Agent skill

Deepgram Cost Tuning

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Optimize Deepgram costs and usage for budget-conscious deployments.

MITAuto-check passedMedia & Creative

Install Deepgram Cost Tuning

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill deepgram-cost-tuning -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace deepgram-cost-tuning --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/deepgram-cost-tuning .claude/skills/deepgram-cost-tuning && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
deepgram-cost-tuning
GitHub stars
2.8k
Token cost
~3k tokens
SKILL.md length
329 words
Files
2 (incl. references)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Optimize Deepgram costs and usage for budget-conscious deployments.

  • Works in 5 steps: Budget-Aware Transcription Service → Reduce Billable Minutes with Audio… → Query Deepgram Usage API → …
  • Reducing transcription costs
  • SKILL.md covers Prerequisites, Examples, Overview and Deepgram Pricing (2026), plus 5 more sections
  • Calls ffmpeg; needs DEEPGRAM_API_KEY

What it does

Deepgram Cost Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize Deepgram costs and usage for budget-conscious deployments. Use when reducing transcription costs, implementing usage controls, or optimizing pricing tier utilization. Trigger: "deepgram cost", "reduce deepgram spending", "deepgram pricing", "deepgram budget", "optimize deepgram usage", "deepgram billing".

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/implementation.md`). Compatibility notes: Designed for Claude Code

It sits in Media & Creative, covering Transcription, Deployment and Pricing strategy. It works with Deepgram. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Reducing transcription costs
  • Implementing usage controls
  • Optimizing pricing tier utilization

Example prompts

  • “deepgram cost”
  • “reduce deepgram spending”
  • “deepgram pricing”
  • “/deepgram-cost-tuning”

Requirements

  • A credential in DEEPGRAM_API_KEY
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(ffmpeg:*)

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Budget-Aware Transcription Service
  2. Reduce Billable Minutes with Audio Preprocessing
  3. Query Deepgram Usage API
  4. Cost-Optimized Model Selection
  5. Feature Cost Awareness

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(ffmpeg:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • ffmpeg

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • deepgram.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • DEEPGRAM_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Deepgram Cost Tuning loads about 3k tokens when it runs, and up to ~5k if it reads all its reference files. Until then it costs about 84 tokens; SKILL.md has 329 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 329 words, ~2,959 tokens.

Download SKILL.mdSave it as .claude/skills/deepgram-cost-tuning/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
deepgram-cost-tuning
description
Optimize Deepgram costs and usage for budget-conscious deployments. Use when reducing transcription costs, implementing usage controls, or optimizing pricing tier utilization. Trigger: "deepgram cost", "reduce deepgram spending", "deepgram pricing", "deepgram budget", "optimize deepgram usage", "deepgram billing".
allowed-tools
Read, Write, Edit, Bash(ffmpeg:*)
compatibility
Designed for Claude Code
version
1.13.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, deepgram, cost-optimization, billing

Deepgram Cost Tuning

Prerequisites

  • An aggregate usage/cost baseline, approved budget owner, delivery/quality SLO, and data/retention policy.
  • Synthetic fixtures and a reversible optimization/change record.

Examples

Compare aggregate duration, model, concurrency, and error data in a development/staging workload, change one approved model or batching parameter, and observe the quality/latency/cost tradeoff. Revert if quality or reliability drops; do not reduce retention, privacy, or consent safeguards merely to lower spend.

Overview

Optimize Deepgram API costs through smart model selection, audio preprocessing to reduce billable minutes, usage monitoring via the Deepgram API, budget guardrails, and feature-aware cost estimation. Deepgram bills per audio minute processed.

Deepgram Pricing (2026)

ProductModelPrice/MinuteNotes
STT (Batch)Nova-3$0.0043Best accuracy
STT (Batch)Nova-2$0.0043Proven stable
STT (Streaming)Nova-3$0.0059Real-time
STT (Streaming)Nova-2$0.0059Real-time
STT (Batch)Base$0.0048Fastest
STT (Batch)Whisper$0.0048Multilingual
TTSAura-2Pay-per-characterSee TTS pricing
IntelligenceSummarize/Topics/SentimentIncluded with STTNo extra cost

Add-on costs:

  • Diarization: +$0.0044/min
  • Multichannel: billed per channel

Instructions

Step 1: Budget-Aware Transcription Service
typescript
import { createClient } from '@deepgram/sdk';

interface BudgetConfig {
  monthlyLimitUsd: number;
  warningThreshold: number;  // 0.0-1.0 (e.g., 0.8 = warn at 80%)
  costPerMinute: number;     // Base STT cost
}

class BudgetAwareTranscriber {
  private client: ReturnType<typeof createClient>;
  private config: BudgetConfig;
  private monthlySpendUsd = 0;
  private monthlyMinutes = 0;

  constructor(apiKey: string, config: BudgetConfig) {
    this.client = createClient(apiKey);
    this.config = config;
  }

  async transcribe(source: any, options: any) {
    // Estimate cost before transcription
    const estimatedCost = this.estimateCost(options);
    const projected = this.monthlySpendUsd + estimatedCost;

    if (projected > this.config.monthlyLimitUsd) {
      throw new Error(
        `Budget exceeded: $${this.monthlySpendUsd.toFixed(2)} spent, ` +
        `$${this.config.monthlyLimitUsd} limit`
      );
    }

    if (projected > this.config.monthlyLimitUsd * this.config.warningThreshold) {
      console.warn(
        `Budget warning: ${((projected / this.config.monthlyLimitUsd) * 100).toFixed(0)}% ` +
        `of $${this.config.monthlyLimitUsd} limit`
      );
    }

    const { result, error } = await this.client.listen.prerecorded.transcribeUrl(
      source, options
    );
    if (error) throw error;

    // Track actual usage
    const duration = result.metadata.duration / 60;  // Convert to minutes
    const actualCost = this.calculateCost(duration, options);
    this.monthlyMinutes += duration;
    this.monthlySpendUsd += actualCost;

    return result;
  }

  private estimateCost(options: any): number {
    // Conservative estimate — assume 5 minutes per file
    return this.calculateCost(5, options);
  }

  private calculateCost(minutes: number, options: any): number {
    let cost = minutes * this.config.costPerMinute;
    if (options.diarize) cost += minutes * 0.0044;  // Diarization add-on
    return cost;
  }

  getUsageSummary() {
    return {
      minutesUsed: this.monthlyMinutes.toFixed(1),
      spentUsd: this.monthlySpendUsd.toFixed(4),
      remainingUsd: (this.config.monthlyLimitUsd - this.monthlySpendUsd).toFixed(4),
      utilizationPercent: ((this.monthlySpendUsd / this.config.monthlyLimitUsd) * 100).toFixed(1),
    };
  }
}

// Usage:
const transcriber = new BudgetAwareTranscriber(process.env.DEEPGRAM_API_KEY!, {
  monthlyLimitUsd: 100,
  warningThreshold: 0.8,
  costPerMinute: 0.0043,
});
Step 2: Reduce Billable Minutes with Audio Preprocessing
bash
# Remove silence — can save 10-40% of billable minutes
ffmpeg -i input.wav \
  -af "silenceremove=stop_periods=-1:stop_duration=0.5:stop_threshold=-30dB" \
  -ar 16000 -ac 1 -acodec pcm_s16le \
  trimmed.wav

# Speed up audio (1.25x) — saves 20% of billable minutes
# Deepgram handles slightly sped-up audio well
ffmpeg -i input.wav \
  -filter:a "atempo=1.25" \
  -ar 16000 -ac 1 -acodec pcm_s16le \
  faster.wav
typescript
import { execSync } from 'child_process';

function measureSavings(inputPath: string) {
  // Get original duration
  const origDuration = parseFloat(
    execSync(`ffprobe -v quiet -show_entries format=duration -of csv=p=0 "${inputPath}"`)
      .toString().trim()
  );

  // Remove silence
  execSync(`ffmpeg -y -i "${inputPath}" \
    -af "silenceremove=stop_periods=-1:stop_duration=0.5:stop_threshold=-30dB" \
    -ar 16000 -ac 1 -acodec pcm_s16le /tmp/trimmed.wav 2>/dev/null`);

  const trimmedDuration = parseFloat(
    execSync(`ffprobe -v quiet -show_entries format=duration -of csv=p=0 /tmp/trimmed.wav`)
      .toString().trim()
  );

  const savings = ((1 - trimmedDuration / origDuration) * 100).toFixed(1);
  const costSaved = ((origDuration - trimmedDuration) / 60 * 0.0043).toFixed(4);

  console.log(`Original: ${origDuration.toFixed(1)}s`);
  console.log(`Trimmed: ${trimmedDuration.toFixed(1)}s`);
  console.log(`Savings: ${savings}% (${costSaved}/file at $0.0043/min)`);
}
Step 3: Query Deepgram Usage API
typescript
import { createClient } from '@deepgram/sdk';

async function getUsageDashboard(projectId: string) {
  const client = createClient(process.env.DEEPGRAM_API_KEY!);

  // Get usage for current month
  const now = new Date();
  const monthStart = new Date(now.getFullYear(), now.getMonth(), 1);

  const { result } = await client.manage.getUsage(projectId, {
    start: monthStart.toISOString(),
    end: now.toISOString(),
  });

  // Aggregate by model
  const byModel: Record<string, { minutes: number; cost: number }> = {};
  for (const entry of (result as any).results ?? []) {
    const model = entry.model ?? 'unknown';
    if (!byModel[model]) byModel[model] = { minutes: 0, cost: 0 };
    byModel[model].minutes += (entry.hours ?? 0) * 60 + (entry.minutes ?? 0);
  }

  console.log('=== Monthly Usage ===');
  for (const [model, data] of Object.entries(byModel)) {
    const cost = data.minutes * 0.0043;
    console.log(`${model}: ${data.minutes.toFixed(1)} min ($${cost.toFixed(2)})`);
  }

  // Monthly projection
  const dayOfMonth = now.getDate();
  const daysInMonth = new Date(now.getFullYear(), now.getMonth() + 1, 0).getDate();
  const totalMinutes = Object.values(byModel).reduce((s, d) => s + d.minutes, 0);
  const projectedMinutes = (totalMinutes / dayOfMonth) * daysInMonth;
  const projectedCost = projectedMinutes * 0.0043;

  console.log(`\nProjected monthly: ${projectedMinutes.toFixed(0)} min ($${projectedCost.toFixed(2)})`);
}
Step 4: Cost-Optimized Model Selection
typescript
function recommendModel(params: {
  qualityNeeded: 'high' | 'medium' | 'low';
  isRealtime: boolean;
  languages: string[];
  budgetPerMinute?: number;
}): { model: string; pricePerMin: number; reason: string } {
  const { qualityNeeded, isRealtime, languages, budgetPerMinute } = params;

  // Multilingual -> Whisper
  if (languages.length > 1 || !['en', 'es', 'fr', 'de'].includes(languages[0])) {
    return { model: 'whisper-large', pricePerMin: 0.0048, reason: 'Multilingual support' };
  }

  // Budget constraint
  if (budgetPerMinute !== undefined && budgetPerMinute < 0.005) {
    return { model: 'nova-2', pricePerMin: 0.0043, reason: 'Best price per quality' };
  }

  // Real-time -> Nova-3 (streaming price $0.0059/min)
  if (isRealtime) {
    return { model: 'nova-3', pricePerMin: 0.0059, reason: 'Best real-time accuracy' };
  }

  // Quality based
  switch (qualityNeeded) {
    case 'high':
      return { model: 'nova-3', pricePerMin: 0.0043, reason: 'Highest accuracy' };
    case 'medium':
      return { model: 'nova-2', pricePerMin: 0.0043, reason: 'Good accuracy, proven' };
    case 'low':
      return { model: 'base', pricePerMin: 0.0048, reason: 'Fastest processing' };
  }
}
Step 5: Feature Cost Awareness
typescript
// Feature cost breakdown per minute of audio
const featureCosts: Record<string, { cost: number; description: string }> = {
  // Free features (included with STT)
  smart_format:   { cost: 0,      description: 'Punctuation + paragraphs + numerals' },
  punctuate:      { cost: 0,      description: 'Punctuation only' },
  paragraphs:     { cost: 0,      description: 'Paragraph formatting' },
  summarize:      { cost: 0,      description: 'AI summary (included with STT)' },
  detect_topics:  { cost: 0,      description: 'Topic detection (included)' },
  sentiment:      { cost: 0,      description: 'Sentiment analysis (included)' },
  intents:        { cost: 0,      description: 'Intent recognition (included)' },
  redact:         { cost: 0,      description: 'PII redaction (included)' },

  // Paid add-ons
  diarize:        { cost: 0.0044, description: 'Speaker identification (+$0.0044/min)' },
  multichannel:   { cost: 0.0043, description: 'Per-channel billing (1x STT cost per channel)' },
};

function estimateJobCost(params: {
  durationMinutes: number;
  model: string;
  features: string[];
  channels?: number;
}): number {
  const baseCost = params.durationMinutes * 0.0043;
  let addOnCost = 0;

  for (const feature of params.features) {
    addOnCost += (featureCosts[feature]?.cost ?? 0) * params.durationMinutes;
  }

  // Multichannel: billed per channel
  const channelMultiplier = params.channels ?? 1;

  return (baseCost + addOnCost) * channelMultiplier;
}

// Example: 60 min meeting with diarization
// estimateJobCost({ durationMinutes: 60, model: 'nova-3', features: ['diarize'] })
// = (60 * 0.0043) + (60 * 0.0044) = $0.258 + $0.264 = $0.522

Output

  • Budget-aware transcription with auto-blocking
  • Audio preprocessing to reduce billable minutes
  • Usage dashboard via Deepgram API
  • Cost-optimized model recommendation
  • Feature cost breakdown with estimation

Cost Optimization Quick Wins

StrategySavingsEffort
Remove silence from audio10-40%Low (ffmpeg one-liner)
Disable diarization when not needed~50%Low (remove option)
Use callback for long filesIndirect (no timeouts)Low
Cache repeated transcriptions20-60%Medium (Redis)
Speed up audio 1.25x20%Low (ffmpeg)
Use Nova-2 instead of Nova-30% (same price)None
Batch pre-recorded vs streaming37% ($0.0043 vs $0.0059)Medium

Error Handling

IssueCauseSolution
Budget exceededNo controlsEnable budget check before transcription
Unexpected chargesDiarization always onMake diarization opt-in
Usage API emptyWrong project IDGet ID from getProjects()
Cost spikeBatch job without limitsSet concurrency limits + budget cap

Resources

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in skills/.curated/deepgram-cost-tuning of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • references/implementation.md

Open the folder on GitHubat commit cfae287

Compare with similar skills

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Deepgram JS Speech To Textdeepgram/deepgram-js-sdk276—~1.8kAutomated safety check: PassMIT
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Works with

Questions about Deepgram Cost Tuning

What does Deepgram Cost Tuning do?

Optimize Deepgram costs and usage for budget-conscious deployments. Deepgram Cost Tuning is an agent skill from jeremylongshore/tons-of-skills-marketplace. Optimize Deepgram costs and usage for budget-conscious deployments.

When should I use Deepgram Cost Tuning?

Deepgram Cost Tuning fits situations like: reducing transcription costs; implementing usage controls; optimizing pricing tier utilization.

How do I install Deepgram Cost Tuning in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill deepgram-cost-tuning -a claude-code`. Or copy the skill folder (skills/.curated/deepgram-cost-tuning in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/deepgram-cost-tuning in your project. Claude Code loads it when a task matches its description.

How do I install Deepgram Cost Tuning in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill deepgram-cost-tuning -a codex`. Or copy the skill folder (skills/.curated/deepgram-cost-tuning in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/deepgram-cost-tuning in your project. Codex loads it when a task matches its description.

Can I use Deepgram Cost Tuning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill deepgram-cost-tuning -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/deepgram-cost-tuning, .gemini/skills/deepgram-cost-tuning, .github/skills/deepgram-cost-tuning and .opencode/skills/deepgram-cost-tuning in your project.

What does Deepgram Cost Tuning need to run?

Going by SKILL.md and its folder, Deepgram Cost Tuning needs the command-line tools its instructions call (ffmpeg) and credentials named DEEPGRAM_API_KEY. Our summary lists: A credential in DEEPGRAM_API_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(ffmpeg:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Deepgram Cost Tuning access the network?

SKILL.md names 1 domain. As links in the text: deepgram.com. This is read from the text; nothing was executed.

Is Deepgram Cost Tuning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Deepgram Cost Tuning use?

Deepgram Cost Tuning is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Deepgram Cost Tuning use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.

What are the alternatives to Deepgram Cost Tuning?

Skills that share tags, products or a category with Deepgram Cost Tuning: Deepgram (Anil-matcha/awesome-muse-connectors, 1.3k stars), 9Router Speech-to-Text (decolua/9router, 31k stars), Deepgram JS Audio Intelligence (deepgram/deepgram-js-sdk, 276 stars) and Deepgram JS Speech To Text (deepgram/deepgram-js-sdk, 276 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Deepgram Cost Tuning?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.