diff --git a/litellm/llms/openai.py b/litellm/llms/openai.py
index d2c5b6ff4..3f15e2f69 100644
--- a/litellm/llms/openai.py
+++ b/litellm/llms/openai.py
@@ -323,7 +323,26 @@ class OpenAIChatCompletion(BaseLLM):
                     raise OpenAIError(status_code=408, message=f"{type(e).__name__}")
                 else:
                     raise OpenAIError(status_code=500, message=f"{str(e)}")
-                
+    async def aembedding(
+            self, 
+            data: dict, 
+            model_response: ModelResponse, 
+            timeout: float,
+            api_key: Optional[str]=None,
+            api_base: Optional[str]=None,
+            client=None,
+            max_retries=None,
+        ): 
+        response = None
+        try: 
+            if client is None:
+                openai_aclient = AsyncOpenAI(api_key=api_key, base_url=api_base, http_client=litellm.aclient_session, timeout=timeout, max_retries=max_retries)
+            else:
+                openai_aclient = client
+            response = await openai_aclient.embeddings.create(**data)
+            return convert_to_model_response_object(response_object=json.loads(response.model_dump_json()), model_response_object=model_response, response_type="embedding")
+        except Exception as e:
+            raise e
     def embedding(self,
                 model: str,
                 input: list,
@@ -334,6 +353,7 @@ class OpenAIChatCompletion(BaseLLM):
                 logging_obj=None,
                 optional_params=None,
                 client=None,
+                aembedding=None,
                 ):
         super().embedding()
         exception_mapping_worked = False
@@ -347,6 +367,9 @@ class OpenAIChatCompletion(BaseLLM):
             max_retries = data.pop("max_retries", 2)
             if not isinstance(max_retries, int): 
                 raise OpenAIError(status_code=422, message="max retries must be an int")
+            if aembedding == True:
+                response =  self.aembedding(data=data, model_response=model_response, api_base=api_base, api_key=api_key, timeout=timeout, client=client, max_retries=max_retries)
+                return response
             if client is None:
                 openai_client = OpenAI(api_key=api_key, base_url=api_base, http_client=litellm.client_session, timeout=timeout, max_retries=max_retries)
             else:
diff --git a/litellm/main.py b/litellm/main.py
index 03807b9d7..61552eead 100644
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -1668,17 +1668,43 @@ async def aembedding(*args, **kwargs):
     - `response` (Any): The response returned by the `embedding` function.
     """
     loop = asyncio.get_event_loop()
+    model = args[0] if len(args) > 0 else kwargs["model"]
+    ### PASS ARGS TO Embedding ### 
+    kwargs["aembedding"] = True
+    custom_llm_provider = None
+    try: 
+        # Use a partial function to pass your keyword arguments
+        func = partial(embedding, *args, **kwargs)
 
-    # Use a partial function to pass your keyword arguments
-    func = partial(embedding, *args, **kwargs)
+        # Add the context to the function
+        ctx = contextvars.copy_context()
+        func_with_context = partial(ctx.run, func)
 
-    # Add the context to the function
-    ctx = contextvars.copy_context()
-    func_with_context = partial(ctx.run, func)
+        _, custom_llm_provider, _, _ = get_llm_provider(model=model, api_base=kwargs.get("api_base", None))
 
-    # Call the synchronous function using run_in_executor
-    response =  await loop.run_in_executor(None, func_with_context)
-    return response
+        if (custom_llm_provider == "openai" 
+            or custom_llm_provider == "azure" 
+            or custom_llm_provider == "custom_openai"
+            or custom_llm_provider == "anyscale"
+            or custom_llm_provider == "openrouter"
+            or custom_llm_provider == "deepinfra"
+            or custom_llm_provider == "perplexity"
+            or custom_llm_provider == "huggingface"): # currently implemented aiohttp calls for just azure and openai, soon all. 
+            # Await normally
+            init_response = await loop.run_in_executor(None, func_with_context)
+            if isinstance(init_response, dict) or isinstance(init_response, ModelResponse): ## CACHING SCENARIO 
+                response = init_response
+            elif asyncio.iscoroutine(init_response):
+                response = await init_response
+        else: 
+            # Call the synchronous function using run_in_executor
+            response =  await loop.run_in_executor(None, func_with_context)
+        return response
+    except Exception as e: 
+        custom_llm_provider = custom_llm_provider or "openai"
+        raise exception_type(
+                model=model, custom_llm_provider=custom_llm_provider, original_exception=e, completion_kwargs=args,
+            )
 
 @client
 def embedding(
@@ -1725,6 +1751,7 @@ def embedding(
     client = kwargs.pop("client", None)
     rpm = kwargs.pop("rpm", None)
     tpm = kwargs.pop("tpm", None)
+    aembedding = kwargs.pop("aembedding", None)
 
     optional_params = {}
     for param in kwargs:
@@ -1809,7 +1836,8 @@ def embedding(
                 timeout=timeout,
                 model_response=EmbeddingResponse(), 
                 optional_params=optional_params,
-                client=client
+                client=client,
+                aembedding=aembedding,
             )
         elif model in litellm.cohere_embedding_models:
             cohere_key = (
diff --git a/litellm/utils.py b/litellm/utils.py
index b80732d49..924e14f5c 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -1303,6 +1303,8 @@ def client(original_function):
                     return result
             elif "acompletion" in kwargs and kwargs["acompletion"] == True: 
                 return result
+            elif "aembedding" in kwargs and kwargs["aembedding"] == True: 
+                return result
             
             ### POST-CALL RULES ### 
             post_call_processing(original_response=result, model=model)