GitLab Model Registry
GitLab Model Registry: A GitLab-native model registry with strong lifecycle tracking and CI/CD links. Ranked #32 of 34 in Model Registry Software by our editors (5.1/10); pricing: Free plan · 30-day trial; best for gitLab-centered development teams.
At a glance
- Editor score5.1 / 10
- PricingFree plan · 30-day trial
- Best forGitLab-centered development teams
- Free planYes
- Paid from$29/user/mo
- Founded2011 · No physical headquarters; GitLab is all-remote
- Facts checked22 Sep 2026
Where it wins
- Semantic versioning, metadata, lineage, comparisons, and artifact storage
- MLflow compatibility plus GitLab CI/CD and merge-request links
- Available on web, API, GitLab Self-Managed, and GitLab Dedicated
Where it doesn't
- Free tier is limited to 5 users per top-level group
- Free tier includes 400 compute minutes and 10 GiB adjustable storage
- Ultimate pricing is not specified in the published plan details
Our verdict on GitLab Model Registry
GitLab Model Registry is a centralized repository for managing machine learning models throughout their lifecycle. It is aimed at development teams that already organize code, pipelines, merge requests, and packages in GitLab. Teams can create models and versions through the GitLab UI or MLflow-compatible workflows, using semantic versioning, model documentation, metrics, parameters, data lineage, and stored artifacts. The feature is available through GitLab.com, GitLab Self-Managed, and GitLab Dedicated.
The registry’s strongest fit is its connection to the wider GitLab development workflow. Model versions can be linked to CI/CD jobs, pipelines, and merge requests, while the Model Registry API supports uploading and downloading package files. MLflow client compatibility supports model and version management, and integrations include GitLab CI/CD, GitLab Package Registry, and GitLab API. This makes the product more suitable for teams that want model lifecycle records close to software delivery activity than for teams seeking a standalone registry detached from their development platform.
Pricing follows GitLab’s tier structure. The Free plan includes Model Registry, five users per top-level group, 400 compute minutes per month, and 10 GiB of adjustable storage. Premium starts at $29 per user per month when billed annually and adds 10,000 compute minutes, 500 GiB of storage, and priority support. Ultimate includes 50,000 compute minutes, 500 GiB of storage, and compliance and governance features. Choose GitLab Model Registry when semantic versioning, lineage, artifacts, MLflow compatibility, and CI/CD context are central requirements. Teams that do not use GitLab may prefer a registry aligned with another development ecosystem.
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GitLab Model Registry pricing
All 3 GitLab Model Registry plans and prices →
GitLab Model Registry fact sheet
| Free plan | Yes |
|---|---|
| Paid from | $29/user/mo |
| Model versioning | Yes |
| Approval workflows | Not verified |
| Model lineage | Yes |
| Deployment tracking | Not verified |
| Model aliases | Not verified |
| Artifact storage | Yes |
| Free trial | 30 days · no card needed |
| Deployment | Cloud, Self-hosted |
| Platforms | Web |
| Support | Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 4 integrations: MLflow, GitLab CI/CD, GitLab Package Registry, GitLab API |
| Pricing | Free plan · 30-day trial (source) |
| Website | docs.gitlab.com |
| Facts checked | 22 Sep 2026 |
GitLab Model Registry integrations
GitLab Model Registry lists 4 integrations on its own site.
- MLflow
- GitLab CI/CD
- GitLab Package Registry
- GitLab API
Alternatives to GitLab Model Registry
- SAS Model ManagerA governance-focused registry for managing models from versioning through monitoring.9.3
- Cloudera AI RegistryA focused model registry for teams operating machine learning workflows in Cloudera AI.9.2
- MLflow Model RegistryAn open registry for versioning, lineage, approvals, aliases, and model promotion.8.9
See all GitLab Model Registry alternatives →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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