Deploying ADK to Non-Google Cloud Platforms?

[Alexd1001]

Hi everyone,

Has anyone successfully deployed ADK agents to platforms like Digital Ocean, AWS, or Azure?

The official docs focus heavily on GCP/Vertex AI, but ADK claims to be "deployment-agnostic." I'm looking for:

• Docker configs or deployment scripts
• Environment setup for non-GCP platforms
• Any gotchas or limitations you've encountered

Particularly interested in Digital Ocean, AWS ECS, or self-hosted Kubernetes.

Anyone tried this? Would love to hear about your experience (successful or not)!

Thanks!

[adk-bot]

Response from ADK Answering Agent (experimental, answer may be inaccurate)

TLDR: Yes, you can deploy ADK to any platform that supports containers, including AWS, Azure, and Digital Ocean. The key is to package your agent into a Docker container. While ADK is deployment-agnostic, you'll need to manage session persistence for scaled deployments.

---

Hi Alex,

That's an excellent question. You are correct that while many of the official examples focus on Google Cloud, ADK is fundamentally designed to be "deployment-agnostic." [1, 2] You can absolutely deploy it to other platforms like AWS, Azure, Digital Ocean, or a self-hosted Kubernetes cluster.

The general approach for any non-GCP platform is to containerize your agent application. [3]

General Deployment Steps

1. Create a Dockerfile for your agent. This file will define the environment, copy your agent code, install dependencies, and specify the command to start the server.
2. Use the ADK API Server: The adk api_server command starts a pre-packaged FastAPI web server to serve your agent. You will typically use this in your Dockerfile's CMD or ENTRYPOINT.
3. Build and Push the Image: Build your Docker image and push it to a container registry that your deployment platform can access (e.g., Amazon ECR, Azure Container Registry, or Docker Hub).
4. Deploy the Container: Deploy the image to your chosen service (e.g., AWS ECS, Azure App Service, or a Kubernetes Deployment).

Example: Python Dockerfile

Here is a sample Dockerfile based on the official documentation for deploying to Cloud Run, which can be adapted for any platform. [4]

# Use an official Python runtime as a parent image
FROM python:3.11-slim

# Set the working directory
WORKDIR /usr/src/app

# Copy the requirements file and install dependencies
COPY requirements.txt ./
pip install --no-cache-dir -r requirements.txt

# Copy the rest of the application code
COPY . .

# Set the command to run the ADK API server
# This assumes your agent is defined in a file named "main.py"
# and the agent object is named "my_agent".
CMD ["adk", "api_server", "--module_name=main", "--agent_name=my_agent", "--port=8080", "--host=0.0.0.0"]

Gotchas and Considerations

You highlighted this in your question, and there are a few important things to keep in mind:

• Session Management: By default, ADK uses an InMemorySessionService for managing conversation state. [5] This works for single-container deployments, but if you scale to multiple instances (e.g., in Kubernetes or ECS), it will lead to inconsistent conversations. For production, you must implement a custom SessionService that uses a distributed backend like Redis or a database to maintain a stateless application architecture.
• Streaming & WebSockets: If your agent uses streaming, ensure your load balancer and ingress are configured to support long-lived WebSocket connections. [6] Depending on your session management strategy, you may also need to enable session affinity ("sticky sessions").
• Configuration & Secrets: You will need to manage environment variables for API keys (like GOOGLE_API_KEY) and other configurations within your chosen deployment platform. [7]
• Using Non-Google Models: For a truly platform-independent deployment, you can configure ADK to use self-hosted open-source models (like Llama or Gemma) via LiteLLM. This allows you to point ADK to a local inference server instead of Google's APIs. [8]

I hope this helps you get started! We'd love to hear about your experience deploying ADK to other platforms.

---

[1] https://github.com/google/adk-docs/blob/main/README.md
[2] https://github.com/google/adk-python/blob/main/contributing/adk_project_overview_and_architecture.md
[3] https://google.github.io/adk-docs/deploy/
[4] https://google.github.io/adk-docs/deploy/cloud-run/
[5] https://google.github.io/adk-docs/get-started/testing/
[6] https://google.github.io/adk-docs/streaming/custom-streaming-ws/
[7] https://github.com/google/adk-python/blob/main/contributing/samples/adk_documentation/adk_release_analyzer/README.md
[8] https://google.github.io/adk-docs/agents/models/

[hangfei]

We are trying to provide more of those support through community repo: https://github.com/google/adk-python-community