diff --git a/litellm/llms/sagemaker.py b/litellm/llms/sagemaker.py
new file mode 100644
index 0000000000..250383b2cf
--- /dev/null
+++ b/litellm/llms/sagemaker.py
@@ -0,0 +1,103 @@
+import os
+import json
+from enum import Enum
+import requests
+import time
+from typing import Callable
+from litellm.utils import ModelResponse
+
+class SagemakerError(Exception):
+    def __init__(self, status_code, message):
+        self.status_code = status_code
+        self.message = message
+        super().__init__(
+            self.message
+        )  # Call the base class constructor with the parameters it needs
+
+def completion(
+    model: str,
+    messages: list,
+    model_response: ModelResponse,
+    print_verbose: Callable,
+    encoding,
+    api_key,
+    logging_obj,
+    optional_params=None,
+    litellm_params=None,
+    logger_fn=None,
+):
+
+    model = model
+    prompt = ""
+    for message in messages:
+        if "role" in message:
+            if message["role"] == "user":
+                prompt += (
+                    f"{message['content']}"
+                )
+            else:
+                prompt += (
+                    f"{message['content']}"
+                )
+        else:
+            prompt += f"{message['content']}"
+    data = {
+        "prompt": prompt,
+        # "instruction": prompt, # some baseten models require the prompt to be passed in via the 'instruction' kwarg
+        **optional_params,
+    }
+
+    ## LOGGING
+    logging_obj.pre_call(
+            input=prompt,
+            api_key=api_key,
+            additional_args={"complete_input_dict": data},
+        )
+    ## COMPLETION CALL
+    response = requests.post(
+        "https://api.ai21.com/studio/v1/" + model + "/complete", headers=headers, data=json.dumps(data)
+    )
+    if "stream" in optional_params and optional_params["stream"] == True:
+        return response.iter_lines()
+    else:
+        ## LOGGING
+        logging_obj.post_call(
+                input=prompt,
+                api_key=api_key,
+                original_response=response.text,
+                additional_args={"complete_input_dict": data},
+            )
+        print_verbose(f"raw model_response: {response.text}")
+        ## RESPONSE OBJECT
+        completion_response = response.json()
+        if "error" in completion_response:
+            raise SagemakerError(
+                message=completion_response["error"],
+                status_code=response.status_code,
+            )
+        else:
+            try:
+                model_response["choices"][0]["message"]["content"] = completion_response["completions"][0]["data"]["text"]
+            except:
+                raise SagemakerError(message=json.dumps(completion_response), status_code=response.status_code)
+
+        ## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here. 
+        prompt_tokens = len(
+            encoding.encode(prompt)
+        ) 
+        completion_tokens = len(
+            encoding.encode(model_response["choices"][0]["message"]["content"])
+        )
+
+        model_response["created"] = time.time()
+        model_response["model"] = model
+        model_response["usage"] = {
+            "prompt_tokens": prompt_tokens,
+            "completion_tokens": completion_tokens,
+            "total_tokens": prompt_tokens + completion_tokens,
+        }
+        return model_response
+
+def embedding():
+    # logic for parsing in - calling - parsing out model embedding calls
+    pass
diff --git a/litellm/main.py b/litellm/main.py
index c1c5be6b0e..d1ae00fa0f 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -22,6 +22,7 @@ from litellm.utils import (
 from .llms import anthropic
 from .llms import together_ai
 from .llms import ai21
+from .llms import sagemaker
 from .llms.huggingface_restapi import HuggingfaceRestAPILLM
 from .llms.baseten import BasetenLLM
 from .llms.aleph_alpha import AlephAlphaLLM
@@ -680,6 +681,32 @@ def completion(
             
             ## RESPONSE OBJECT
             response = model_response
+        elif custom_llm_provider == "sagemaker":
+            # boto3 reads keys from .env
+            model_response = sagemaker.completion(
+                model=model,
+                messages=messages,
+                model_response=model_response,
+                print_verbose=print_verbose,
+                optional_params=optional_params,
+                litellm_params=litellm_params,
+                logger_fn=logger_fn,
+                encoding=encoding,
+                api_key=ai21_key,
+                logging_obj=logging
+            )
+            
+            # TODO: Add streaming for sagemaker
+            # if "stream" in optional_params and optional_params["stream"] == True:
+            #     # don't try to access stream object,
+            #     response = CustomStreamWrapper(
+            #         model_response, model, custom_llm_provider="ai21", logging_obj=logging
+            #     )
+            #     return response
+            
+            ## RESPONSE OBJECT
+            response = model_response
+
         elif custom_llm_provider == "ollama":
             endpoint = (
                 litellm.api_base if litellm.api_base is not None else api_base