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More AI models for GitLab Duo Agent Platform Self-Hosted (opens in new tab)

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GitLab 19.0 expands open source model support for Duo Agent Platform Self-Hosted, giving regulated and air-gapped teams more capable AI options without sending source code to external APIs. The update supports selecting different models for different workflows and enables both fully on-premises and hybrid deployments. GitLab’s goal is to reduce the capability gap between isolated environments and cloud-based AI services.

Challenges for Regulated and Air-Gapped Teams

  • Data residency, compliance rules, and network isolation often prohibit third-party AI APIs.
  • Air-gapped environments must run inference locally because they have no internet or external connectivity.
  • Teams have traditionally faced a trade-off between using an underpowered model and deploying an unnecessarily large model for routine tasks.
  • These constraints have limited AI productivity gains in highly regulated environments.

Expanded Open Source Model Support

GitLab evaluated models for:

  • Multi-step tool use
  • Instruction adherence
  • Code generation
  • Reasoning across large diffs and multi-file codebases

Newly supported models include:

  • Mistral Devstral 2 123B
  • GLM-5.1
  • Kimi-K2.6
  • MiniMax-M2.7

Deployment Options

  • The recommended setup uses on-premises hardware with vLLM for model serving.
  • Organizations can also deploy models on GPU-enabled virtual machines in private clouds.
  • Both approaches keep data within the organization’s controlled environment.
  • Fully air-gapped teams should use locally hosted models and consult hardware requirements for each model.
  • Hybrid deployments can combine self-hosted and GitLab-managed models on a per-feature basis.

Availability and Licensing

  • Offline-license customers need the GitLab Duo Agent Platform Self-Hosted add-on.
  • Online-license customers can use usage-based models and combine self-hosted and GitLab-managed models.

GitLab recommends choosing models and infrastructure based on network isolation, compliance requirements, hardware availability, and workflow needs. The expanded support makes self-hosted AI a more practical option for organizations that require strict control over their code and data.