u002F\u002F +$0.02\u002Fhr\n  entity_detection: true,       \u002F\u002F +$0.08\u002Fhr\n  content_safety: true,         \u002F\u002F +$0.02\u002Fhr\n  iab_categories: true,         \u002F\u002F +$0.02\u002Fhr\n});\n\n\u002F\u002F CHEAP: Only what's needed ($0.12 + $0.02 = $0.14\u002Fhr)\nconst cheap = await client.transcripts.transcribe({\n  audio: audioUrl,\n  speech_model: 'nano',         \u002F\u002F $0.12\u002Fhr\n  speaker_labels: true,         \u002F\u002F +$0.02\u002Fhr\n  \u002F\u002F Skip features you don't use\n});\n```\n\n### Step 4: Usage Tracking\n\n```typescript\nclass AssemblyAIUsageTracker {\n  private totalAudioHours = 0;\n  private totalCost = 0;\n  private transcriptionCount = 0;\n\n  track(audioDurationSeconds: number, model: 'best' | 'nano', features: string[]) {\n    const hours = audioDurationSeconds \u002F 3600;\n    this.totalAudioHours += hours;\n    this.transcriptionCount++;\n\n    const estimate = estimateTranscriptionCost(hours, {\n      model,\n      speakerLabels: features.includes('speaker_labels'),\n      sentimentAnalysis: features.includes('sentiment_analysis'),\n      entityDetection: features.includes('entity_detection'),\n      contentSafety: features.includes('content_safety'),\n      iabCategories: features.includes('iab_categories'),\n      piiRedaction: features.includes('redact_pii'),\n    });\n\n    this.totalCost += estimate.totalCost;\n\n    return estimate;\n  }\n\n  getSummary() {\n    return {\n      totalAudioHours: this.totalAudioHours.toFixed(2),\n      totalCost: `$${this.totalCost.toFixed(2)}`,\n      transcriptionCount: this.transcriptionCount,\n      avgCostPerTranscription: `$${(this.totalCost \u002F this.transcriptionCount).toFixed(4)}`,\n    };\n  }\n}\n```\n\n### Step 5: Cost Reduction Strategies\n\n| Strategy | Savings | Trade-off |\n|----------|---------|-----------|\n| Use Nano instead of Best | 68% cheaper | Slightly lower accuracy |\n| Disable unused features | Up to $0.16\u002Fhr | Missing insights |\n| Cache transcript results | Eliminate re-fetch costs | Stale data risk |\n| Use LeMUR instead of per-feature AI | Often cheaper for summaries | Different output format |\n| Pre-filter audio (skip silence) | Proportional savings | Requires preprocessing |\n| Batch with webhooks | No savings, but better throughput | More complex architecture |\n\n### Step 6: Budget Alerts\n\n```typescript\nconst MONTHLY_BUDGET = 100; \u002F\u002F $100\nconst tracker = new AssemblyAIUsageTracker();\n\n\u002F\u002F After each transcription\nconst estimate = tracker.track(transcript.audio_duration ?? 0, 'best', ['speaker_labels']);\nconst summary = tracker.getSummary();\n\nif (parseFloat(summary.totalCost.replace('$', '')) > MONTHLY_BUDGET * 0.8) {\n  console.warn(`Budget warning: ${summary.totalCost} of $${MONTHLY_BUDGET} used`);\n  \u002F\u002F Send alert to Slack, email, etc.\n}\n```\n\n## Output\n- Accurate cost estimation with feature-level breakdown\n- Model selection strategy (Best vs. Nano)\n- Feature budgeting to eliminate unnecessary costs\n- Usage tracking with budget alerts\n- Cost reduction strategies ranked by impact\n\n## Error Handling\n| Issue | Cause | Solution |\n|-------|-------|----------|\n| Unexpected high bill | Entity detection enabled everywhere | Audit features per endpoint |\n| Nano accuracy too low | Wrong model for use case | Switch critical paths to Best |\n| Budget exceeded | No monitoring | Implement usage tracker + alerts |\n| Double billing | Re-transcribing same audio | Cache transcript IDs, check before submitting |\n\n## Resources\n- [AssemblyAI Pricing](https:\u002F\u002Fwww.assemblyai.com\u002Fpricing)\n- [AssemblyAI Billing Dashboard](https:\u002F\u002Fwww.assemblyai.com\u002Fapp)\n- [Speech Model Comparison](https:\u002F\u002Fwww.assemblyai.com\u002Fdocs\u002Fspeech-to-text\u002Fspeech-recognition)\n\n## Next Steps\nFor architecture patterns, see `assemblyai-reference-architecture`.\n\n","# AssemblyAI Cost Tuning\n\n## Overview\nOptimize AssemblyAI costs through model selection, feature-aware billing, and usage monitoring. AssemblyAI charges per audio hour with add-on pricing for intelligence features.\n\n## Prerequisites\n- Access to AssemblyAI billing dashboard at https:\u002F\u002Fwww.assemblyai.com\u002Fapp\n- Understanding of current usage patterns\n\n## Actual Pricing (Pay-As-You-Go)\n\n### Speech-to-Text (Async)\n| Model | Price per Hour | Best For |\n|-------|---------------|----------|\n| **Best** (Universal-3) | $0.37\u002Fhr | Highest accuracy, production |\n| **Nano** | $0.12\u002Fhr | High volume, cost-sensitive |\n\n### Streaming Speech-to-Text\n| Model | Price per Hour |\n|-------|---------------|\n| Universal Streaming | $0.47\u002Fhr |\n\n### Audio Intelligence Add-Ons\n| Feature | Additional Cost per Hour |\n|---------|-------------------------|\n| Speaker Diarization | $0.02\u002Fhr |\n| Sentiment Analysis | $0.02\u002Fhr |\n| Entity Detection | $0.08\u002Fhr |\n| Auto Highlights | Included |\n| Content Safety | $0.02\u002Fhr |\n| IAB Categories | $0.02\u002Fhr |\n| Summarization | Included (uses LeMUR) |\n| PII Redaction | $0.02\u002Fhr |\n| PII Audio Redaction | +processing time |\n\n### LeMUR\n| Model | Price per Input Token | Price per Output Token |\n|-------|----------------------|----------------------|\n| Default | ~$0.003\u002F1K tokens | ~$0.015\u002F1K tokens |\n\n## Instructions\n\n### Step 1: Cost Estimation Calculator\n\n```typescript\ninterface CostEstimate {\n  baseTranscriptionCost: number;\n  featuresCost: number;\n  totalCost: number;\n  breakdown: Record\u003Cstring, number>;\n}\n\nfunction estimateTranscriptionCost(\n  audioHours: number,\n  options: {\n    model?: 'best' | 'nano';\n    speakerLabels?: boolean;\n    sentimentAnalysis?: boolean;\n    entityDetection?: boolean;\n    contentSafety?: boolean;\n    iabCategories?: boolean;\n    piiRedaction?: boolean;\n  } = {}\n): CostEstimate {\n  const model = options.model ?? 'best';\n  const baseRate = model === 'best' ? 0.37 : 0.12;\n  const baseCost = audioHours * baseRate;\n\n  const breakdown: Record\u003Cstring, number> = {\n    [`transcription (${model})`]: baseCost,\n  };\n\n  let featuresCost = 0;\n\n  if (options.speakerLabels) {\n    const cost = audioHours * 0.02;\n    breakdown['speaker_labels'] = cost;\n    featuresCost += cost;\n  }\n  if (options.sentimentAnalysis) {\n    const cost = audioHours * 0.02;\n    breakdown['sentiment_analysis'] = cost;\n    featuresCost += cost;\n  }\n  if (options.entityDetection) {\n    const cost = audioHours * 0.08;\n    breakdown['entity_detection'] = cost;\n    featuresCost += cost;\n  }\n  if (options.contentSafety) {\n    const cost = audioHours * 0.02;\n    breakdown['content_safety'] = cost;\n    featuresCost += cost;\n  }\n  if (options.iabCategories) {\n    const cost = audioHours * 0.02;\n    breakdown['iab_categories'] = cost;\n    featuresCost += cost;\n  }\n  if (options.piiRedaction) {\n    const cost = audioHours * 0.02;\n    breakdown['pii_redaction'] = cost;\n    featuresCost += cost;\n  }\n\n  return {\n    baseTranscriptionCost: baseCost,\n    featuresCost,\n    totalCost: baseCost + featuresCost,\n    breakdown,\n  };\n}\n\n\u002F\u002F Example: 100 hours with Best model + diarization + sentiment\nconst estimate = estimateTranscriptionCost(100, {\n  model: 'best',\n  speakerLabels: true,\n  sentimentAnalysis: true,\n});\n\u002F\u002F Result: $37 (transcription) + $2 (speakers) + $2 (sentiment) = $41\n```\n\n### Step 2: Model Selection Strategy\n\n```typescript\nimport { AssemblyAI } from 'assemblyai';\n\nconst client = new AssemblyAI({\n  apiKey: process.env.ASSEMBLYAI_API_KEY!,\n});\n\n\u002F\u002F Use Nano for high-volume, cost-sensitive workloads\n\u002F\u002F - 3x cheaper than Best ($0.12 vs $0.37)\n\u002F\u002F - Good enough for search indexing, keyword detection\nconst cheapTranscript = await client.transcripts.transcribe({\n  audio: audioUrl,\n  speech_model: 'nano',\n});\n\n\u002F\u002F Use Best for critical, accuracy-sensitive workloads\n\u002F\u002F - Medical transcription, legal proceedings, compliance\n\u002F\u002F - Supports word_boost for domain terminology\nconst accurateTranscript = await client.transcripts.transcribe({\n  audio: audioUrl,\n  speech_model: 'best',\n  word_boost: ['specialized', 'domain', 'terms'],\n  boost_param: 'high',\n});\n```\n\n### Step 3: Feature Budget — Only Enable What You Need\n\n```typescript\n\u002F\u002F EXPENSIVE: All features enabled ($0.37 + $0.16 = $0.53\u002Fhr)\nconst expensive = await client.transcripts.transcribe({\n  audio: audioUrl,\n  speech_model: 'best',        \u002F\u002F $0.37\u002Fhr\n  speaker_labels: true,         \u002F\u002F +$0.02\u002Fhr\n  sentiment_analysis: true,     \u002F\u002F +$0.02\u002Fhr\n  entity_detection: true,       \u002F\u002F +$0.08\u002Fhr\n  content_safety: true,         \u002F\u002F +$0.02\u002Fhr\n  iab_categories: true,         \u002F\u002F +$0.02\u002Fhr\n});\n\n\u002F\u002F CHEAP: Only what's needed ($0.12 + $0.02 = $0.14\u002Fhr)\nconst cheap = await client.transcripts.transcribe({\n  audio: audioUrl,\n  speech_model: 'nano',         \u002F\u002F $0.12\u002Fhr\n  speaker_labels: true,         \u002F\u002F +$0.02\u002Fhr\n  \u002F\u002F Skip features you don't use\n});\n```\n\n### Step 4: Usage Tracking\n\n```typescript\nclass AssemblyAIUsageTracker {\n  private totalAudioHours = 0;\n  private totalCost = 0;\n  private transcriptionCount = 0;\n\n  track(audioDurationSeconds: number, model: 'best' | 'nano', features: string[]) {\n    const hours = audioDurationSeconds \u002F 3600;\n    this.totalAudioHours += hours;\n    this.transcriptionCount++;\n\n    const estimate = estimateTranscriptionCost(hours, {\n      model,\n      speakerLabels: features.includes('speaker_labels'),\n      sentimentAnalysis: features.includes('sentiment_analysis'),\n      entityDetection: features.includes('entity_detection'),\n      contentSafety: features.includes('content_safety'),\n      iabCategories: features.includes('iab_categories'),\n      piiRedaction: features.includes('redact_pii'),\n    });\n\n    this.totalCost += estimate.totalCost;\n\n    return estimate;\n  }\n\n  getSummary() {\n    return {\n      totalAudioHours: this.totalAudioHours.toFixed(2),\n      totalCost: `$${this.totalCost.toFixed(2)}`,\n      transcriptionCount: this.transcriptionCount,\n      avgCostPerTranscription: `$${(this.totalCost \u002F this.transcriptionCount).toFixed(4)}`,\n    };\n  }\n}\n```\n\n### Step 5: Cost Reduction Strategies\n\n| Strategy | Savings | Trade-off |\n|----------|---------|-----------|\n| Use Nano instead of Best | 68% cheaper | Slightly lower accuracy |\n| Disable unused features | Up to $0.16\u002Fhr | Missing insights |\n| Cache transcript results | Eliminate re-fetch costs | Stale data risk |\n| Use LeMUR instead of per-feature AI | Often cheaper for summaries | Different output format |\n| Pre-filter audio (skip silence) | Proportional savings | Requires preprocessing |\n| Batch with webhooks | No savings, but better throughput | More complex architecture |\n\n### Step 6: Budget Alerts\n\n```typescript\nconst MONTHLY_BUDGET = 100; \u002F\u002F $100\nconst tracker = new AssemblyAIUsageTracker();\n\n\u002F\u002F After each transcription\nconst estimate = tracker.track(transcript.audio_duration ?? 0, 'best', ['speaker_labels']);\nconst summary = tracker.getSummary();\n\nif (parseFloat(summary.totalCost.replace('$', '')) > MONTHLY_BUDGET * 0.8) {\n  console.warn(`Budget warning: ${summary.totalCost} of $${MONTHLY_BUDGET} used`);\n  \u002F\u002F Send alert to Slack, email, etc.\n}\n```\n\n## Output\n- Accurate cost estimation with feature-level breakdown\n- Model selection strategy (Best vs. Nano)\n- Feature budgeting to eliminate unnecessary costs\n- Usage tracking with budget alerts\n- Cost reduction strategies ranked by impact\n\n## Error Handling\n| Issue | Cause | Solution |\n|-------|-------|----------|\n| Unexpected high bill | Entity detection enabled everywhere | Audit features per endpoint |\n| Nano accuracy too low | Wrong model for use case | Switch critical paths to Best |\n| Budget exceeded | No monitoring | Implement usage tracker + alerts |\n| Double billing | Re-transcribing same audio | Cache transcript IDs, check before submitting |\n\n## Resources\n- [AssemblyAI Pricing](https:\u002F\u002Fwww.assemblyai.com\u002Fpricing)\n- [AssemblyAI Billing Dashboard](https:\u002F\u002Fwww.assemblyai.com\u002Fapp)\n- [Speech Model Comparison](https:\u002F\u002Fwww.assemblyai.com\u002Fdocs\u002Fspeech-to-text\u002Fspeech-recognition)\n\n## Next Steps\nFor architecture patterns, see `assemblyai-reference-architecture`.\n\n",{"name":5,"tags":49,"author":54,"license":15,"version":55,"description":56,"allowed-tools":57,"compatible-with":58},[50,51,52,25,53,27],"saas","ai","speech-to-text","transcription","Jeremy Longshore 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