How to Ensure Data Privacy When Using OpenCode with Local LLMs

Achieving Privacy with OpenCode and Local LLMs

Maintaining privacy while using AI-powered coding tools is a top priority for many developers. Tools like OpenCode, which act as a harness for managing LLM interactions, provide a bridge between your workflow and artificial intelligence. However, privacy ultimately hinges on where the model is performing its processing.

Understanding the Privacy Model

OpenCode itself is a management interface, not an AI engine. By default, it is often configured to connect to external LLM providers. In this configuration, your code, files, and conversation history are transmitted to third-party servers.

To ensure your data remains strictly on your own hardware, you must point OpenCode to a locally hosted model instance rather than an external API. This ensures that even when the AI processes complex codebases, provides suggestions, or executes tools, the data never leaves your environment.

Recommended Tools for Local Hosting

If you want to run models locally, you need a backend to serve them. Several robust tools exist that integrate well with client-side harnesses:

llama.cpp: A popular and highly efficient backend for running LLMs on local hardware.
Ollama: A user-friendly tool that streamlines downloading and running local models. Note that some configurations default to external services, so ensure you are pointing to the local endpoint.
Jan and LM Studio: These provide comprehensive, easy-to-use interfaces for managing and running models locally on your own machine.

Choosing the Right Model

Once you have your hosting infrastructure set up, selecting the right model is key. Recent recommendations from the development community include the Qwen3.6 27b and 35b models. These models are praised for their capability and performance when hosted on local machines using tools like Ollama.

Practical Steps to Secure Your Workflow

Select a Private Backend: Install a tool like Ollama, Jan, or LM Studio to host models locally.
Configure the Harness: Point OpenCode’s configuration to the local URL (typically localhost) provided by your hosting software.
Verify Data Flow: Ensure that all model calls are directed to your local host to maintain complete data isolation.

By decoupling the harness from external providers, you gain the productivity benefits of AI coding assistants without sacrificing control over your proprietary code or sensitive data.