The Enterprise Model Registry
for Secure AI
Secure. Portable. No Lock‑In.
The only model registry that works anywhere containers run — from public cloud to the most locked‑down air‑gapped environments.
Why Kitops exists
Enterprise AI teams face three problems with traditional model registries: security gaps, vendor lock‑in, and deployment friction.
KitOps solves them all.
Unlike proprietary registries that trap you in closed ecosystems, KitOps stores models, datasets, code, and configs as OCI artifacts in your container registries — Docker Hub, ECR, GCR, Harbor, Artifactory, and beyond.
With 100,000+ downloads and 18+ months in production, KitOps is trusted by organizations that can’t compromise on security, compliance, or uptime.
stay informed About Kitops
Why engineers choose Kitops?
- Immutable by design — once published, artifacts can’t be tampered with
- Cryptographically signed — every model, dataset, and config is verifiable
- Provenance tracking — full supply chain auditability out of the box
- Defense in depth — layered SHA digest verification for all artifacts
- OCI‑native — runs on any compliant container registry
- No proprietary formats — open standards mean zero lock‑in
- CI/CD‑ready — GitHub Actions, GitLab CI, Jenkins, Azure DevOps, and more
- Deployment‑friendly — works with Kubernetes, Docker, and existing pipelines
- Air‑gapped deployments — fully supported
- Multi‑cloud portability — same artifact runs anywhere
- Deduplicated storage — cuts registry costs by up to 80%
- Automatic containerization — for any model
- RBAC integration — aligns with your security policies
Works with the tools you Already use
Built for Engineers Who Demand Trust
- Trusted in production
- Relied on by Enterprise teams
- Engineered for adoption
- Built to meet proven standards
- Designed for air-gapped environments
- Security by default
- Native to your stack
- Works at enterprise scale
- Moves at CI/CD speed
Enterprise‑Grade from Day One
Get Started with KitOps
Install the KitOps CLI
brew install KitOpsPackage Your First Model
kit pack . -t myregistry.com/my-model:latestStore in Your Registry
kit push myregistry.com/my-model:latestDeploy Anywhere
kit unpack myregistry.com/my-model:latest --modelfAq
Are ModelKits a versioning solution or a packaging solution?
ModelKits do both. With a ModelKit, you can package all the parts of your AI project in one shareable asset, and tag them with a version. ModelKits were designed for the model development lifecycle, where projects are handed off from data science teams to application teams to deployment teams. Versioning and packaging makes it easy for team members to find the datasets and configurations that map to a specific model version. You can read more details about KitOps in our overview.
How do I get started with Kit?
The easiest way to get started is to follow our Quick Start, where you’ll learn how to:
- Package up a model, notebook, and datasets into a single ModelKit you can use with your existing tools
- Share the ModelKit with others through your public or private registry
- Grab only the assets you need from the ModelKit for testing, integration, local running, or deployment
Can I see if something changed between ModelKits?
Yes [choir sings hallelujah], each ModelKit includes SHA digests for the ModelKit and every artifact it holds so you can quickly see if something changed between ModelKit versions.
What are the benefits of using Kit?
Increased speed: Teams can work faster with a centralized and versioned package for their AI project coordination. ModelKits eliminate hunting for datasets or code, and make it obvious which datasets and configurations are needed for each model. Handoffs can be automated and executed quickly and with confidence.
Reduced risk: ModelKits are self-verifying. Both the ModelKit itself and all the artifacts added to it are tamper-proof. Anyone can quickly and easily verify when something may have changed.
Improved efficiency: Models stored in ModelKits can be run locally for experimentation or application integration, or packaged for deployment with a single command. Any artifact in a ModelKit can be separately pulled saving time and space on local or shared machines. This makes it easy for data scientists, application developers, and DevOps engineers to find and grab the pieces they need to do their job without being overwhelmed with unnecessary files.
What tools are compatible with Kit?
ModelKits store their assets as OCI-compatible artifacts. This makes them compatible with nearly every development and deployment tool and registry in use today.
Where are ModelKits stored?
ModelKits can be stored in any OCI-compliant registry - for example in a container registry like Docker Hub or Jozu Hub, or your favorite cloud vendor’s container registry, they can even be stored in an artifact repository like Artifactory.
Is KitOps open source and free to use?
Yes, it is licensed with the Apache 2.0 license and welcomes all users and contributors. If you’re interested in contributing, let us know.
Are ModelKits a replacement for Docker containers?
No, ModelKits complement containers - in fact, KitOps can take a ModelKit and generate a container for the model automatically. However, not all models should be deployed inside containers - sometimes it’s more efficient and faster to deploy an init container linked to the model for deployment. Datasets may also not need to be in containers - many datasets are easier to read and manipulate for training and validation when they’re not in a container. Finally, each container is still separate so even if you do want to put everything in its own container it’s not clear to people outside the AI project which datasets go with which models and which configurations.
Why would I use KitOps for versioning instead of Git?
Models and datasets in AI projects are often 10s or 100s of GB in size. Git was designed to work with many small files that can be easily diff’ed between versions. Git treats models and datasets stored in LFS (large file storage) as atomic blobs and can’t differentiate between versions of them. This makes it both inefficient and dangerous since it’s easy for someone to tamper with the models and datasets in the LFS without Git knowing. Finally, once you use LFS, a clone is no longer guaranteed to be the same as the original repo, because the repo refers to an LFS server that is independent of the clone and can change independently.
My MLOps tools do versioning, why do I need Kit?
KitOps is the only standards-based and open source solution for packaging and versioning AI project assets. Popular MLOps tools use proprietary and often closed formats to lock you into their ecosystem. This makes handoffs between MLOps tool users and non-MLOps tool users (like your application development and DevOps teams) unnecessarily hard. The future of MLOps tools is still being written, and it’s likely that many will be acquired or shut down and the cost of moving projects from one proprietary format to another is high. By using the OCI standard that’s already supported by nearly every tool on the planet, ModelKits give you a future-proofed solution for packaging and versioning that is compatible with both your MLOps tools and development / DevOps tools so everyone can collaborate regardless of the tools they use.
Is enterprise support available for Kit?
Enterprise support for ModelKits and the Kit CLI is available from Jozu.
Production-ready support from the creators and maintainers of the industry's leading open source AI/ML packaging standards