Tabby

Open-source, self-hosted AI coding assistant for teams that keep code in-house

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Screenshot of Tabby, Open-source, self-hosted AI coding assistant for teams that keep code in-house

What is Tabby?

Tabby is an open-source, self-hosted AI coding assistant with code completion, an in-IDE answer engine, inline chat and context providers. It targets teams that want Copilot-style help while keeping source code and model serving on their own infrastructure, with support for consumer GPUs and major editors including VS Code, Neovim and the JetBrains IDEs.

Tabby is an open-source AI coding assistant that runs on infrastructure you control. It is positioned by its maker as a self-contained alternative to hosted assistants such as GitHub Copilot, with the codebase and the model serving stack living inside your own environment. Teams choose it when sending source code to a third-party cloud is not an option, or when they want to tune the assistant to the conventions of a private repository rather than accept a generic model's defaults. The assistant covers four working modes. Code completion generates context-aware suggestions as you type inside the editor, predicting the next lines of a function or the shape of a query. The Answer Engine responds to questions about your codebase from within the IDE, returning explanations, examples and source references. Inline chat keeps a conversation anchored to the code you are looking at, so questions and replies stay tied to the surrounding context. Context providers pull in documentation, configuration files and external APIs to give the model a deeper view of a project. The deployment story is the real differentiator. Tabby is built to run without an external DBMS or cloud services, and the homepage states it supports consumer-grade GPUs and Cloud IDEs. That means a small team can stand up an instance on modest hardware and connect it to editors already in use. The tradeoff is operational: someone has to host, upgrade and monitor it. Because it is open source, the software supply chain is inspectable, which matters to security-conscious engineering organizations. Editor coverage is broad and includes VS Code, Neovim, the JetBrains family (IntelliJ, PyCharm, WebStorm, GoLand, CLion, PhpStorm, RubyMine, Rider, AppCode), Eclipse and Android Studio. Tabby is aimed at development teams and platform engineers rather than solo hobbyists, and it fits best where an internal tooling budget and some infrastructure ownership already exist.

How do you use Tabby?

  1. 1Read the documentation
    Start at the docs welcome page to understand architecture, deployment options and editor requirements before installing anything.
    Tabby — Read the documentation
  2. 2Check hardware and deployment needs
    Confirm your GPU and network situation against the self-hosted model, since Tabby is designed to run without external database or cloud services.
  3. 3Stand up a Tabby instance
    Deploy the server in your own environment and connect it to the model backend that suits your hardware.
  4. 4Install the editor extension
    Add the Tabby extension to your editor, whether VS Code, Neovim or a JetBrains IDE, and point it at your instance.
  5. 5Configure context providers
    Connect documentation, configuration files and external APIs so answers reflect your project's actual conventions.
  6. 6Join the community
    Use the Slack workspace for setup questions and browse the blog for release notes and usage patterns.
    Tabby — Join the community

Pros and cons

Pros

  • Runs entirely on your own infrastructure, so source code never leaves the environmentAI
  • Open-source codebase makes the software supply chain inspectableAI
  • No external DBMS or cloud services required, which simplifies self-hostingAI
  • Supports consumer-grade GPUs, lowering the hardware bar for small teamsAI
  • Wide editor coverage across VS Code, Neovim, JetBrains IDEs, Eclipse and Android StudioAI

Cons

  • You own hosting, upgrades and monitoring, which is real ongoing engineering workAI
  • Claims of premier AI quality are hard to verify against hosted assistants without benchmarkingAI
  • Context providers beyond the core set are flagged as coming soon, so coverage is incompleteAI
  • Pricing is not published on the homepage, so budget planning needs a sales conversationAI
  • Setup and model configuration assume infrastructure comfort, not a plug-and-play installAI

How much does Tabby cost?

Support

Support runs through a public Slack community and a blog, plus a newsletter for release news. The homepage also offers a form to schedule a demo, which is the route for commercial or deployment questions.

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Integrations

Editor coverage includes VS Code, Neovim, IntelliJ, PyCharm, WebStorm, GoLand, CLion, PhpStorm, RubyMine, Rider, AppCode, Eclipse and Android Studio. The homepage also names Mistral AI, DeepSeek and Qwen as model partners, and Cloud IDEs are supported.

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Features

Four core capabilities: context-aware code completion inside the editor, an Answer Engine that responds to questions about your codebase, inline chat tied to the code under discussion, and context providers that pull in docs, configuration and APIs. Deployment is self-contained with no external DBMS or cloud services, and consumer-grade GPUs are supported.

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Frequently asked questions about Tabby

  • How do you use Tabby?
    The walkthrough on this page covers 6 steps: 1. Read the documentation 2. Check hardware and deployment needs 3. Stand up a Tabby instance 4. Install the editor extension 5. Configure context providers 6. Join the community.
  • What platforms does Tabby support?
    Tabby is available on Web App, Windows, MacOS, Linux and Browser extension.
  • What does Tabby integrate with?
    Editor coverage includes VS Code, Neovim, IntelliJ, PyCharm, WebStorm, GoLand, CLion, PhpStorm, RubyMine, Rider, AppCode, Eclipse and Android Studio. The homepage also names Mistral AI, DeepSeek and Qwen as model partners, and Cloud IDEs are supported.
  • What are the limitations of Tabby?
    You own hosting, upgrades and monitoring, which is real ongoing engineering work. Claims of premier AI quality are hard to verify against hosted assistants without benchmarking. Context providers beyond the core set are flagged as coming soon, so coverage is incomplete.

Status

StatusActive
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Added10/22/2026

Platforms

Web AppWindowsMacOSLinuxBrowser extension

Pricing

FreeFreemium