GitHub Copilot is GitHub's hosted AI coding assistant, sold per user with usage-based AI credits. Pages here cover replacing it with a self-hosted coding assistant such as Tabby running on your own GPUs.
Moving developers from GitHub Copilot to a self-hosted Tabby server: sizing GPUs for completion and chat models, rolling out the VS Code, JetBrains and Vim plugins, connecting LDAP or GitLab sign-in, indexing repositories for context, and running both during the pilot.
Comparing GitHub Copilot with self-hosted Tabby: where code and prompts go, model choice and quality on your own hardware, IDE coverage, admin and SSO features by Tabby plan, the Apache-2.0 core versus the ee directory, and who operates the GPUs.
Cost of ownership by component: Copilot seats and AI credits for agent and chat use, against Tabby's Community, Team or Enterprise plan plus GPU servers or cloud GPUs, model serving, support and the engineering time to run it.
For defense, finance and industrial teams whose source code cannot reach a hosted service: running Tabby with locally stored models on isolated GPU servers, moving model and plugin updates in through a controlled path, and what a hosted assistant cannot offer there.
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