Tabby is designed to run on your own infrastructure, giving you control over where code and inference traffic lives.
The completion engine provides real-time, context-aware suggestions in your IDE as you type, adapting to your projects and coding style.
Tabby’s Answer Engine lets you ask questions directly from your IDE and receive explanations or examples without leaving your coding environment.
Inline chat provides conversational interaction with the assistant anchored to specific code regions, enabling code reviews, refactors, or explanations in place.
Tabby can connect to various data sources like documentation, config files, and external APIs to enrich its understanding of your project.
Official materials list integrations with VS Code, Neovim, IntelliJ, Eclipse, Android Studio, and multiple JetBrains products.
Organizations that cannot send code to third-party clouds can deploy Tabby on-premises, using models that never leave their network while still benefiting from modern AI-assisted coding.
Engineering teams with VS Code, JetBrains, and Neovim users can standardize on Tabby as a single AI assistant, managed via Team or Enterprise licensing.
ML or platform teams can experiment with different open-source code models behind Tabby, tuning them for their stack and swapping models without changing IDE plugins.
By using Context Providers and data connectors, teams can surface internal docs, runbooks, and configuration as part of Tabby’s responses, reducing the friction of finding and applying institutional knowledge.
Smaller teams can start with the free Community plan, deploying Tabby for a few developers, then move to Team or Enterprise once value is clear and more seats are needed.
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