KitOps
Open-source AI/ML packaging and version control tool, creating signed OCI standard artifacts for seamless container repository management
Visit Website ↗What is KitOps
KitOps is an open-source AI/ML packaging and version control tool designed to solve a practical problem: an AI project consists of not only models but also datasets, code, configurations, agent skills, and MCP servers, which are often scattered and version-mismatched, leading to chaos during handovers and reproducibility. KitOps packages these into "ModelKit" - signed, version-labeled OCI standard artifacts, stored in your existing container repository, such as Docker Hub, AWS ECR, Google GCR, or Harbor.
In other words, it leverages your existing container ecosystem without requiring a new, proprietary repository. Security is ensured through cryptographic signatures using Cosign and SHA-256 hashing, with optional partial downloads. KitOps provides CLI and Python SDK, integrable with CI/CD.
Key Features and Use Cases
KitOps is Apache 2.0 licensed, free, and open-source, with enterprise support provided by Jozu. It has been downloaded over 260,000 times and has been running in production environments for over 18 months. As part of the Kubeflow, KServe, and MLflow suite, KitOps is responsible for the "packaging" step, pushing ModelKit to OCI repositories for subsequent jobs or inference. Suitable for teams needing collaboration between data scientists, application developers, and SREs, particularly those with existing container and Kubernetes ecosystems looking to integrate AI products into the same governance framework.
Key Features
- Packages models, datasets, code, agent skills, and MCP servers into OCI standard artifacts
- Compatible with existing container repositories like Docker Hub, ECR, GCR, and Harbor
- Ensures security and trust through Cosign cryptographic signatures and SHA-256 hashing
- Optional partial downloads for efficient use of resources
- Provides CLI and Python SDK for integration with CI/CD pipelines
Pros
- Leverages existing container repositories, no need to learn new systems
- Complete version control and signing for reproducibility and handovers
- Apache 2.0 licensed, CNCF project, ensuring a neutral ecosystem
Cons
- Limited value for non-Kubernetes teams as a foundational infrastructure tool
- Requires combination with other tools like Kubeflow and KServe for complete functionality
- Has a learning curve due to its DevOps/MLOps engineering orientation
Use Cases
- Versioning and packaging AI/ML models and related artifacts
- Enabling data scientists, developers, and SREs to share the same artifacts
- Pushing ModelKit to OCI repositories for use in Kubeflow and KServe
- Automating AI artifact packaging and signing in CI/CD pipelines
Editor's Note
Among the plethora of MLOps tools, KitOps stands out by not reinventing the wheel - it utilizes OCI standards and your existing container repositories, integrating AI artifacts into your current governance framework, which is a significant advantage for teams already using Kubernetes. Being a CNCF project with Apache licensing also provides assurance against vendor lock-in. The trade-off is that it is a foundational tool that needs to be used in conjunction with other tools. We give it a rating of 4.0.
FAQ
Will KitOps replace MLflow?
No, they complement each other. KitOps is responsible for packaging and version control, pushing artifacts to OCI repositories, and does not replace experiment tracking or model serving.
Do I need to build a new repository?
No, KitOps is designed to work with your existing container repositories, such as Docker Hub, ECR, GCR, and Harbor, without the need for a new, proprietary system.