# Mistral Deploy Integration

> Deploy Mistral AI integrations to Vercel, Docker, 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 Docker".

- Skill: `gabrielmoreira/mistral-deploy-integration` (Agent Skill)
- Install (CLI): `npx skillmds@latest add gabrielmoreira/mistral-deploy-integration`
- Raw SKILL.md: https://api.skillmd.com/api/skills/gabrielmoreira/mistral-deploy-integration/raw
- Safety review: pending (external: skill-scanner PASS, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- License: MIT
- Author: gabrielmoreira (https://skillmd.com/u/gabrielmoreira)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/gabrielmoreira/mistral-deploy-integration

---

# 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

```bash
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

```typescript
// 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

```dockerfile
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"]
```

```bash
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

```bash
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:

```bash
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:

```typescript
import { Mistral } from '@mistralai/mistralai';

const client = new Mistral({
  apiKey: 'your-local-key',
  serverURL: 'http://localhost:8000', // vLLM endpoint
});
```

### Step 6: Health Check Endpoint

```typescript
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 |

## Examples

### Deploy with a rollback-ready health gate

Deploy a Cloud Run revision with the API key injected from the platform secret store, then call the health endpoint before shifting traffic. If model listing fails or the endpoint reports unhealthy, keep traffic on the prior revision and inspect the redacted deployment logs before retrying.

## Resources

- [Mistral AI Documentation](https://docs.mistral.ai/)
- [vLLM Deployment](https://docs.mistral.ai/deployment/self-deployment/vllm/)
- [Cloud Deployment](https://docs.mistral.ai/deployment/ai-studio/)

## Output

- Platform-specific deployment configurations
- Secure API key management per platform
- Streaming support for Edge/Serverless
- Health check endpoint
- Self-hosted option with vLLM