Deploy Vision Agents to production using Docker. For a complete Kubernetes setup with Helm charts, monitoring, and Grafana dashboards, see the Kubernetes Deployment guide.
Key Considerations Factor Recommendation Region US East for lowest latency (most AI providers default here) CPU vs GPU CPU for most voice agents; GPU only if running local models Scaling Use the HTTP server for multi-session deployments
Docker Two Dockerfiles are provided:
CPU (Dockerfile) - Small, fast to build (~150MB)FROM python:3.13-slim WORKDIR /app RUN pip install uv COPY pyproject.toml uv.lock agent.py ./ EXPOSE 8080 ENV UV_LINK_MODE=copy CMD [ "sh" , "-c" , "uv sync --frozen && uv run agent.py serve --host 0.0.0.0 --port 8080" ]
GPU (Dockerfile.gpu) - For local model inference (~8GB)FROM pytorch/pytorch:2.9.1-cuda12.8-cudnn9-runtime WORKDIR /app RUN pip install uv COPY pyproject.toml uv.lock agent.py ./ EXPOSE 8080 ENV UV_LINK_MODE=copy CMD [ "sh" , "-c" , "uv sync --frozen && uv run agent.py serve --host 0.0.0.0 --port 8080" ]
Build for Linux (required for cloud deployment):
docker buildx build --platform linux/amd64 -t vision-agent .
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:
STREAM_API_KEY = your_key STREAM_API_SECRET = your_secret DEEPGRAM_API_KEY = your_key ELEVENLABS_API_KEY = your_key GOOGLE_API_KEY = your_key
For Kubernetes, create a secret:
kubectl create secret generic vision-agent-env --from-env-file=.env
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
