diff --git a/docs/my-website/docs/tutorials/sagemaker_llms.md b/docs/my-website/docs/tutorials/sagemaker_llms.md
deleted file mode 100644
index 1fe9594ab5..0000000000
--- a/docs/my-website/docs/tutorials/sagemaker_llms.md
+++ /dev/null
@@ -1,72 +0,0 @@
-import Image from '@theme/IdealImage';
-
-# Deploy & Query Llama2-7B on Sagemaker
-
-This tutorial has 2 major components:
-1. Deploy Llama2-7B on Jumpstart
-2. Use LiteLLM to Query Llama2-7B on Sagemaker
-
-## Deploying Llama2-7B on AWS Sagemaker
-### Pre-requisites
-Ensure you have AWS quota for deploying your selected LLM. You can apply for a quota increase here: https://console.aws.amazon.com/servicequotas/home
-* ml.g5.48xlarge
-* ml.g5.2xlarge
-
-### Create an Amazon SageMaker domain to use Studio and Studio Notebooks
-
-- Head to AWS console https://aws.amazon.com/console/ 
-- Navigate to AWS Sagemaker from the console
-- On AWS Sagemaker select 'Create a Sagemaker Domain' 
-<Image img={require('../../img/sagemaker_domain.png')} />
-
-### Deploying Llama2-7B using AWS Sagemaker Jumpstart
-
-- After creating your sagemaker domain, click 'Open Studio', which should take you to AWS sagemaker studio 
-
-- On the left sidebar navigate to SageMaker Jumpstart -> Models, notebooks, solutions
-- Now select the LLM you want to deploy by clicking 'View Model' - (in this case select Llama2-7B)
-<Image img={require('../../img/sagemaker_jumpstart.png')} />
-- Click `Deploy` for the Model you want to deploy
-<Image img={require('../../img/sagemaker_deploy.png')} />
-
-- After deploying Llama2, copy your model endpoint 
-<Image img={require('../../img/sagemaker_endpoint.png')} />
-
-### Use LiteLLM to Query Llama2-7B on Sagemaker
-
-#### Prerequisites
-* `pip install boto3`
-* `pip install litellm`
-* Create your AWS Access Key, get your `AWS_ACCESS_KEY_ID` and  `AWS_SECRET_ACCESS_KEY`. You can create a new aws access key on the aws console under `Security Credentials` under your profile
-
-#### Querying deployed Llama2-7b 
-Set `model` = `sagemaker/<your model endpoint>` for `completion`. Use the model endpoint you got after deploying llama2-7b on sagemaker. If you used jumpstart your model endpoint will look like this `jumpstart-dft-meta-textgeneration-llama-2-7b`
-
-Code Example:
-```python
-from litellm import completion
-os.environ['AWS_ACCESS_KEY_ID'] = "your-access-key-id"
-os.environ['AWS_SECRET_ACCESS_KEY'] = "your-secret-key"
-
-response = completion(
-            model="sagemaker/jumpstart-dft-meta-textgeneration-llama-2-7b", 
-            messages=[{'role': 'user', 'content': 'are you a llama'}],
-            temperature=0.2, # optional params
-            max_tokens=80,
-        )
-
-```
-
-That's it! Happy completion()!
-
-#### Next Steps: 
-- Add Caching: https://docs.litellm.ai/docs/caching/gpt_cache
-- Add Logging and Observability to your deployed LLM: https://docs.litellm.ai/docs/observability/callbacks
-
-
-
-
-
-
-
-
diff --git a/docs/my-website/sidebars.js b/docs/my-website/sidebars.js
index 3c3e1cbf97..ac9586d769 100644
--- a/docs/my-website/sidebars.js
+++ b/docs/my-website/sidebars.js
@@ -254,7 +254,6 @@ const sidebars = {
         "tutorials/huggingface_tutorial",
         "tutorials/TogetherAI_liteLLM",
         "tutorials/finetuned_chat_gpt",
-        "tutorials/sagemaker_llms",
         "tutorials/text_completion",
         "tutorials/first_playground",
         "tutorials/model_fallbacks",