Customers running GitLab Duo Agent Platform Self-Hosted operate under constraints many software teams don't face: data residency mandates, air-gapped networks, and compliance regulations that prohibit sending source code to third-party APIs. Those constraints also come with a trade-off. The most capable models tend to land in cloud-first deployments, leaving regulated and isolated environments a step behind on AI capability, and forcing teams into a single-model setup that's either overkill for routine work or underpowered for complex agentic tasks.
GitLab 19.0 narrows that gap by expanding self-hosted open source model support. Customers can match the right model to the right workflow, even for teams running their own GPUs in fully isolated or air-gapped environments. Whether your focus is data residency, network isolation, or regulatory compliance, you now have more capable options.
For teams in fully isolated environments — no external API calls, no internet connectivity — open source models on local inference infrastructure are the only viable path. Air-gapped environments have historically been the last to realize AI productivity gains. This can be due to compliance regulations, data classification requirements that prohibit sending code to third-party APIs, or network controls that block cloud-based inference.
Open source models deployed on-premises address these constraints directly. The inference runs on your hardware, and no data leaves your environment. GitLab's engineering team evaluated candidate models against the task requirements of Duo Agent Platform — multi-step tool use, instruction adherence, code generation quality, and reasoning over large diffs and multi-file codebases — and selected models that perform reliably enough to power real agentic workflows.
The newly supported models include:
The primary pattern is on-premises hardware running vLLM, GitLab's recommended serving platform for open source models. For teams that want self-managed inference without dedicated hardware capital costs, open source models also run on GPU-enabled virtual machines in virtual private clouds, giving you on-demand capacity with the same data isolation guarantees.
Here are some considerations to choose a deployment model:
Fully air-gapped? Open source models on your own inference hardware are the path. See the supported models documentation for hardware requirements per model.
Hybrid deployment? GitLab Duo Agent Platform Self-Hosted supports mixing self-hosted models with GitLab-managed models per feature. See the AI Gateway configuration documentation for details.
Customers with an offline license require the GitLab Duo Agent Platform Self-Hosted add-on.
Customers with an online license can use the usage-based model and can combine self-hosted and GitLab-managed models in a hybrid configuration.
Contact our sales team to discuss your deployment requirements.
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