TL;DR — KitOps standardizes the delivery of AI assets by packaging a model, its code, dependencies, and metadata into a portable OCI artifact called a ModelKit. It turns "download weights from random storage" into "pull a versioned artifact from a registry".
What it is
KitOps is an open-source packaging standard and CLI for AI/ML workloads. A ModelKit is an OCI artifact that can contain model weights, prompts, code, parameters, and a declarative manifest. It lives in AI Native Infra › Model Asset and Registry.
Why it exists
Training artifacts are more than weights. You need a reproducible bundle that includes the model, tokenizer, config, preprocessing, and runtime assumptions. KitOps makes that bundle portable and registry-friendly, so teams can promote the same artifact from dev to staging to prod without hand-assembling files.
How it works
KitOps uses a ModelKit manifest to declare inputs and outputs. The CLI packages the referenced files into an OCI image layout and pushes it to a registry. Consumers pull the ModelKit, unpack it, and run it with the recorded metadata intact.
Key features
- OCI-native — store models in any OCI registry.
- Reproducible — model, config, and code travel together.
- Registry agnostic — works with Harbor, ECR, GHCR, and on-prem registries.
- Promotion friendly — tag ModelKits through environments like images.
Quick start
kitops init my-model
kitops pack --from ./bundle --tag registry.example.com/ml/my-model:1.0
kitops push registry.example.com/ml/my-model:1.0
When to use, when to skip
Use it when you want strict, registry-backed packaging for model delivery and promotion. Skip it if you only need an upstream model catalog like Hugging Face Hub or if your team hasn't standardized on OCI registries yet.
vs / alongside
| Tool | Role | Note |
|---|---|---|
| KitOps | Model packaging in OCI | Portable bundles |
| ORAS | Generic OCI artifact push/pull | Lower-level primitive |
| Hugging Face Hub | Hosted registry | Upstream discovery |
| KServe | Serving layer | Consumes packaged artifacts |
References
- KitOps — project home.
- KitOps docs — ModelKit and CLI.
- KitOps-ML/kitops — source.
Extra reads
- KitOps blog — packaging patterns.
Verified against KitOps docs, May 2026.