diff --git a/docs/my-website/docs/proxy/deploy.md b/docs/my-website/docs/proxy/deploy.md
index 496bde05f1..655d93b6b0 100644
--- a/docs/my-website/docs/proxy/deploy.md
+++ b/docs/my-website/docs/proxy/deploy.md
@@ -28,7 +28,7 @@ docker run ghcr.io/berriai/litellm:main-latest
 
 <TabItem value="cli" label="With CLI Args">
 
-### Run with LiteLLM CLI args
+#### Run with LiteLLM CLI args
 
 See all supported CLI args [here](https://docs.litellm.ai/docs/proxy/cli): 
 
@@ -129,13 +129,25 @@ spec:
             name: litellm-config-file
 ```
 
-> [!TIP]
-> To avoid issues with predictability, difficulties in rollback, and inconsistent environments, use versioning or SHA digests (for example, `litellm:main-v1.30.3` or `litellm@sha256:12345abcdef...`) instead of `litellm:main-latest`.
+:::info
+To avoid issues with predictability, difficulties in rollback, and inconsistent environments, use versioning or SHA digests (for example, `litellm:main-v1.30.3` or `litellm@sha256:12345abcdef...`) instead of `litellm:main-latest`.
+:::
 
 </TabItem>
 
 </Tabs>
 
+**That's it ! That's the quick start to deploy litellm**
+
+## Options to deploy LiteLLM 
+
+| Method | When to Use | Docs |
+| --- | --- | --- |
+| LiteLLM - Quick Start | call 100+ LLMs + Load Balancing  | [Quick Start](#quick-start) |
+| LiteLLM Database container + PostgresDB  | + use Virtual Keys + Track Spend  | [Deploy with Database](#deploy-with-database) |
+| LiteLLM container + Redis | + load balance across multiple litellm containers | [LiteLLM container + Redis](#litellm-container--redis) |
+| LiteLLM Database container + PostgresDB + Redis | + use Virtual Keys + Track Spend + load balance across multiple litellm containers | [LiteLLM container + Redis](#litellm-container--redis) |
+
 ## Deploy with Database
 
 We maintain a [seperate Dockerfile](https://github.com/BerriAI/litellm/pkgs/container/litellm-database) for reducing build time when running LiteLLM proxy with a connected Postgres Database 
@@ -159,7 +171,7 @@ Your OpenAI proxy server is now running on `http://0.0.0.0:4000`.
 </TabItem>
 <TabItem value="kubernetes-deploy" label="Kubernetes">
 
-### Step 1. Create deployment.yaml
+#### Step 1. Create deployment.yaml
 
 ```yaml
    apiVersion: apps/v1
@@ -188,7 +200,7 @@ Your OpenAI proxy server is now running on `http://0.0.0.0:4000`.
 kubectl apply -f /path/to/deployment.yaml
 ```
 
-### Step 2. Create service.yaml 
+#### Step 2. Create service.yaml 
 
 ```yaml
 apiVersion: v1
@@ -209,7 +221,7 @@ spec:
 kubectl apply -f /path/to/service.yaml
 ```
 
-### Step 3. Start server
+#### Step 3. Start server
 
 ```
 kubectl port-forward service/litellm-service 4000:4000
@@ -220,13 +232,13 @@ Your OpenAI proxy server is now running on `http://0.0.0.0:4000`.
 </TabItem>
 <TabItem value="helm-deploy" label="Helm">
 
-### Step 1. Clone the repository
+#### Step 1. Clone the repository
 
 ```bash
 git clone https://github.com/BerriAI/litellm.git
 ```
 
-### Step 2. Deploy with Helm
+#### Step 2. Deploy with Helm
 
 ```bash
 helm install \
@@ -235,7 +247,7 @@ helm install \
   deploy/charts/litellm
 ```
 
-### Step 3. Expose the service to localhost
+#### Step 3. Expose the service to localhost
 
 ```bash
 kubectl \
@@ -249,6 +261,38 @@ Your OpenAI proxy server is now running on `http://127.0.0.1:8000`.
 </TabItem>
 </Tabs>
 
+## LiteLLM container + Redis
+Use Redis when you need litellm to load balance across multiple litellm containers
+
+The only change required is setting Redis on your `config.yaml`
+LiteLLM Proxy supports sharing rpm/tpm shared across multiple litellm instances, pass `redis_host`, `redis_password` and `redis_port` to enable this. (LiteLLM will use Redis to track rpm/tpm usage )
+
+```yaml
+model_list:
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: azure/<your-deployment-name>
+      api_base: <your-azure-endpoint>
+      api_key: <your-azure-api-key>
+      rpm: 6      # Rate limit for this deployment: in requests per minute (rpm)
+  - model_name: gpt-3.5-turbo
+    litellm_params:
+      model: azure/gpt-turbo-small-ca
+      api_base: https://my-endpoint-canada-berri992.openai.azure.com/
+      api_key: <your-azure-api-key>
+      rpm: 6
+router_settings:
+  redis_host: <your redis host>
+  redis_password: <your redis password>
+  redis_port: 1992
+```
+
+Start docker container with config
+
+```shell
+docker run ghcr.io/berriai/litellm:main-latest --config your_config.yaml
+```
+
 ## Best Practices for Deploying to Production
 ### 1. Switch of debug logs in production 
 don't use [`--detailed-debug`, `--debug`](https://docs.litellm.ai/docs/proxy/debugging#detailed-debug) or `litellm.set_verbose=True`. We found using debug logs can add 5-10% latency per LLM API call
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 33720b43eb..62a2d38424 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -42,10 +42,6 @@ const sidebars = {
         "proxy/team_based_routing",
         "proxy/ui",
         "proxy/budget_alerts",
-        "proxy/model_management",
-        "proxy/health",
-        "proxy/debugging",
-        "proxy/pii_masking",
         {
           "type": "category",
           "label": "🔥 Load Balancing",
@@ -54,6 +50,10 @@ const sidebars = {
             "proxy/reliability",
           ]
         },
+        "proxy/model_management",
+        "proxy/health",
+        "proxy/debugging",
+        "proxy/pii_masking",
         "proxy/caching",
         {
           "type": "category",