How to Choose an AI Coding Tool and Manage Local Cache: Cleanup and Migration Tips
This article provides a practical framework for evaluating AI coding assistants, with a focus on local resource usage and disk footprint. It outlines six key dimensions to verify during tool selection, explains why the C drive can become full due to cached models, session logs, and indexes, and offers a clear, step-by-step approach to safely clean or migrate local data—measure, back up, clean or migrate, and verify.
When choosing an AI coding tool, treat local resource usage as a first-class criterion. If your C drive is running out of space, follow this sequence: measure first, back up, then clean or migrate, and finally verify.
Six Dimensions to Verify When Choosing a Tool
Beyond feature comparison, you should also investigate what the tool does on your local machine. The table below lists common dimensions and how to verify them.
| Dimension | What to Check | How to Verify |
|---|---|---|
| Localization and multilingual capability | Whether Chinese prompts are understood accurately; whether code comments and requirement breakdowns match your working style | Ask the same question in different tools and compare outputs |
| Cost and billing | Whether free tier limits, overage rules, and team pricing are transparent | Review the official pricing page and track your actual usage for a week |
| Cache and log paths | Whether cache and log directories can be changed in settings | Check settings panels or configuration file documentation |
| Data storage locations | Where conversation history, indexes, and model files are stored on disk | Use a file monitoring tool during a full conversation to observe write activity |
| Workflow integration | Whether the tool integrates with Git, terminals, and project management tools | Run a complete commit workflow in a real project |
| Uninstall residue | Whether data directories remain after uninstallation | Uninstall and check the user data directory |
The weight assigned to each dimension depends on your use case. Individual developers typically care more about cost and language support. Teams, on the other hand, often need to pay closer attention to data storage locations, log policies, and compliance requirements. Specific behavior may vary significantly between versions, so always refer to the official documentation and verify with real testing.
Why the C Drive Gets Full
A common symptom: you cannot start a new conversation in the IDE, and checking the disk shows that the C drive has almost no free space. When you investigate, the application data directory often turns out to occupy tens of gigabytes.
What a tool stores locally depends on how it is implemented. The common categories include the following three types.
- Model-related cache: model files, indexes, or vector data used to avoid repeated loading and network requests.
- Session and context data: conversation history, context snapshots, and code indexes. Longer conversations typically generate more of this data.
- Logs and plugin files: runtime logs, plugin dependency packages, and update leftovers.
Whether intermediate inference data (such as syntax tree parsing results) is saved depends on the specific tool. Refer to the documentation rather than assuming a uniform answer.
Start by identifying the directories that consume the most space, then decide how to handle them. The script below lists the ten largest subdirectories under the user data directory.
