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