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mistral-deploy-integration Deploy Mistral AI integrations to Vercel, Fly.io, and Cloud Run platforms.
Use when deploying Mistral AI-powered applications to production,
configuring platform-specific secrets, or setting up deployment pipelines.
Trigger with phrases like "deploy mistral", "mistral Vercel",
"mistral production deploy", "mistral Cloud Run", "mistral Fly.io".
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill mistral-deploy-integration ai automation claude-code devops mcp ai-agents
Mistral AI Deploy Integration
Overview
Deploy Mistral AI-powered applications to production with secure API key management. Covers Vercel (Edge + Serverless), Docker, Cloud Run, and self-hosted vLLM deployments. All connect to api.mistral.ai or your own inference endpoint.
Prerequisites
Mistral AI production API key
Platform CLI installed (vercel, docker, or gcloud)
Application using @mistralai/mistralai SDK
Instructions
Step 1: Platform Secret Configuration
set -euo pipefail
# Vercel
vercel env add MISTRAL_API_KEY production
vercel env add MISTRAL_MODEL production # optional: default model
# Cloud Run
echo -n "your-key" | gcloud secrets create mistral-api-key --data-file=-
# Docker
echo "MISTRAL_API_KEY=your-key" > .env.production
echo ".env.production" >> .gitignore
Step 2: Vercel Edge Function
// api/chat.ts — Vercel Edge Function with streaming
import { Mistral } from '@mistralai/mistralai';
export const config = { runtime: 'edge' };
export default async function handler(req: Request) {
const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY! });
const { messages, stream = false } = await req.json();
if (stream) {
const streamResponse = await client.chat.stream({
model: process.env.MISTRAL_MODEL ?? 'mistral-small-latest',
messages,
});
const encoder = new TextEncoder();
const readable = new ReadableStream({
async start(controller) {
for await (const event of streamResponse) {
const content = event.data?.choices?.[0]?.delta?.content;
if (content) {
controller.enqueue(encoder.encode(`data: ${JSON.stringify({ content })}\n\n`));
}
}
controller.enqueue(encoder.encode('data: [DONE]\n\n'));
controller.close();
},
});
return new Response(readable, {
headers: {
'Content-Type': 'text/event-stream',
'Cache-Control': 'no-cache',
},
});
}
const response = await client.chat.complete({
model: process.env.MISTRAL_MODEL ?? 'mistral-small-latest',
messages,
});
return Response.json(response);
}
Step 3: Docker Deployment FROM node:20-slim AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --production=false
COPY . .
RUN npm run build
FROM node:20-slim
WORKDIR /app
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./
ENV NODE_ENV=production
EXPOSE 3000
HEALTHCHECK --interval=30s --timeout=5s \
CMD curl -sf http://localhost:3000/health || exit 1
CMD ["node", "dist/index.js"]
set -euo pipefail
docker build -t mistral-app .
docker run -d --name mistral-app \
-p 3000:3000 \
-e MISTRAL_API_KEY="$MISTRAL_API_KEY" \
-e MISTRAL_MODEL="mistral-small-latest" \
mistral-app
Step 4: Cloud Run Deployment set -euo pipefail
# Build and push
gcloud builds submit --tag gcr.io/$PROJECT_ID/mistral-app
# Deploy with secret injection
gcloud run deploy mistral-service \
--image gcr.io/$PROJECT_ID/mistral-app \
--region us-central1 \
--platform managed \
--set-secrets=MISTRAL_API_KEY=mistral-api-key:latest \
--set-env-vars=MISTRAL_MODEL=mistral-small-latest \
--min-instances=1 \
--max-instances=10 \
--memory=512Mi \
--timeout=60s
Step 5: Self-Hosted with vLLM For data sovereignty or latency requirements, self-host open-weight Mistral models:
set -euo pipefail
# Serve Mistral with vLLM (OpenAI-compatible API)
docker run --runtime nvidia --gpus all \
-v ~/.cache/huggingface:/root/.cache/huggingface \
-p 8000:8000 \
-e HF_TOKEN="$HF_TOKEN" \
vllm/vllm-openai:latest \
--model mistralai/Mistral-Small-24B-Instruct-2501 \
--dtype auto \
--api-key "your-local-key"
Point the SDK at your local endpoint:
import { Mistral } from '@mistralai/mistralai';
const client = new Mistral({
apiKey: 'your-local-key',
serverURL: 'http://localhost:8000', // vLLM endpoint
});
Step 6: Health Check Endpoint import { Mistral } from '@mistralai/mistralai';
export async function GET() {
const start = performance.now();
try {
const client = new Mistral({ apiKey: process.env.MISTRAL_API_KEY! });
await client.models.list();
return Response.json({
status: 'healthy',
provider: 'mistral',
latencyMs: Math.round(performance.now() - start),
});
} catch (error: any) {
return Response.json(
{ status: 'unhealthy', error: error.message },
{ status: 503 },
);
}
}
Error Handling Issue Cause Solution API key not found Missing env/secret Verify secret config on platform Function timeout Long completion Increase timeout, use streaming Cold start latency Serverless spin-up Set min-instances=1 or use edge vLLM OOM Model too large for GPU Use quantized model or smaller variant
Resources
Output
Platform-specific deployment configurations
Secure API key management per platform
Streaming support for Edge/Serverless
Health check endpoint
Self-hosted option with vLLM
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
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CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
