Tabnine: AI Code Completion Setup & Best Practices

Tabnine has been in the AI code completion space since 2018, well before GitHub Copilot made it mainstream. Its key differentiator is a relentless focus on privacy and security. For developers and organizations that cannot send code to external servers, Tabnine offers local AI models that run entirely on your machine, plus enterprise options that guarantee your code never leaves your network.

Why Choose Tabnine Over Copilot

The primary reason is code privacy. Tabnine's models can run locally without sending any code to external servers. The AI is trained exclusively on permissively licensed open-source code, meaning no legal ambiguity about the suggestions it generates. For enterprises in healthcare, finance, defense, or any regulated industry, this is not a nice-to-have but a requirement. Tabnine also integrates with more IDEs than any competitor, supporting VS Code, JetBrains (IntelliJ, PyCharm, WebStorm, etc.), Neovim, Eclipse, and more.

Setting Up Tabnine

Install the Tabnine extension from your IDE's marketplace. For VS Code, search "Tabnine" in the Extensions panel and install. For JetBrains, go to Settings > Plugins > Marketplace and search for Tabnine. After installation, create a Tabnine account or sign in. The basic setup is complete — Tabnine starts providing completions immediately. For the local model, go to Tabnine settings and select the on-device model option. This downloads a smaller but capable model that runs entirely on your hardware. You need at least 8GB of RAM and a modern CPU; a GPU is optional but improves speed.

Key Features

Whole-line and full-function completions predict not just the next few characters but entire logical blocks. Natural language to code lets you write a comment describing what you want, and Tabnine generates the implementation. Chat provides a conversational AI assistant within your IDE for explanations, refactoring suggestions, and debugging help. Personalization learns your coding style, project patterns, and team conventions over time. The more you use it, the more relevant suggestions become. Code review capabilities can analyze pull requests and suggest improvements.

Team and Enterprise Features

Tabnine Enterprise allows organizations to train custom AI models on their own proprietary codebase. This means completions are tailored to your specific frameworks, naming conventions, and architectural patterns. Admin controls let team leads manage which models are used, set privacy policies, and monitor usage. SSO integration supports SAML and OIDC for enterprise authentication. The self-hosted option runs the entire Tabnine infrastructure within your own cloud or on-premises environment.

Best Practices for Better Completions

Write clear variable and function names — Tabnine uses naming context heavily. Add brief comments before complex functions to guide the AI. Accept completions with Tab and reject with Escape; the more you do this, the better Tabnine learns your preferences. Use the Tabnine Chat for complex tasks like generating test cases or explaining unfamiliar code. Keep your project open (not individual files) so Tabnine has full context of imports, types, and dependencies.

Pricing

The free Starter plan includes basic code completions and limited chat. Tabnine Dev at $12/month per user adds advanced completions, unlimited chat, personalization, and access to stronger models. Tabnine Enterprise starts at $39/month per user with custom model training, self-hosting, admin controls, and dedicated support.

Getting Started

Install Tabnine in your IDE, sign up for a free account, and start coding. The AI begins learning your patterns immediately. For teams evaluating AI coding tools with strict security requirements, Tabnine's privacy-first approach and on-premise options make it the standout choice.