diff --git a/components/backends/sglang/README.md b/components/backends/sglang/README.md
index a7471b5c32..236e0f1e77 100644
--- a/components/backends/sglang/README.md
+++ b/components/backends/sglang/README.md
@@ -173,10 +173,10 @@ Below we provide a selected list of advanced examples. Please open up an issue i
 
 ## Deployment
 
-We currently provide deployment examples for Kubernetes (coming soon!) and SLURM
+We currently provide deployment examples for Kubernetes and SLURM.
 
 ## Kubernetes
-- **[Deploying Dynamo with SGLang on Kubernetes - coming soon!](.)**
+- **[Deploying Dynamo with SGLang on Kubernetes](deploy/README.md)**
 
 ## SLURM
 - **[Deploying Dynamo with SGLang on SLURM](slurm_jobs/README.md)**
diff --git a/components/backends/sglang/deploy/README.md b/components/backends/sglang/deploy/README.md
new file mode 100644
index 0000000000..4b2c9fcff4
--- /dev/null
+++ b/components/backends/sglang/deploy/README.md
@@ -0,0 +1,136 @@
+# SGLang Kubernetes Deployment Configurations
+
+This directory contains Kubernetes Custom Resource Definition (CRD) templates for deploying SGLang inference graphs using the **DynamoGraphDeployment** resource.
+
+## Available Deployment Patterns
+
+### 1. **Aggregated Deployment** (`agg.yaml`)
+Basic deployment pattern with frontend and a single decode worker.
+
+**Architecture:**
+- `Frontend`: OpenAI-compatible API server
+- `SGLangDecodeWorker`: Single worker handling both prefill and decode
+
+### 2. **Aggregated Router Deployment** (`agg_router.yaml`)
+Enhanced aggregated deployment with KV cache routing capabilities.
+
+**Architecture:**
+- `Frontend`: OpenAI-compatible API server with router mode enabled (`--router-mode kv`)
+- `SGLangDecodeWorker`: Single worker handling both prefill and decode
+
+### 3. **Disaggregated Deployment** (`disagg.yaml`)**
+High-performance deployment with separated prefill and decode workers.
+
+**Architecture:**
+- `Frontend`: HTTP API server coordinating between workers
+- `SGLangDecodeWorker`: Specialized decode-only worker (`--disaggregation-mode decode`)
+- `SGLangPrefillWorker`: Specialized prefill-only worker (`--disaggregation-mode prefill`)
+- Communication via NIXL transfer backend (`--disaggregation-transfer-backend nixl`)
+
+## CRD Structure
+
+All templates use the **DynamoGraphDeployment** CRD:
+
+```yaml
+apiVersion: nvidia.com/v1alpha1
+kind: DynamoGraphDeployment
+metadata:
+  name: <deployment-name>
+spec:
+  services:
+    <ServiceName>:
+      # Service configuration
+```
+
+### Key Configuration Options
+
+**Resource Management:**
+```yaml
+resources:
+  requests:
+    cpu: "10"
+    memory: "20Gi"
+    gpu: "1"
+  limits:
+    cpu: "10"
+    memory: "20Gi"
+    gpu: "1"
+```
+
+**Container Configuration:**
+```yaml
+extraPodSpec:
+  mainContainer:
+    image: my-registry/sglang-runtime:my-tag
+    workingDir: /workspace/components/backends/sglang
+    args:
+      - "python3"
+      - "-m"
+      - "dynamo.sglang.worker"
+      # Model-specific arguments
+```
+
+## Prerequisites
+
+Before using these templates, ensure you have:
+
+1. **Dynamo Cloud Platform installed** - See [Installing Dynamo Cloud](../../docs/guides/dynamo_deploy/dynamo_cloud.md)
+2. **Kubernetes cluster with GPU support**
+3. **Container registry access** for SGLang runtime images
+4. **HuggingFace token secret** (referenced as `envFromSecret: hf-token-secret`)
+
+## Usage
+
+### 1. Choose Your Template
+Select the deployment pattern that matches your requirements:
+- Use `agg.yaml` for development/testing
+- Use `agg_router.yaml` for production with load balancing
+- Use `disagg.yaml` for maximum performance
+
+### 2. Customize Configuration
+Edit the template to match your environment:
+
+```yaml
+# Update image registry and tag
+image: your-registry/sglang-runtime:your-tag
+
+# Configure your model
+args:
+  - "--model-path"
+  - "your-org/your-model"
+  - "--served-model-name"
+  - "your-org/your-model"
+```
+
+### 3. Deploy
+```bash
+kubectl apply -f <your-template>.yaml
+```
+
+## Model Configuration
+
+All templates use **DeepSeek-R1-Distill-Llama-8B** as the default model. But you can use any sglang argument and configuration. Key parameters:
+
+## Monitoring and Health
+
+- **Frontend health endpoint**: `http://<frontend-service>:8000/health`
+- **Liveness probes**: Check process health every 60s
+
+## Further Reading
+
+- **Deployment Guide**: [Creating Kubernetes Deployments](../../../../docs/guides/dynamo_deploy/create_deployment.md)
+- **Quickstart**: [Deployment Quickstart](../../../../docs/guides/dynamo_deploy/quickstart.md)
+- **Platform Setup**: [Dynamo Cloud Installation](../../../../docs/guides/dynamo_deploy/dynamo_cloud.md)
+- **Examples**: [Deployment Examples](../../../../docs/examples/README.md)
+- **Kubernetes CRDs**: [Custom Resources Documentation](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/)
+
+## Troubleshooting
+
+Common issues and solutions:
+
+1. **Pod fails to start**: Check image registry access and HuggingFace token secret
+2. **GPU not allocated**: Verify cluster has GPU nodes and proper resource limits
+3. **Health check failures**: Review model loading logs and increase `initialDelaySeconds`
+4. **Out of memory**: Increase memory limits or reduce model batch size
+
+For additional support, refer to the [deployment troubleshooting guide](../../docs/guides/dynamo_deploy/quickstart.md#troubleshooting).