How do I deploy Tabby AI Code Assistant on Railway?

You can deploy Tabby AI Code Assistant on Railway by clicking the "Deploy Now" button on this page. Railway will automatically set up all the necessary services and configurations for you.

What are the system requirements?

Railway handles all the infrastructure requirements. You only need a web browser to deploy and manage your application.

Is this template free to use?

Yes, this template is free to use within your existing Railway account.

Can I customize this template?

Yes, you can fully customize this template. After deployment, you will have full control of what you've deployed in your Railway project.

How do I get support for this template?

You can get support through our community forum: tabby-ai-code-assistant-981823df, or click "Get template help" for this template for more assistance.

Deploy and Host Tabby AI Code Assistant on Railway

Tabby is a professional-grade, self-hosted AI coding assistant designed as a private, open-source alternative to GitHub Copilot. It features real-time code completion, a robust chat interface, and repository indexing. By delegating LLM processing to high-performance APIs like OpenAI, Tabby provides sub-second latency for a seamless developer experience.

About Hosting Tabby AI Code Assistant

This deployment is optimized for Railway's infrastructure, utilizing a Dynamic Configuration Injection method. Unlike standard setups, this template uses a custom Bash entrypoint to bridge Railway's environment variables with Tabby's config.toml.

It mounts a persistent Railway Volume at /data, ensuring that your user accounts, indexed repositories, and settings survive redeployments. The architecture is "Compute-Light": by offloading the heavy lifting to OpenAI's servers, the instance runs efficiently on minimal CPU/RAM while delivering the power of modern LLMs. Security is baked in with an automated Hexadecimal UUID Generator for JWT signing, keeping your private assistant accessible only to you.

Common Use Cases

Proprietary Code Security: Code with AI without feeding your private logic into public training sets, ensuring your IP remains strictly within your control.
Multi-IDE AI Power: Host once on Railway and connect multiple instances of VS Code, IntelliJ, or Vim using a single secure URL and Auth Token.
Legacy Code Expert: Use the repository indexing feature to let Tabby read your existing projects, allowing it to suggest functions and variables unique to your codebase.

Dependencies for Tabby AI Code Assistant Hosting

OpenAI API Key: Required for the completion, chat, and embedding engines to function.
Railway Volume: Must be attached to the service at /data for persistence.
Outbound Network: Railway's edge network must reach api.openai.com.

Deployment Dependencies

TabbyML Official Site: https://tabby.tabbyml.com/
OpenAI API Key Management: https://platform.openai.com/api-keys
Railway Volume Documentation: https://docs.railway.app/guides/volumes

Implementation and Variable Details

Core Variables

TABBY_MODEL: Defines the LLM intelligence (default: gpt-4o-mini).
TABBY_ENDPOINT: The API bridge (default: https://api.openai.com/v1).
TABBY_WEBSERVER_JWT_TOKEN_SECRET: Secure auto-generated UUID (8-4-4-4-12).

Switching Models

Model ID	Best For	Speed
gpt-4o-mini	Fast completions and low cost	Ultra Fast
gpt-4o	Complex logic and refactoring	Fast
o1-mini	Advanced reasoning and algorithms	Moderate

Dynamic Injection Script

/bin/bash -c "export TABBY_ROOT=/data && printf '[model.completion.http]\nkind = \"openai/completion\"\nmodel_name = \"%s\"...' > /data/config.toml"

This ensures config.toml is always synchronized with Railway environment variables without manual file editing or SSH access.

Why Deploy Tabby AI Code Assistant on Railway?

Railway is a unified platform for deploying infrastructure without operational overhead. By deploying Tabby AI Code Assistant on Railway, you gain a scalable, secure, and low-maintenance AI coding assistant that integrates seamlessly into a full-stack environment.