diff --git a/docs/my-website/src/pages/index.md b/docs/my-website/src/pages/index.md
index d074fd376..291c51ab0 100644
--- a/docs/my-website/src/pages/index.md
+++ b/docs/my-website/src/pages/index.md
@@ -12,7 +12,41 @@ https://github.com/BerriAI/litellm
 - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
 - Track spend & set budgets per project [LiteLLM Proxy Server](https://docs.litellm.ai/docs/simple_proxy)
 
-## Basic usage
+## How to use LiteLLM
+You can use litellm through either:
+1. [LiteLLM Proxy Server](#litellm-proxy-server-llm-gateway) - Server (LLM Gateway) to call 100+ LLMs, load balance, cost tracking across projects
+2. [LiteLLM python SDK](#basic-usage) - Python Client to call 100+ LLMs, load balance, cost tracking
+
+### **When to use LiteLLM Proxy Server (LLM Gateway)**
+
+:::tip
+
+Use LiteLLM Proxy Server if you want a **central service (LLM Gateway) to access multiple LLMs**
+
+Typically used by Gen AI Enablement /  ML PLatform Teams
+
+:::
+
+  - LiteLLM Proxy gives you a unified interface to access multiple LLMs (100+ LLMs)
+  - Track LLM Usage and setup guardrails
+  - Customize Logging, Guardrails, Caching per project
+
+### **When to use LiteLLM Python SDK**
+
+:::tip
+
+  Use LiteLLM Python SDK if you want to use LiteLLM in your **python code**
+
+Typically used by developers building llm projects
+
+:::
+
+  - LiteLLM SDK gives you a unified interface to access multiple LLMs (100+ LLMs) 
+  - Retry/fallback logic across multiple deployments (e.g. Azure/OpenAI) - [Router](https://docs.litellm.ai/docs/routing)
+
+## **LiteLLM Python SDK**
+
+### Basic usage 
 
 <a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_Getting_Started.ipynb">
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
@@ -146,9 +180,9 @@ response = completion(
 
 </Tabs>
 
-## Streaming
+### Streaming
+Set `stream=True` in the `completion` args. 
 
-Set `stream=True` in the `completion` args.
 <Tabs>
 <TabItem value="openai" label="OpenAI">
 
@@ -280,7 +314,7 @@ response = completion(
 
 </Tabs>
 
-## Exception handling
+### Exception handling 
 
 LiteLLM maps exceptions across all supported providers to the OpenAI exceptions. All our exceptions inherit from OpenAI's exception types, so any error-handling you have for that, should work out of the box with LiteLLM.
 
@@ -296,8 +330,7 @@ except OpenAIError as e:
     print(e)
 ```
 
-## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
-
+### Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
 LiteLLM exposes pre defined callbacks to send data to Lunary, Langfuse, Helicone, Promptlayer, Traceloop, Slack
 
 ```python
@@ -312,14 +345,13 @@ os.environ["LUNARY_PUBLIC_KEY"] = "your-lunary-public-key"
 os.environ["OPENAI_API_KEY"]
 
 # set callbacks
-litellm.success_callback = ["langfuse", "lunary"] # log input/output to lunary, langfuse, supabase
+litellm.success_callback = ["lunary", "langfuse", "helicone"] # log input/output to lunary, langfuse, supabase, helicone
 
 #openai call
 response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
 ```
 
-## Track Costs, Usage, Latency for streaming
-
+### Track Costs, Usage, Latency for streaming
 Use a callback function for this - more info on custom callbacks: https://docs.litellm.ai/docs/observability/custom_callback
 
 ```python
@@ -352,7 +384,7 @@ response = completion(
 )
 ```
 
-## OpenAI Proxy
+## **LiteLLM Proxy Server (LLM Gateway)**
 
 Track spend across multiple projects/people
 
@@ -367,6 +399,8 @@ The proxy provides:
 
 ### 📖 Proxy Endpoints - [Swagger Docs](https://litellm-api.up.railway.app/)
 
+Go here for a complete tutorial with keys + rate limits - [**here**](./proxy/docker_quick_start.md)
+
 ### Quick Start Proxy - CLI
 
 ```shell
@@ -378,14 +412,14 @@ pip install 'litellm[proxy]'
 ```shell
 $ litellm --model huggingface/bigcode/starcoder
 
-#INFO: Proxy running on http://0.0.0.0:8000
+#INFO: Proxy running on http://0.0.0.0:4000
 ```
 
 #### Step 2: Make ChatCompletions Request to Proxy
 
 ```python
 import openai # openai v1.0.0+
-client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url
+client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:4000") # set proxy to base_url
 # request sent to model set on litellm proxy, `litellm --model`
 response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
     {
@@ -401,4 +435,5 @@ print(response)
 
 - [exception mapping](./exception_mapping.md)
 - [retries + model fallbacks for completion()](./completion/reliable_completions.md)
-- [proxy virtual keys & spend management](./tutorials/fallbacks.md)
+- [proxy virtual keys & spend management](./proxy/virtual_keys.md)
+- [E2E Tutorial for LiteLLM Proxy Server](./proxy/docker_quick_start.md)