kitops
KitOps is an open-source CNCF tool that packages AI/ML models, datasets, code, and configuration into versioned OCI artifacts for secure storage in container registries. It enables teams to version, sign, and audit AI assets throughout their lifecycle—from development through production—using standard container infrastructure.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | kitops-ml/kitops |
| Owner | kitops-ml |
| Primary language | Go |
| License | Apache-2.0 — OSI-approved |
| Stars | 1.4k |
| Forks | 177 |
| Open issues | 50 |
| Latest release | v1.15.0 (2026-06-25) |
| Last updated | 2026-07-06 |
| Source | https://github.com/kitops-ml/kitops |
What kitops is
KitOps provides a CLI and Python SDK to create ModelKits (OCI-compliant, layered artifacts) via a declarative Kitfile, supporting immutable SHA-256 digests, Cosign cryptographic signing, and selective layer unpacking. Built in Go, it integrates with existing OCI registries and CI/CD pipelines, with transparent support for both ModelKit and CNCF ModelPack formats.
Get the kitops source
Clone the repository and explore it locally.
git clone https://github.com/kitops-ml/kitops.gitcd kitops# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires an OCI-compliant container registry (Docker Hub, ECR, GCR, Artifactory, etc.) already in place; no managed registry is provided.
- Kitfile syntax and ModelKit structure must align with your ML project layout; use `kit init` to auto-generate, then customize for code, data, and model paths.
- Integration with CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) requires custom scripting to call `kit pack`, `kit push`, and optional signing with Cosign; templates are provided but not pre-built plugins for all platforms.
- Cosign signing is optional but strongly recommended for production; key management (HSM, cloud KMS, or local) is your responsibility.
- Team training on Kitfile semantics, OCI digest verification, and attestation workflows is necessary for adoption beyond initial pilot projects.
When to avoid it — and what to weigh
- You need out-of-the-box model serving or inference — KitOps is a packaging and versioning tool, not a serving runtime. You still need MLflow, KServe, Ray Serve, or similar to actually deploy and run inference.
- You require Windows-native CLI without containerization — While the tool supports Windows, its tight coupling to OCI registries and container workflows makes it less suited for teams with no existing container infrastructure.
- Your workflow is entirely cloud-hosted SaaS (e.g., Hugging Face Spaces only) — KitOps assumes you control or have access to an OCI-compliant registry (Docker Hub, ECR, GCR, Artifactory, etc.); pure SaaS-only workflows may find this constraint limiting.
- You need built-in model performance benchmarking or monitoring — KitOps packages and versions artifacts but does not include runtime performance tracking, drift detection, or operational monitoring capabilities.
License & commercial use
KitOps is licensed under Apache License 2.0, a permissive OSI-approved open-source license. The license permits commercial use, modification, and distribution provided Apache 2.0 obligations (license inclusion, notice of changes) are met.
Apache 2.0 permits commercial use. No vendor lock-in or proprietary features block commercial deployment. However, use of external tools (Cosign, Rekor, registries) may have separate commercial terms. No warranty or liability guarantees are provided under the open-source license; commercial support and SLAs are not included in the GitHub repository.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
KitOps provides immutable digests (SHA-256), Cosign signing support, and audit-ready lineage metadata suitable for compliance frameworks (EU AI Act, NIST AI RMF, ISO 42001). No built-in model vulnerability scanning; relies on optional external tools (Jozu Hub, third-party scanners). Supply chain integrity depends on correct registry authentication, key management, and CI/CD gating policies—all user-configured. AI SBOM generation is supported but output format and completeness depend on Kitfile accuracy.
Alternatives to consider
MLflow Models + OCI Registry
MLflow provides versioning and packaging but requires custom OCI wrapping; less specialized for AI supply chain and attestation workflows than KitOps.
Hugging Face Hub with signing (e.g., via third-party tools)
Hosted, managed registry with community models; simpler UX for public sharing but locks you into Hugging Face infrastructure and lacks fine-grained audit/attestation features.
OpenShift / Kubernetes native artifact storage (e.g., via Kyverno)
Tighter Kubernetes integration but steeper learning curve and less tailored to ML/AI workflows; suitable for Kubernetes-only shops.
Build on kitops with DEV.co software developers
Start with the 15-minute quickstart at kitops.org, install the CLI, and pack your first ModelKit. For enterprise security workflows, review signing and attestation best practices.
Talk to DEV.coRelated on DEV.co
Explore the category and the services that help you build with it.
kitops FAQ
Does KitOps replace my model registry (MLflow, Weights & Biases)?
Can I use KitOps without a container registry?
Is signing (Cosign) mandatory?
What if my model is larger than my registry's layer limit?
Software development & web development with DEV.co
Adopting kitops is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source devops software in production.
Ready to version your AI/ML projects with KitOps?
Start with the 15-minute quickstart at kitops.org, install the CLI, and pack your first ModelKit. For enterprise security workflows, review signing and attestation best practices.