Deepgram Deploy Integration

Deepgram Deploy Integration

Overview

Deploy Deepgram transcription services to Docker, Kubernetes, AWS Lambda, and Google Cloud Run. Includes production Dockerfile, K8s manifests with secret management, serverless handlers for event-driven transcription, and health check patterns.

Prerequisites

• Working Deepgram integration (tested locally)
• Production API key in secret manager
• Container registry access (Docker Hub, ECR, GCR)
• Target platform CLI installed

Instructions

Step 1: Production Dockerfile

# Multi-stage build for minimal production image
FROM node:20-alpine AS builder

WORKDIR /app
COPY package*.json ./
RUN npm ci --production=false
COPY tsconfig.json ./
COPY src/ ./src/
RUN npm run build

FROM node:20-alpine AS runtime

# Security: non-root user
RUN addgroup -g 1001 -S app && adduser -S app -u 1001
WORKDIR /app

# Production dependencies only
COPY package*.json ./
RUN npm ci --production && npm cache clean --force

# Copy built application
COPY --from=builder /app/dist ./dist

# Health check (tests Deepgram connectivity)
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
  CMD wget -q --spider http://localhost:3000/health || exit 1

USER app
EXPOSE 3000

CMD ["node", "dist/server.js"]

Step 2: Docker Compose

# docker-compose.yml
version: '3.8'

services:
  deepgram-service:
    build: .
    ports:
      - "3000:3000"
    environment:
      - NODE_ENV=production
      - DEEPGRAM_API_KEY=${DEEPGRAM_API_KEY}
      - DEEPGRAM_MODEL=nova-3
    healthcheck:
      test: ["CMD", "wget", "-q", "--spider", "http://localhost:3000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
    restart: unless-stopped
    deploy:
      resources:
        limits:
          memory: 512M
          cpus: '1.0'

  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    volumes:
      - redis-data:/data

volumes:
  redis-data:

Step 3: Kubernetes Deployment

# k8s/deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: deepgram-service
  labels:
    app: deepgram-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: deepgram-service
  template:
    metadata:
      labels:
        app: deepgram-service
    spec:
      containers:
        - name: deepgram-service
          image: your-registry/deepgram-service:latest
          ports:
            - containerPort: 3000
          env:
            - name: NODE_ENV
              value: production
            - name: DEEPGRAM_API_KEY
              valueFrom:
                secretKeyRef:
                  name: deepgram-secrets
                  key: api-key
            - name: DEEPGRAM_MODEL
              value: nova-3
          resources:
            requests:
              memory: "256Mi"
              cpu: "250m"
            limits:
              memory: "512Mi"
              cpu: "1000m"
          livenessProbe:
            httpGet:
              path: /health
              port: 3000
            initialDelaySeconds: 10
            periodSeconds: 30
          readinessProbe:
            httpGet:
              path: /health
              port: 3000
            initialDelaySeconds: 5
            periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
  name: deepgram-service
spec:
  selector:
    app: deepgram-service
  ports:
    - port: 80
      targetPort: 3000
  type: ClusterIP
---
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: deepgram-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: deepgram-service
  minReplicas: 2
  maxReplicas: 10
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 70

# Create secret
kubectl create secret generic deepgram-secrets \
  --from-literal=api-key=$DEEPGRAM_API_KEY

# Deploy
kubectl apply -f k8s/

Step 4: AWS Lambda Handler

// lambda/handler.ts
import { createClient } from '@deepgram/sdk';
import { S3Client, GetObjectCommand } from '@aws-sdk/client-s3';
import type { S3Event } from 'aws-lambda';

const deepgram = createClient(process.env.DEEPGRAM_API_KEY!);
const s3 = new S3Client({});

// Trigger: S3 upload of audio file -> Lambda -> Deepgram -> Store result
export async function handler(event: S3Event) {
  for (const record of event.Records) {
    const bucket = record.s3.bucket.name;
    const key = decodeURIComponent(record.s3.object.key);

    console.log(`Processing: s3://${bucket}/${key}`);

    // Get audio from S3
    const { Body } = await s3.send(new GetObjectCommand({ Bucket: bucket, Key: key }));
    const audio = Buffer.from(await Body!.transformToByteArray());

    // Transcribe
    const { result, error } = await deepgram.listen.prerecorded.transcribeFile(
      audio,
      {
        model: 'nova-3',
        smart_format: true,
        diarize: true,
        utterances: true,
      }
    );

    if (error) {
      console.error(`Transcription failed for ${key}:`, error.message);
      throw error;
    }

    console.log(`Transcribed ${key}: ${result.metadata.duration}s, ` +
      `${result.results.channels[0].alternatives[0].words?.length} words`);

    return {
      statusCode: 200,
      body: JSON.stringify({
        file: key,
        duration: result.metadata.duration,
        transcript: result.results.channels[0].alternatives[0].transcript,
        request_id: result.metadata.request_id,
      }),
    };
  }
}

Step 5: Google Cloud Run

// server.ts — Cloud Run entry point
import express from 'express';
import { createClient } from '@deepgram/sdk';

const app = express();
app.use(express.json({ limit: '50mb' }));

const deepgram = createClient(process.env.DEEPGRAM_API_KEY!);

app.post('/transcribe', async (req, res) => {
  try {
    const { url, model = 'nova-3', diarize = false } = req.body;

    const { result, error } = await deepgram.listen.prerecorded.transcribeUrl(
      { url },
      { model, smart_format: true, diarize }
    );

    if (error) return res.status(502).json({ error: error.message });

    res.json({
      transcript: result.results.channels[0].alternatives[0].transcript,
      confidence: result.results.channels[0].alternatives[0].confidence,
      duration: result.metadata.duration,
      request_id: result.metadata.request_id,
    });
  } catch (err: any) {
    res.status(500).json({ error: err.message });
  }
});

app.get('/health', async (req, res) => {
  try {
    const { error } = await deepgram.manage.getProjects();
    res.json({ status: error ? 'degraded' : 'healthy' });
  } catch {
    res.status(503).json({ status: 'unhealthy' });
  }
});

const port = process.env.PORT || 3000;
app.listen(port, () => console.log(`Listening on port ${port}`));

# Deploy to Cloud Run
gcloud run deploy deepgram-service \
  --source . \
  --set-env-vars DEEPGRAM_API_KEY=$(gcloud secrets versions access latest --secret deepgram-key) \
  --memory 512Mi \
  --timeout 300 \
  --concurrency 50 \
  --min-instances 1 \
  --max-instances 10

Step 6: Deploy Script

#!/bin/bash
set -euo pipefail

ENV="${1:?Usage: deploy.sh <staging|production>}"

echo "Deploying to $ENV..."

# Build
npm ci && npm run build && npm test

# Build container
docker build -t deepgram-service:$ENV .

# Deploy based on target
case $ENV in
  staging)
    kubectl --context staging apply -f k8s/
    kubectl --context staging rollout status deployment/deepgram-service
    ;;
  production)
    kubectl --context production apply -f k8s/
    kubectl --context production rollout status deployment/deepgram-service
    ;;
esac

# Post-deploy smoke test
echo "Running smoke test..."
ENDPOINT=$(kubectl get svc deepgram-service -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
curl -sf "http://$ENDPOINT/health" || { echo "SMOKE TEST FAILED"; exit 1; }
echo "Deploy successful."

Output

• Production Dockerfile (multi-stage, non-root, health check)
• Docker Compose with Redis for caching
• Kubernetes manifests (Deployment, Service, HPA, Secret)
• AWS Lambda handler (S3 trigger -> Deepgram -> result)
• Cloud Run service with health check
• Environment-aware deploy script

Error Handling

Resources

• Docker Best Practices
• Kubernetes Deployments
• AWS Lambda Node.js
• Cloud Run Quickstart