Developers can now build local AI coding assistants that keep code private and avoid API bills. OpenCode provides a flexible UI/CLI for coding tasks, while Docker Model Runner runs models locally and exposes an OpenAI-compatible API. This combination lets teams use AI without sending sensitive context to external vendors.

The setup addresses major concerns for enterprises: data privacy and cost control. With code never leaving local infrastructure, there's no risk of it being used for training or stored by third parties. Cost becomes predictable—pay for compute, not per-token fees. Models like qwen3-coder and gpt-oss are recommended for their long context windows, crucial for understanding entire codebases.

Configuring OpenCode involves pointing it to DMR's local endpoint (e.g., `http://localhost:12434/v1`). Teams can package models as OCI artifacts, enabling consistent versioning across developers. This turns AI models into reproducible infrastructure, much like Docker images, solving the 'works on my machine' problem for AI-assisted workflows.