Imported from jeremylongshore/tons-of-skills-marketplace (plugins/saas-packs/groq-pack/skills/groq-deploy-integration/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill groq-deploy-integration. Copyright stays with the author (MIT).

Groq Deploy Integration

Overview

Deploy applications using Groq's inference API to Vercel Edge, Cloud Run, Docker, and other platforms. Groq's sub-200ms latency makes it ideal for edge deployments and real-time applications.

This SKILL.md is the high-level workflow. Every platform recipe — full source for the Vercel Edge Function, Dockerfile, Cloud Run command, Express health-check server, and Vercel AI SDK handler — lives verbatim in references/implementation.md. End-to-end walkthroughs that chain those recipes are in references/examples.md.

Prerequisites

• Groq API key stored in GROQ_API_KEY
• Application using groq-sdk (or @ai-sdk/groq for the Vercel AI SDK path)
• Platform CLI installed (vercel, docker, or gcloud)

Instructions

Pick the deployment target, then follow its recipe in references/implementation.md.

1. Write the handler. For Vercel Edge, create app/api/chat/route.ts with export const runtime = "edge" and stream Server-Sent Events when the request asks for them; otherwise return a JSON completion. See Step 1 in references/implementation.md.
2. Store the secret. Never bake GROQ_API_KEY into an image. Use the platform's secret store — see the Environment Variable Config table below.
3. Deploy. vercel --prod for Vercel (Step 2); build the Dockerfile (Step 3) and gcloud run deploy --source . for Cloud Run (Step 4) — all in references/implementation.md.
4. Add a health check. The Express server (Step 5) exposes /health that pings Groq with the cheapest model (llama-3.1-8b-instant, max_tokens: 1) and reports latency, so orchestrators can probe liveness cheaply.
5. Keep instances warm. On serverless platforms set min-instances=1 to keep cold-start latency off the request path.

The essential Vercel Edge skeleton looks like this — the full streaming body is in the reference:

// app/api/chat/route.ts
import Groq from "groq-sdk";
export const runtime = "edge";

export async function POST(req: Request) {
  const groq = new Groq({ apiKey: process.env.GROQ_API_KEY! });
  const { messages } = await req.json();
  const completion = await groq.chat.completions.create({
    model: "llama-3.3-70b-versatile",
    messages,
    max_tokens: 2048,
  });
  return Response.json(completion);
}

Environment Variable Config

Output

Following this skill produces:

• A deployed Groq inference endpoint (POST /api/chat) on the chosen platform that streams text/event-stream chunks on demand and returns JSON completions otherwise.
• The secret registered in the platform's secret store — never committed to source or an image layer.
• A /health liveness endpoint returning { status: "healthy", groq: { connected: true, latencyMs: N } } (HTTP 200) or { status: "unhealthy", ... } (HTTP 503) for orchestrator probes.
• A warm serverless configuration (min-instances=1) keeping cold-start latency off the request path.

Error Handling

Examples

Full worked walkthroughs live in references/examples.md:

• Example A — Vercel Edge streaming chat: drop in the Step 1 handler, vercel env add + vercel --prod, get a streaming POST /api/chat URL.
• Example B — Cloud Run with a liveness probe: Dockerfile HEALTHCHECK + Express /health + gcloud run deploy --min-instances=1, yielding a 200/503 health signal Cloud Run consumes.
• Example C — Vercel AI SDK path: swap the raw client for @ai-sdk/groq streamText + toDataStreamResponse() for zero manual stream plumbing.

Resources

• Groq API Documentation
• Vercel AI SDK + Groq
• Groq Client Libraries
• Full implementation recipes
• Worked examples

Next Steps

For multi-environment setup (separate dev/staging/prod secrets and pipelines), see the groq-multi-env-setup skill in this pack.