Ditching Copilot for OpenCode: What Happens to Your Data?

Security and privacy concerns are driving a significant shift in how engineers integrate artificial intelligence into their daily workflows. Many developers are currently switching from GitHub Copilot to OpenCode to regain control over their intellectual property and minimize the risk of sensitive code leaking into external training sets. While GitHub Copilot provides a highly polished experience with deep integration into VS Code, its reliance on cloud processing and telemetry collection creates friction for organizations with strict compliance requirements. OpenCode offers an alternative path that prioritizes transparency and data sovereignty, especially when combined with tools that keep the execution environment local.

Understanding the Data Lifecycle in AI Coding Tools

When you use an AI assistant, your code travels through several stages. Understanding these stages is essential to evaluating the privacy risks associated with switching from GitHub Copilot to OpenCode.

In a standard cloud-based setup, the editor collects context from your open files. This includes not just the file you are currently typing in, but also surrounding files that might provide relevant variable definitions or architectural patterns. This context is bundled into a prompt and sent to a remote server. Once the server generates a suggestion, the data remains in the provider logs for a period of time.

GitHub Copilot has established clear policies regarding its Business and Enterprise tiers, where they claim not to use your code for training their base models. However, telemetry regarding how you interact with suggestions is often still collected. OpenCode, being an open model, allows for a different approach. You can choose exactly where the model runs and how much of your metadata is shared with the entity serving the model.

Telemetry vs. Training Data

It is important to distinguish between the code used for inference and the telemetry data collected by the tool. Telemetry usually includes:

• Latency metrics for suggestion generation.
• Acceptance rates (whether you pressed tab or ignored the code).
• Editor version and OS information.
• Frequency of feature usage.

GitHub Copilot relies on this telemetry to improve its ranking algorithms. If you are switching from GitHub Copilot to OpenCode, you are likely looking for a way to disable this heartbeat of data entirely. OpenCode itself is a model, but the environment where you run it determines the telemetry policy. Using a local-first approach ensures that neither the code nor the usage patterns leave your hardware unless you explicitly authorize an external API call.

The Role of Local-First Infrastructure

The most effective way to secure your development environment is to keep the files and the execution engine on your own machine. Deska supports this by acting as a free desktop application for Mac, Windows, and Linux that prioritizes local data. By using Deska, you can run multiple tools like OpenCode and Claude Code side by side in an infinite canvas without your code being indexed by a central authority.

Deska functions as a workspace where your files and sessions stay on your machine. When you interact with OpenCode via a panel in Deska, the interaction is direct. If you are a subscriber using managed inference, the data is processed according to the privacy terms of the provider, but the workspace itself does not scrape your activity. For those seeking absolute sovereignty, using your own API keys ensures you maintain the relationship with the model provider of your choice.

Comparing GitHub Copilot and OpenCode Environments

The following table compares the typical data handling characteristics of these two approaches.

Managing AI Agents and Context

A significant part of the modern workflow involves coding agents. These agents do more than just complete a line of code: they can read entire directories and execute terminal commands. This level of access requires a high degree of trust in the tool.

When you use coding agents within a controlled workspace, you can monitor exactly what the agent sees. In Deska, agents like OpenCode run as panels. You can see the terminal output and the file system changes in real time. This visibility is a core component of data sovereignty. You are not just trusting a "black box" to handle your files: you are observing the process on a workspace that you control.

Furthermore, features like Ask Deska allow you to use voice or chat to drive the workspace. Since Deska is local-first, the instructions you give to open a terminal or check a session are handled with a focus on your local environment. This is a contrast to cloud-integrated assistants that may log your natural language queries to improve their intent recognition models.

Secure Remote Access and Mobile Monitoring

One common reason developers stick with cloud-heavy tools is the ease of remote access. However, this often involves exposing ports or storing session data on a third-party server.

When moving away from centralized tools, you should look for solutions that use direct pairing. For example, the Deska mobile app allows you to monitor your work through a secure relay. The devices pair directly, meaning no ports are exposed to the public internet. Your code remains on your primary workstation, and the mobile device acts as a secure window into that local session. This maintains the privacy benefits of a local setup while providing the flexibility of the cloud.

Best Practices for Data Sovereignty

If you are committed to the transition, follow these steps to ensure your data remains protected:

1. Audit your extensions: Remove any legacy plugins that have broad read access to your file system.
2. Use a local-first workspace: Platforms like Deska ensure that data and storage are kept on your machine by default.
3. Manage your keys: If you are not running the model locally on your GPU, use a Bring Your Own Key (BYOK) model to control your billing and data relationships.
4. Monitor agent permissions: Always check what an AI agent is allowed to do in your terminals.
5. Encrypt local sessions: Ensure your local disk is encrypted so that the session data stored by your workspace is safe from physical theft.

FAQ

Is OpenCode safer than GitHub Copilot for private code?

OpenCode is an open model weights release, which means you can run it on your own infrastructure. This eliminates the need to send code to GitHub servers, making it a safer choice for proprietary or highly sensitive projects that cannot leave your network.

Can I run OpenCode without an internet connection?

Yes, if you have a local inference engine and the necessary GPU resources, you can run OpenCode entirely offline. This provides the highest level of data sovereignty possible, as no telemetry or code snippets can be transmitted to external servers.

How does Deska handle my API keys for OpenCode?

Deska follows a local-first philosophy. Your API keys are stored locally on your machine. When you use the pricing lifetime tier with your own keys, the application communicates directly with the model provider from your device, and Deska does not sit in the middle of that data exchange.

Start Building Privately

Switching to a more transparent AI workflow does not mean sacrificing productivity. By combining powerful models like OpenCode with a flexible, local-first workspace, you can maintain the speed of AI assisted development without compromising your security standards.

The transition is about choosing tools that respect your boundaries. You can explore a new way of working by trying a workspace that puts you in control of every panel, terminal, and AI interaction. Download the Deska desktop app today to start building on your own terms.