Clusterify.AI
© 2025 All Rights Reserved, Clusterify Solutions FZCO
How Dockerized Magento 2 Can Access Local AI and Ollama
ChatBot As A Service: Why I choose SEE over WebSocket?
Markdown vs. React for an AI ChatBot widget?
cXML 1.2 What Is New, Security And Other Changes in 2026
JWT Session Security Issue With OAuth on Mac and Chrome Browser and My Fix
Mastering Chatbot Psychology For Maximum ROI

To access an Ollama instance running on your host machine from inside a Magento 2 Docker container, you need to navigate the networking bridge between the containerized environment and your host. The solution depends on your Operating System and how Ollama is configured to listen for connections.
AddressDocker provides a special DNS name to reach the host machine, but it behaves differently across platforms.
services:
magento: # your magento service name
extra_hosts:
- "host.docker.internal:host-gateway"
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
From inside your Magento container, you can test if the connection is working using curl.
Run this command inside the container:
curl http://host.docker.internal:11434/api/tags
If you get a JSON response with your models, the bridge is successful.
When configuring your Magento extension or custom code (e.g., a Guzzle client), set the base URL to: http://host.docker.internal:11434
If you are using a strictly local Linux setup without extra_hosts, replace host.docker.internal with 172.17.0.1.
In Docker, localhost refers to the container itself. Use host.docker.internal to access services running on your host machine.
Docker adds only a tiny amount of networking overhead. In practice, most of the response time comes from your hardware and model size, not from the container bridge.
Models like DeepSeek-Coder and Llama 3 are strong choices for Magento 2 work, especially when handling XML configuration, dependency injection, and module structure.
On macOS and Windows, the desktop app usually manages this. On Linux, you can run systemctl enable ollama so the service starts automatically after reboot.
It is excellent for development and internal tooling, but for production Magento stores you will usually want a dedicated inference server or managed API that can handle scaling, uptime, and concurrent requests more reliably.