Groq Cost Tuning

Overview

Optimize Groq inference costs through smart model routing, token minimization, and caching. Groq pricing is already extremely competitive, but at high volume the savings from routing classification to 8B vs 70B are 12x per request.

Groq Pricing (per million tokens)

llama-3.1-8b-instant

llama-3.3-70b-versatile

llama-3.3-70b-specdec

meta-llama/llama-4-scout-17b-16e-instruct

whisper-large-v3-turbo

Check current pricing at groq.com/pricing.

Instructions

Step 1: Smart Model Routing

import Groq from "groq-sdk";

const groq = new Groq();

// Route to cheapest model that meets quality requirements
interface ModelConfig {
  model: string;
  inputCostPer1M: number;
  outputCostPer1M: number;
}

const ROUTING: Record<string, ModelConfig> = {
  classification: { model: "llama-3.1-8b-instant", inputCostPer1M: 0.05, outputCostPer1M: 0.08 },
  extraction:     { model: "llama-3.1-8b-instant", inputCostPer1M: 0.05, outputCostPer1M: 0.08 },
  summarization:  { model: "llama-3.1-8b-instant", inputCostPer1M: 0.05, outputCostPer1M: 0.08 },
  reasoning:      { model: "llama-3.3-70b-versatile", inputCostPer1M: 0.59, outputCostPer1M: 0.79 },
  codeReview:     { model: "llama-3.3-70b-versatile", inputCostPer1M: 0.59, outputCostPer1M: 0.79 },
  chat:           { model: "llama-3.3-70b-versatile", inputCostPer1M: 0.59, outputCostPer1M: 0.79 },
  vision:         { model: "meta-llama/llama-4-scout-17b-16e-instruct", inputCostPer1M: 0.11, outputCostPer1M: 0.34 },
};

function getModel(useCase: string): string {
  return ROUTING[useCase]?.model || "llama-3.1-8b-instant";
}

// Classification on 8B: $0.05/M  vs  70B: $0.59/M  =  12x savings

Step 2: Minimize Tokens Per Request

// COST SAVINGS: Reduce system prompt tokens
// Groq charges for BOTH input and output tokens

// Verbose system prompt: ~200 tokens ($0.012 per 1000 calls on 70B)
const expensive = "You are a highly skilled AI assistant specializing in text classification. When given a piece of text, carefully analyze the sentiment, considering tone, word choice, connotation...";

// Concise system prompt: ~15 tokens ($0.001 per 1000 calls on 70B)
const cheap = "Classify sentiment: positive/negative/neutral. One word.";

// COST SAVINGS: Limit output tokens
async function cheapClassify(text: string): Promise<string> {
  const result = await groq.chat.completions.create({
    model: "llama-3.1-8b-instant",
    messages: [
      { role: "system", content: "Reply with one word: positive, negative, or neutral." },
      { role: "user", content: text },
    ],
    max_tokens: 3,       // One word = 1-2 tokens
    temperature: 0,       // Deterministic = cacheable
  });
  return result.choices[0].message.content!.trim();
}

Step 3: Batch to Reduce Overhead

// Batch 10 items in one request instead of 10 separate requests
// Saves on per-request overhead and reduces RPM usage

async function batchClassify(items: string[]): Promise<string[]> {
  const batchPrompt = items.map((item, i) => `${i + 1}. ${item}`).join("\n");

  const result = await groq.chat.completions.create({
    model: "llama-3.1-8b-instant",
    messages: [
      {
        role: "system",
        content: "Classify each numbered item as positive/negative/neutral. Reply with numbered results only.",
      },
      { role: "user", content: batchPrompt },
    ],
    max_tokens: items.length * 10,
    temperature: 0,
  });

  // Parse numbered results
  return result.choices[0].message.content!
    .split("\n")
    .map((line) => line.replace(/^\d+\.\s*/, "").trim())
    .filter(Boolean);
}
// 10 items in 1 API call vs 10 API calls = ~90% reduction in overhead

Step 4: Cache Deterministic Requests

import { createHash } from "crypto";

const cache = new Map<string, { result: string; ts: number }>();
const CACHE_TTL = 60 * 60_000; // 1 hour

async function cachedCompletion(
  messages: any[],
  model: string
): Promise<string> {
  const key = createHash("md5")
    .update(JSON.stringify({ messages, model }))
    .digest("hex");

  const cached = cache.get(key);
  if (cached && Date.now() - cached.ts < CACHE_TTL) {
    return cached.result; // Zero cost, zero latency
  }

  const response = await groq.chat.completions.create({
    model,
    messages,
    temperature: 0, // Required for cache consistency
  });

  const result = response.choices[0].message.content!;
  cache.set(key, { result, ts: Date.now() });
  return result;
}

Step 5: Usage Tracking

interface UsageRecord {
  timestamp: string;
  model: string;
  promptTokens: number;
  completionTokens: number;
  estimatedCost: number;
}

const usageLog: UsageRecord[] = [];

function trackUsage(model: string, usage: any): void {
  const config = Object.values(ROUTING).find((r) => r.model === model)
    || { inputCostPer1M: 0.10, outputCostPer1M: 0.10 };

  usageLog.push({
    timestamp: new Date().toISOString(),
    model,
    promptTokens: usage.prompt_tokens,
    completionTokens: usage.completion_tokens,
    estimatedCost:
      (usage.prompt_tokens / 1_000_000) * config.inputCostPer1M +
      (usage.completion_tokens / 1_000_000) * config.outputCostPer1M,
  });
}

function dailyCostReport(): { totalCost: string; byModel: Record<string, string> } {
  const totalCost = usageLog.reduce((sum, r) => sum + r.estimatedCost, 0);
  const byModel: Record<string, number> = {};
  for (const r of usageLog) {
    byModel[r.model] = (byModel[r.model] || 0) + r.estimatedCost;
  }
  return {
    totalCost: `$${totalCost.toFixed(4)}`,
    byModel: Object.fromEntries(
      Object.entries(byModel).map(([k, v]) => [k, `$${v.toFixed(4)}`])
    ),
  };
}

Step 6: Spending Limits in Console

In Groq Console > Organization > Billing:

1. Set monthly spending cap (e.g., $100/month)
2. Enable alerts at 50% ($50) and 80% ($80)
3. Configure auto-pause when cap is reached
4. Review usage dashboard weekly

Check your limits at console.groq.com/settings/limits.

Cost Comparison Example

Processing 100,000 customer messages:

Error Handling

Resources

• Groq Pricing
• Groq Spend Limits
• Groq Usage Dashboard

Next Steps

For architecture patterns, see

groq-reference-architecture