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Deploy Vision Agents to production using Docker. For a complete Kubernetes setup with Helm charts, monitoring, and Grafana dashboards, see the Kubernetes Deployment guide.
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Key Considerations

Docker

Two Dockerfiles are provided: CPU (Dockerfile) - Small, fast to build (~150MB)
GPU (Dockerfile.gpu) - For local model inference (~8GB)
Build for Linux (required for cloud deployment):
Only use the GPU Dockerfile if running local models (Roboflow, local VLMs). Most voice agents use cloud APIs and don’t need GPUs. Make sure CUDA drivers are installed and the base image matches your CUDA version.

Environment Variables

Create a .env file with your API keys:
For Kubernetes, create a secret:

Next Steps

Built-in HTTP Server

API endpoints, session limits, and authentication

Horizontal Scaling

Scale across multiple servers with Redis

Kubernetes Deployment

Helm chart, Prometheus, and Grafana

Telemetry & Metrics

OpenTelemetry, Prometheus, and Jaeger setup