Groq Cost Tuning

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.

Prerequisites

• A Groq account with an API key exported as the GROQ_API_KEY environment variable — the groq-sdk client reads it automatically (new Groq()).
• Node.js with the groq-sdk package installed (npm install groq-sdk).
• Access to the Groq Console to set spending caps and read the usage dashboard.

Groq Pricing (per million tokens)

Check current pricing at groq.com/pricing.

Instructions

Apply these six levers in order. Each compounds on the last — routing alone is the biggest win (~12x), and caching plus batching halve the remainder. The lean skeleton below shows the routing core; the full code for every step lives in references/implementation.md.

1. Smart model routing — map each use case to the cheapest model that meets its quality bar (classification/extraction/summarization → llama-3.1-8b-instant; reasoning/code review/chat → llama-3.3-70b-versatile; vision → llama-4-scout).
2. Minimize tokens per request — trim verbose system prompts and cap max_tokens so a one-word answer never bills for a paragraph.
3. Batch to reduce overhead — fold many items into one request; 10-in-1 cuts per-request overhead and RPM pressure ~90%.
4. Cache deterministic requests — at temperature: 0, hash identical prompts into a cache for zero-cost, zero-latency repeat hits.
5. Usage tracking — log token counts and estimated cost per call to catch spend regressions before the invoice.
6. Spending limits in console — set a monthly cap, alerts at 50%/80%, and auto-pause in Groq Console > Billing.

import Groq from "groq-sdk";