diff --git a/docs/.vitepress/config.mts b/docs/.vitepress/config.mts
index 9fde09f2..286dce8f 100644
--- a/docs/.vitepress/config.mts
+++ b/docs/.vitepress/config.mts
@@ -84,6 +84,7 @@ export default defineConfig({
           { text: 'Overview', link: '/docs/overview' },
           { text: 'Get Started', link: '/docs/get-started' },
           { text: 'Next Steps', link: '/docs/next-steps' },
+          { text: 'Deploy ModelKits', link: '/docs/deploy' },
           { text: 'Kit Dev', link: '/docs/dev-mode' },
           { text: 'Why KitOps?', link: '/docs/why-kitops' },
           { text: 'How it is Used', link: '/docs/use-cases' },
diff --git a/docs/src/docs/get-started.md b/docs/src/docs/get-started.md
index bc0ba4ad..f6aa7ad5 100644
--- a/docs/src/docs/get-started.md
+++ b/docs/src/docs/get-started.md
@@ -8,6 +8,7 @@ In this guide, we'll use ModelKits and the kit CLI to easily:
 * Package up a model, notebook, and datasets into a single ModelKit you can share through your existing tools
 * Push that versioned ModelKit package to a registry
 * Grab only the assets you need from the ModelKit for testing, integration, local running, or deployment
+* Package the ModelKit as a container or Kubernetes deployment
 
 ## Before we start...
 
@@ -121,6 +122,10 @@ kit push jozu.ml/brad/quick-start:latest
 
 Note that some registries, like Jozu Hub, don't automatically create a repository. If you receive an error from your `push` command, make sure you have created the repository in your target registry and that you have push rights to the repository.
 
+### ModelKit to Container or Kubernetes
+
+You can build a container or Kubernetes deployment that pulls artifacts directly from the ModelKit. This makes automating container creation and Kubernetes deployment simple. Read more in our [deployment documentation](./deploy.md).
+
 ### Congratulations
 
 You've learned how to unpack a ModelKit, pack one up, and push it. Anyone with access to your remote repository can now pull your new ModelKit and start playing with your model using the `kit pull` or `kit unpack` commands.
diff --git a/docs/src/docs/overview.md b/docs/src/docs/overview.md
index 170cf100..79ca9310 100644
--- a/docs/src/docs/overview.md
+++ b/docs/src/docs/overview.md
@@ -6,7 +6,7 @@ KitOps is an innovative open-source project designed to enhance collaboration am
 
 ### 🎁 ModelKit
 
-At the heart of KitOps is the ModelKit, an OCI-compliant packaging format that enables the seamless sharing of all necessary artifacts involved in the AI/ML model lifecycle. This includes datasets, code, configurations, and the models themselves. By standardizing the way these components are packaged, ModelKit facilitates a more streamlined and collaborative development process that is compatible with nearly any tool.
+At the heart of KitOps is the ModelKit, an OCI-compliant packaging format that enables the seamless sharing of all necessary artifacts involved in the AI/ML model lifecycle. This includes datasets, code, configurations, and the models themselves. By standardizing the way these components are packaged, ModelKit facilitates a more streamlined and collaborative development process that is compatible with nearly any tool. You can even [deploy ModelKits to containers or Kubernetes](./deploy.md).
 
 ### 📄 Kitfile 
 
@@ -37,15 +37,19 @@ KitOps enables you to innovate in AI/ML without the usual infrastructure distrac
 KitOps is not just another tool; it's a comprehensive CLI and packaging system specifically designed for the AI/ML workflow. It acknowledges the nuanced needs of AI/ML projects, such as:
 
 ### 📊 Management of Unstructured Datasets
+
 AI/ML projects often deal with large, unstructured datasets, such as images, videos, and audio files. KitOps simplifies the versioning and sharing of these datasets, making them as manageable as traditional code.
 
 ### 🤝 Synchronized Data and Code Versioning
+
 One of the core strengths of KitOps is its ability to keep data and code versions in sync. This crucial feature solves the reproducibility issues that frequently arise in AI/ML development, ensuring consistency and reliability across project stages.
 
 ### 🚀 Deployment Ready
-Designed with a focus on deployment, ModelKits package assets in standard formats so they're compatible with nearly any tool - helping you get your model to production faster and more efficiently.
+
+Designed with a focus on deployment, ModelKits package assets in standard formats so you can depoloy them as [containers or to Kubernetes](./deploy.md). They're also [compatible with nearly any tool](./modelkit/compatibility.md) - helping you get your model to production faster and more efficiently.
 
 ### 🏭 Standards-Based Approach
+
 KitOps champions openness and interoperability through its core components, ensuring seamless integration into your existing workflows:
 
 ModelKits are designed as OCI (Open Container Initiative) artifacts, making them fully compatible with the Docker image registries and other OCI-compliant storage solutions you already use. This compatibility allows for an easy and familiar integration process. By adhering to widely accepted standards, KitOps ensures you're not tied to a single vendor or platform. This flexibility gives you the freedom to choose the best tools and services for your needs without being restricted by proprietary formats.