Future Support in GitHub Enterprise Server (GHES)

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📌 Feature Request: Future Support in GitHub Enterprise Server (GHES)

We are an enterprise user of GitHub Enterprise Server (GHES).
To better support AI-driven development and enterprise-scale software engineering, we would like to request support for the following features in future GHES releases:

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1. Offline GitHub Copilot (Self-Hosted)

• Request: Provide an offline/self-hosted version of GitHub Copilot on GHES, similar to GitLab Duo Self-Managed + AI Gateway, enabling usage in fully isolated/on-prem environments.
• Reason: We explored alternative AI coding assistants, but none can achieve 100% compatibility and seamless integration with GHES. We strongly hope GitHub can provide an official, fully supported offline Copilot solution.

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2. GitHub Codespaces on GHES

• Request: Enable Codespaces support in GHES, offering the same cloud-based development experience currently available on github.com.
• Reason: We tested Gitpod with GHES, but the developer experience was not as good as Codespaces. Enterprises require a consistent, integrated, and high-performance cloud dev environment within private infrastructure.

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3. Hugging Face Hub–Like ML Platform Features

• Request: Support machine learning (ML) model and dataset hosting directly inside GHES, including:

  • Large file uploads and versioning (models, datasets)
  • Model cards / dataset cards for documentation and reproducibility
  • Application demos (similar to Hugging Face Spaces) to showcase ML apps
  • Hosted inference services accessible via SDK/API
  • Full integration into GHES repositories for end-to-end ML lifecycle management

• Reason: Enterprises increasingly need a secure, private platform for ML development that covers the full lifecycle:
  discover → upload → manage → document → demo → deploy inference.
  Bringing these capabilities into GHES would reduce reliance on external platforms (e.g., Hugging Face Hub), enhance compliance, and keep data/models under enterprise control.

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✅ Summary

We request future GHES support for:

1. Offline Copilot (self-hosted)
2. Codespaces on GHES
3. Hugging Face Hub–like ML lifecycle features (model/data hosting, cards, demos, inference, SDK integration)

These features are essential for enterprises to adopt AI securely and efficiently, and would position GHES as a comprehensive platform for modern software and AI development.

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[UbiquitousBear]

Codespaces could be an interesting thing, it’s an ephemeral environment. I do wonder if some of the ideas from ARC could be applied here. Coder has a solution but the integration isn’t perfect.

Offline Copilot: sadly I don’t see this happening, as much as I’d like to. In a cloud-native era, there are many .com features which aren’t available and will grow because of Copilot. I don’t think we even know what models are being used. But yes, I’d like to use (e.g.) GPT OSS or a coding LLM.

In the AI era, I’m guessing guarding against exposure is incredibly important. From what I’m seeing on the roadmap, I’m not seeing that many user facing features?

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We request that GitHub Codespaces and GitHub Copilot be supported as first-class capabilities within GHES (GitHub Enterprise Server) isolated environments, using an AI Gateway–based architecture to integrate enterprise-controlled LLMs, without any dependency on github.com or external cloud services.

Core Requirements

• GitHub Codespaces

  • Deployable in GHES-backed private infrastructure
  • Provides policy-controlled, ephemeral development environments
  • Requires no outbound connectivity to public SaaS platforms

• GitHub Copilot

  • Usable within GHES isolated environments
  • All inference requests routed exclusively through an enterprise AI Gateway
  • No transmission of source code, context, prompts, or inference data to external services

• AI Gateway

  • Acts as the single egress point between Copilot and LLMs
  • Provides policy enforcement, auditing, and model routing
  • Supports enterprise-hosted, private, or approved open-source models
  • Architectural approach may align with GitLab Duo’s self-hosted AI Gateway model

Design Principles

• Data residency and isolation are hard requirements, not compliance statements
• Models may be interchangeable; control planes must remain enterprise-owned
• Codespaces and Copilot must operate as native GHES capabilities