diff --git a/docs/my-website/docs/index.md b/docs/my-website/docs/index.md
index d7ed1401950..d3284e6fb95 100644
--- a/docs/my-website/docs/index.md
+++ b/docs/my-website/docs/index.md
@@ -13,7 +13,14 @@ 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 [OpenAI Proxy Server](https://docs.litellm.ai/docs/simple_proxy)
 
-## Basic usage 
+## How to use LiteLLM
+You can use litellm through either:
+1. [OpenAI proxy Server](#openai-proxy) - Server 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
+
+## 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"/>
 </a>
@@ -144,7 +151,7 @@ response = completion(
 
 </Tabs>
 
-## Streaming
+### Streaming
 Set `stream=True` in the `completion` args. 
 <Tabs>
 <TabItem value="openai" label="OpenAI">
@@ -276,7 +283,7 @@ 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. 
 
@@ -292,7 +299,7 @@ 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 Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack
 ```python
 from litellm import completion
@@ -311,7 +318,7 @@ litellm.success_callback = ["langfuse", "llmonitor"] # log input/output to langf
 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