Tabnine

Running Tabnine locally can be resource-intensive, potentially impacting performance, especially on older machines. The exact system requirements depend on the size of your codebase, but a powerful CPU and sufficient RAM (at least 16GB is recommended) are important for smooth operation. Consider testing with a smaller project first to gauge the impact.

Tabnine is trained strictly on permissively licensed code to mitigate legal risks related to IP contamination. While you can train it on your private repositories, it will not be able to directly learn from or suggest code based on non-permissively licensed code, ensuring compliance and reducing legal liabilities.

Tabnine offers integrations with popular IDEs like VS Code, IntelliJ IDEA, and more, providing context-aware code suggestions directly within your workflow. While specific integrations are detailed on their website, using a supported IDE generally provides the best experience with optimized performance and seamless integration of Tabnine's features.

The training time for a custom Tabnine model on your private repositories varies depending on the size and complexity of your codebase. Factors influencing training time include the amount of code, the hardware resources allocated to the training process, and the complexity of your internal coding patterns. Tabnine's team can provide more specific estimates based on your project details during the Enterprise plan setup.