Why Model Packaging Isn’t Enough

Docker’s recent release of Docker Model Runner sends a clear signal: AI developers want to package models like they package code. But Docker’s approach—like much of its tools is built for local development, not for the distributed, iterative, production-grade workflows that AI demands.

This isn’t a knock on Docker. It’s a recognition of its scope. If your goal is to run a model on your laptop or share it with a teammate, Model Runner might suffice. But if you're serious about advancing models through development, staging, and production pipelines, you need something much more robust.

Where Docker Model Runner Falls Short in AI Workflows

Docker containers are excellent for application-level reproducibility. But AI models are not applications—they’re evolving systems that require:

• Datasets, training configurations, and preprocessing logic

• Dependencies tied to specific frameworks and hardware

• Lifecycle management: versioning, governance, model evaluation, rollback

• Operational needs: GPU scheduling, autoscaling, routing, monitoring

Docker’s Model Runner abstracts only the model weights, enough to get started, but insufficient for production use.

ModelKits: Purpose-Built for AI

KitOps introduces ModelKits: immutable, composable packages that include not just model weights, but everything needed to reproduce and operate that model across environments.

Each ModelKit includes:

• Model binaries

• Training and inference code

• Configuration files

• Datasets

• Evaluation results

This is more than packaging—it’s reproducibility and lifecycle control, baked into the artifact.

What ModelKits Add to the Picture

Docker’s Model Runner is a helpful starting point—a general-purpose tool now being applied to the evolving domain of AI models. It’s designed for:

• Local usage

• Simplified packaging

• Ad-hoc inference serving

ModelKits are designed for:

• Full ML lifecycle reproducibility

• Multi-stage workflows and pipelines

• Collaboration across research, infra, and product teams

• Seamless transitions across dev, staging, and production

• KitOps supports local-first workflows too. With kit dev, you can spin up an inference server

When paired with a registry and KitOps tooling, ModelKits integrate natively with CI/CD pipelines, distributed inference runtimes, and governance platforms.

Shipping AI Products? Choose the Right Foundation

If you're solo, prototyping, and just need a quick way to serve a model—Docker’s Model Runner will work.

But if you’re running real experiments, versioning datasets, collaborating with others, deploying across environments, or managing audit trails—you need something built for the job.

You need KitOps.

You need ModelKits.

Because packaging the model is just the beginning.

What happens after is where real AI engineering begins.