From deepgram-pack
Optimize Deepgram costs and usage for budget-conscious deployments. Use when reducing transcription costs, implementing usage controls, or optimizing pricing tier utilization. Trigger with phrases like "deepgram cost", "reduce deepgram spending", "deepgram pricing", "deepgram budget", "optimize deepgram usage".
How this skill is triggered — by the user, by Claude, or both
Slash command
/deepgram-pack:deepgram-cost-tuningThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Optimize Deepgram usage and costs through smart model selection, audio preprocessing, and usage monitoring.
Optimize Deepgram usage and costs through smart model selection, audio preprocessing, and usage monitoring.
| Model | Price per Minute | Best For |
|---|---|---|
| Nova-2 | $0.0043 | General transcription |
| Nova | $0.0043 | General transcription |
| Whisper Cloud | $0.0048 | Multilingual |
| Enhanced | $0.0145 | Legacy support |
| Base | $0.0048 | Basic transcription |
Additional Features:
Choose the most cost-effective model for your use case.
Reduce audio duration and optimize format.
Track and control usage in real-time.
Avoid re-transcribing the same content.
// services/cost-optimized-transcription.ts
import { createClient } from '@deepgram/sdk';
interface CostConfig {
maxMonthlySpend: number;
warningThreshold: number; // percentage
model: string;
enabledFeatures: {
diarization: boolean;
smartFormat: boolean;
};
}
interface CostMetrics {
currentMonthMinutes: number;
currentMonthCost: number;
projectedMonthlyCost: number;
}
export class CostOptimizedTranscription {
private client;
private config: CostConfig;
private metrics: CostMetrics;
private modelCosts: Record<string, number> = {
'nova-2': 0.0043,
'nova': 0.0043,
'base': 0.0048,
'enhanced': 0.0145,
};
constructor(apiKey: string, config: Partial<CostConfig> = {}) {
this.client = createClient(apiKey);
this.config = {
maxMonthlySpend: config.maxMonthlySpend ?? 100,
warningThreshold: config.warningThreshold ?? 80,
model: config.model ?? 'nova-2',
enabledFeatures: config.enabledFeatures ?? {
diarization: false,
smartFormat: true,
},
};
this.metrics = {
currentMonthMinutes: 0,
currentMonthCost: 0,
projectedMonthlyCost: 0,
};
}
private calculateCost(durationMinutes: number): number {
let cost = durationMinutes * this.modelCosts[this.config.model];
if (this.config.enabledFeatures.diarization) {
cost += durationMinutes * 0.0044;
}
return cost;
}
private checkBudget(estimatedMinutes: number): void {
const estimatedCost = this.calculateCost(estimatedMinutes);
const projectedTotal = this.metrics.currentMonthCost + estimatedCost;
if (projectedTotal > this.config.maxMonthlySpend) {
throw new Error(`Budget exceeded. Current: $${this.metrics.currentMonthCost.toFixed(2)}, Estimated: $${estimatedCost.toFixed(2)}, Limit: $${this.config.maxMonthlySpend}`);
}
const percentage = (projectedTotal / this.config.maxMonthlySpend) * 100;
if (percentage >= this.config.warningThreshold) {
console.warn(`Budget warning: ${percentage.toFixed(1)}% of monthly limit used`);
}
}
async transcribe(audioUrl: string, estimatedDurationMinutes: number) {
this.checkBudget(estimatedDurationMinutes);
const startTime = Date.now();
const { result, error } = await this.client.listen.prerecorded.transcribeUrl(
{ url: audioUrl },
{
model: this.config.model,
smart_format: this.config.enabledFeatures.smartFormat,
diarize: this.config.enabledFeatures.diarization,
}
);
if (error) throw error;
// Track actual usage
const actualDuration = result.metadata.duration / 60; // seconds to minutes
const cost = this.calculateCost(actualDuration);
this.metrics.currentMonthMinutes += actualDuration;
this.metrics.currentMonthCost += cost;
return {
transcript: result.results.channels[0].alternatives[0].transcript,
metadata: {
duration: actualDuration,
cost,
model: this.config.model,
},
};
}
getMetrics(): CostMetrics & { budgetRemaining: number } {
return {
...this.metrics,
budgetRemaining: this.config.maxMonthlySpend - this.metrics.currentMonthCost,
};
}
}
// lib/audio-reducer.ts
import ffmpeg from 'fluent-ffmpeg';
interface ReductionOptions {
silenceThreshold: string; // dB
silenceMinDuration: number; // seconds
speed: number; // 1.0 = normal, 1.25 = 25% faster
}
export async function reduceDuration(
inputPath: string,
outputPath: string,
options: Partial<ReductionOptions> = {}
): Promise<{ originalDuration: number; reducedDuration: number; savings: number }> {
const {
silenceThreshold = '-30dB',
silenceMinDuration = 0.5,
speed = 1.0,
} = options;
return new Promise((resolve, reject) => {
let originalDuration = 0;
let reducedDuration = 0;
ffmpeg(inputPath)
.on('codecData', (data) => {
originalDuration = parseDuration(data.duration);
})
// Remove silence
.audioFilters([
`silenceremove=start_periods=1:start_silence=${silenceMinDuration}:start_threshold=${silenceThreshold}`,
`silenceremove=stop_periods=-1:stop_silence=${silenceMinDuration}:stop_threshold=${silenceThreshold}`,
// Optionally speed up
...(speed !== 1.0 ? [`atempo=${speed}`] : []),
])
.output(outputPath)
.on('end', () => {
ffmpeg.ffprobe(outputPath, (err, metadata) => {
if (err) return reject(err);
reducedDuration = metadata.format.duration || 0;
resolve({
originalDuration,
reducedDuration,
savings: ((originalDuration - reducedDuration) / originalDuration) * 100,
});
});
})
.on('error', reject)
.run();
});
}
function parseDuration(duration: string): number {
const parts = duration.split(':').map(Number);
return parts[0] * 3600 + parts[1] * 60 + parts[2];
}
// lib/usage-dashboard.ts
import { createClient } from '@deepgram/sdk';
interface UsageSummary {
period: { start: Date; end: Date };
totalMinutes: number;
totalCost: number;
byModel: Record<string, { minutes: number; cost: number }>;
byDay: Array<{ date: string; minutes: number; cost: number }>;
projections: {
monthlyMinutes: number;
monthlyCost: number;
};
}
export class UsageDashboard {
private client;
private projectId: string;
constructor(apiKey: string, projectId: string) {
this.client = createClient(apiKey);
this.projectId = projectId;
}
async getUsageSummary(daysBack = 30): Promise<UsageSummary> {
const end = new Date();
const start = new Date(end.getTime() - daysBack * 24 * 60 * 60 * 1000);
// Get usage data from Deepgram API
const { result, error } = await this.client.manage.getProjectUsageRequest(
this.projectId,
{
start: start.toISOString(),
end: end.toISOString(),
}
);
if (error) throw error;
// Aggregate data
const byModel: Record<string, { minutes: number; cost: number }> = {};
const byDay: Map<string, { minutes: number; cost: number }> = new Map();
let totalMinutes = 0;
let totalCost = 0;
for (const request of result.requests || []) {
const minutes = (request.duration || 0) / 60;
const model = request.model || 'unknown';
const cost = this.calculateCost(minutes, model);
const dateKey = new Date(request.created).toISOString().split('T')[0];
totalMinutes += minutes;
totalCost += cost;
if (!byModel[model]) {
byModel[model] = { minutes: 0, cost: 0 };
}
byModel[model].minutes += minutes;
byModel[model].cost += cost;
if (!byDay.has(dateKey)) {
byDay.set(dateKey, { minutes: 0, cost: 0 });
}
const day = byDay.get(dateKey)!;
day.minutes += minutes;
day.cost += cost;
}
// Calculate projections
const dailyAverage = totalMinutes / daysBack;
const daysInMonth = 30;
return {
period: { start, end },
totalMinutes,
totalCost,
byModel,
byDay: Array.from(byDay.entries()).map(([date, data]) => ({
date,
...data,
})),
projections: {
monthlyMinutes: dailyAverage * daysInMonth,
monthlyCost: (totalCost / daysBack) * daysInMonth,
},
};
}
private calculateCost(minutes: number, model: string): number {
const rates: Record<string, number> = {
'nova-2': 0.0043,
'nova': 0.0043,
'base': 0.0048,
'enhanced': 0.0145,
};
return minutes * (rates[model] || 0.0043);
}
}
// lib/cost-alerts.ts
import { UsageDashboard } from './usage-dashboard';
interface AlertConfig {
dailyLimit: number;
weeklyLimit: number;
monthlyLimit: number;
alertChannels: Array<'email' | 'slack' | 'webhook'>;
}
export class CostAlerts {
private dashboard: UsageDashboard;
private config: AlertConfig;
private alertsSent: Set<string> = new Set();
constructor(dashboard: UsageDashboard, config: Partial<AlertConfig> = {}) {
this.dashboard = dashboard;
this.config = {
dailyLimit: config.dailyLimit ?? 10,
weeklyLimit: config.weeklyLimit ?? 50,
monthlyLimit: config.monthlyLimit ?? 200,
alertChannels: config.alertChannels ?? ['email'],
};
}
async checkAndAlert(): Promise<void> {
const daily = await this.dashboard.getUsageSummary(1);
const weekly = await this.dashboard.getUsageSummary(7);
const monthly = await this.dashboard.getUsageSummary(30);
const alerts: string[] = [];
if (daily.totalCost > this.config.dailyLimit) {
alerts.push(`Daily spend ($${daily.totalCost.toFixed(2)}) exceeds limit ($${this.config.dailyLimit})`);
}
if (weekly.totalCost > this.config.weeklyLimit) {
alerts.push(`Weekly spend ($${weekly.totalCost.toFixed(2)}) exceeds limit ($${this.config.weeklyLimit})`);
}
if (monthly.totalCost > this.config.monthlyLimit) {
alerts.push(`Monthly spend ($${monthly.totalCost.toFixed(2)}) exceeds limit ($${this.config.monthlyLimit})`);
}
// Send alerts (deduplicated)
for (const alert of alerts) {
const alertKey = `${new Date().toDateString()}-${alert}`;
if (!this.alertsSent.has(alertKey)) {
await this.sendAlert(alert);
this.alertsSent.add(alertKey);
}
}
}
private async sendAlert(message: string): Promise<void> {
console.log(`COST ALERT: ${message}`);
for (const channel of this.config.alertChannels) {
switch (channel) {
case 'slack':
await this.sendSlackAlert(message);
break;
case 'email':
await this.sendEmailAlert(message);
break;
case 'webhook':
await this.sendWebhookAlert(message);
break;
}
}
}
private async sendSlackAlert(message: string): Promise<void> {
const webhookUrl = process.env.SLACK_WEBHOOK_URL;
if (!webhookUrl) return;
await fetch(webhookUrl, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
text: `Deepgram Cost Alert: ${message}`,
}),
});
}
private async sendEmailAlert(message: string): Promise<void> {
// Implement email sending
}
private async sendWebhookAlert(message: string): Promise<void> {
// Implement webhook sending
}
}
// lib/cost-aware-model.ts
interface ModelRecommendation {
model: string;
estimatedCost: number;
qualityLevel: 'high' | 'medium' | 'low';
reason: string;
}
export function recommendModel(params: {
audioDurationMinutes: number;
monthlyBudget: number;
currentMonthSpend: number;
qualityRequirement: 'high' | 'medium' | 'any';
}): ModelRecommendation {
const { audioDurationMinutes, monthlyBudget, currentMonthSpend, qualityRequirement } = params;
const budgetRemaining = monthlyBudget - currentMonthSpend;
const models = [
{ name: 'nova-2', rate: 0.0043, quality: 'high' as const },
{ name: 'nova', rate: 0.0043, quality: 'high' as const },
{ name: 'base', rate: 0.0048, quality: 'low' as const },
];
// Filter by quality requirement
const eligible = models.filter(m => {
if (qualityRequirement === 'high') return m.quality === 'high';
if (qualityRequirement === 'medium') return m.quality !== 'low';
return true;
});
// Find cheapest that fits budget
for (const model of eligible.sort((a, b) => a.rate - b.rate)) {
const cost = audioDurationMinutes * model.rate;
if (cost <= budgetRemaining) {
return {
model: model.name,
estimatedCost: cost,
qualityLevel: model.quality,
reason: `Best value within budget ($${budgetRemaining.toFixed(2)} remaining)`,
};
}
}
// Fallback to cheapest
const cheapest = eligible[0];
return {
model: cheapest.name,
estimatedCost: audioDurationMinutes * cheapest.rate,
qualityLevel: cheapest.quality,
reason: 'Warning: May exceed budget',
};
}
Proceed to deepgram-reference-architecture for architecture patterns.
npx claudepluginhub rowanbrooks100/claude-code-plugins-plus-skills --plugin deepgram-packGuides collaborative design exploration before implementation: explores context, asks clarifying questions, proposes approaches, and writes a design doc for user approval.
Creates structured, bite-sized implementation plans from specs or requirements before writing code. Useful for breaking down multi-step tasks into testable steps with file structure and task boundaries.
Synthesizes the current conversation into a structured spec (PRD) and publishes it to the project issue tracker with a ready-for-agent label, without interviewing the user.
8plugins reuse this skill
First indexed Jul 10, 2026
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