diff --git a/docs/my-website/docs/completion/token_usage.md b/docs/my-website/docs/completion/token_usage.md
index 807ccfd91ec..0bec6b3f902 100644
--- a/docs/my-website/docs/completion/token_usage.md
+++ b/docs/my-website/docs/completion/token_usage.md
@@ -1,7 +1,21 @@
 # Completion Token Usage & Cost
 By default LiteLLM returns token usage in all completion requests ([See here](https://litellm.readthedocs.io/en/latest/output/))
 
-However, we also expose some helper functions + **[NEW]** an API to calculate token usage across providers:
+LiteLLM returns `response_cost` in all calls. 
+
+```python
+from litellm import completion 
+
+response = litellm.completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+            mock_response="Hello world",
+        )
+
+print(response._hidden_params["response_cost"])
+```
+
+LiteLLM also exposes some helper functions:
 
 - `encode`: This encodes the text passed in, using the model-specific tokenizer. [**Jump to code**](#1-encode)
 
@@ -23,7 +37,7 @@ However, we also expose some helper functions + **[NEW]** an API to calculate to
 
 - `api.litellm.ai`: Live token + price count across [all supported models](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). [**Jump to code**](#10-apilitellmai)
 
-📣 This is a community maintained list. Contributions are welcome! ❤️
+📣 [This is a community maintained list](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). Contributions are welcome! ❤️
 
 ## Example Usage 
 
diff --git a/docs/my-website/docs/proxy/cost_tracking.md b/docs/my-website/docs/proxy/cost_tracking.md
index f01e1042e35..3ccf8f383aa 100644
--- a/docs/my-website/docs/proxy/cost_tracking.md
+++ b/docs/my-website/docs/proxy/cost_tracking.md
@@ -114,6 +114,14 @@ print(response)
 **Step3 - Verify Spend Tracked**
 That's IT. Now Verify your spend was tracked
 
+<Tabs>
+<TabItem value="curl" label="Response Headers">
+
+<Image img={require('../../img/response_cost_img.png')} />
+
+</TabItem>
+<TabItem value="db" label="DB + UI">
+
 The following spend gets tracked in Table `LiteLLM_SpendLogs`
 
 ```json
@@ -144,6 +152,10 @@ Use the `/global/spend/report` endpoint to get daily spend report per
 - team
 - customer [this is `user` passed to `/chat/completions` request](#how-to-track-spend-with-litellm)
 
+</TabItem>
+</Tabs>
+
+
 <Tabs>
 
 <TabItem value="per team" label="Spend Per Team">
diff --git a/docs/my-website/img/response_cost_img.png b/docs/my-website/img/response_cost_img.png
new file mode 100644
index 00000000000..9f466b3fbbc
Binary files /dev/null and b/docs/my-website/img/response_cost_img.png differ
diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index 2504a95f145..9a7df7ebefb 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -572,7 +572,6 @@ def completion_cost(
                 completion_string = litellm.utils.get_response_string(
                     response_obj=completion_response
                 )
-
                 completion_characters = litellm.utils._count_characters(
                     text=completion_string
                 )
@@ -610,7 +609,7 @@ def response_cost_calculator(
         TextCompletionResponse,
     ],
     model: str,
-    custom_llm_provider: str,
+    custom_llm_provider: Optional[str],
     call_type: Literal[
         "embedding",
         "aembedding",
@@ -632,6 +631,10 @@ def response_cost_calculator(
     base_model: Optional[str] = None,
     custom_pricing: Optional[bool] = None,
 ) -> Optional[float]:
+    """
+    Returns
+    - float or None: cost of response OR none if error.
+    """
     try:
         response_cost: float = 0.0
         if cache_hit is not None and cache_hit is True:
diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index 710b3d11d81..553c8a48cb4 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -433,6 +433,7 @@ def get_custom_headers(
     api_base: Optional[str] = None,
     version: Optional[str] = None,
     model_region: Optional[str] = None,
+    response_cost: Optional[Union[float, str]] = None,
     fastest_response_batch_completion: Optional[bool] = None,
     **kwargs,
 ) -> dict:
@@ -443,6 +444,7 @@ def get_custom_headers(
         "x-litellm-model-api-base": api_base,
         "x-litellm-version": version,
         "x-litellm-model-region": model_region,
+        "x-litellm-response-cost": str(response_cost),
         "x-litellm-key-tpm-limit": str(user_api_key_dict.tpm_limit),
         "x-litellm-key-rpm-limit": str(user_api_key_dict.rpm_limit),
         "x-litellm-fastest_response_batch_completion": (
@@ -3048,6 +3050,7 @@ async def chat_completion(
         model_id = hidden_params.get("model_id", None) or ""
         cache_key = hidden_params.get("cache_key", None) or ""
         api_base = hidden_params.get("api_base", None) or ""
+        response_cost = hidden_params.get("response_cost", None) or ""
         fastest_response_batch_completion = hidden_params.get(
             "fastest_response_batch_completion", None
         )
@@ -3066,6 +3069,7 @@ async def chat_completion(
                 cache_key=cache_key,
                 api_base=api_base,
                 version=version,
+                response_cost=response_cost,
                 model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
                 fastest_response_batch_completion=fastest_response_batch_completion,
             )
@@ -3095,6 +3099,7 @@ async def chat_completion(
                 cache_key=cache_key,
                 api_base=api_base,
                 version=version,
+                response_cost=response_cost,
                 model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
                 fastest_response_batch_completion=fastest_response_batch_completion,
                 **additional_headers,
@@ -3290,6 +3295,7 @@ async def completion(
         model_id = hidden_params.get("model_id", None) or ""
         cache_key = hidden_params.get("cache_key", None) or ""
         api_base = hidden_params.get("api_base", None) or ""
+        response_cost = hidden_params.get("response_cost", None) or ""
 
         ### ALERTING ###
         data["litellm_status"] = "success"  # used for alerting
@@ -3304,6 +3310,7 @@ async def completion(
                 cache_key=cache_key,
                 api_base=api_base,
                 version=version,
+                response_cost=response_cost,
             )
             selected_data_generator = select_data_generator(
                 response=response,
@@ -3323,6 +3330,7 @@ async def completion(
                 cache_key=cache_key,
                 api_base=api_base,
                 version=version,
+                response_cost=response_cost,
             )
         )
 
@@ -3527,6 +3535,7 @@ async def embeddings(
         model_id = hidden_params.get("model_id", None) or ""
         cache_key = hidden_params.get("cache_key", None) or ""
         api_base = hidden_params.get("api_base", None) or ""
+        response_cost = hidden_params.get("response_cost", None) or ""
 
         fastapi_response.headers.update(
             get_custom_headers(
@@ -3535,6 +3544,7 @@ async def embeddings(
                 cache_key=cache_key,
                 api_base=api_base,
                 version=version,
+                response_cost=response_cost,
                 model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
             )
         )
@@ -3676,6 +3686,7 @@ async def image_generation(
         model_id = hidden_params.get("model_id", None) or ""
         cache_key = hidden_params.get("cache_key", None) or ""
         api_base = hidden_params.get("api_base", None) or ""
+        response_cost = hidden_params.get("response_cost", None) or ""
 
         fastapi_response.headers.update(
             get_custom_headers(
@@ -3684,6 +3695,7 @@ async def image_generation(
                 cache_key=cache_key,
                 api_base=api_base,
                 version=version,
+                response_cost=response_cost,
                 model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
             )
         )
@@ -3812,6 +3824,7 @@ async def audio_speech(
         model_id = hidden_params.get("model_id", None) or ""
         cache_key = hidden_params.get("cache_key", None) or ""
         api_base = hidden_params.get("api_base", None) or ""
+        response_cost = hidden_params.get("response_cost", None) or ""
 
         # Printing each chunk size
         async def generate(_response: HttpxBinaryResponseContent):
@@ -3825,6 +3838,7 @@ async def audio_speech(
             cache_key=cache_key,
             api_base=api_base,
             version=version,
+            response_cost=response_cost,
             model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
             fastest_response_batch_completion=None,
         )
@@ -3976,6 +3990,7 @@ async def audio_transcriptions(
         model_id = hidden_params.get("model_id", None) or ""
         cache_key = hidden_params.get("cache_key", None) or ""
         api_base = hidden_params.get("api_base", None) or ""
+        response_cost = hidden_params.get("response_cost", None) or ""
 
         fastapi_response.headers.update(
             get_custom_headers(
@@ -3984,6 +3999,7 @@ async def audio_transcriptions(
                 cache_key=cache_key,
                 api_base=api_base,
                 version=version,
+                response_cost=response_cost,
                 model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
             )
         )
diff --git a/litellm/tests/test_completion_cost.py b/litellm/tests/test_completion_cost.py
index 3a65f729423..bffb68e0e5d 100644
--- a/litellm/tests/test_completion_cost.py
+++ b/litellm/tests/test_completion_cost.py
@@ -712,9 +712,30 @@ def test_vertex_ai_claude_completion_cost():
     assert cost == predicted_cost
 
 
+
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_completion_cost_hidden_params(sync_mode):
+    if sync_mode:
+        response = litellm.completion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+            mock_response="Hello world",
+        )
+    else:
+        response = await litellm.acompletion(
+            model="gpt-3.5-turbo",
+            messages=[{"role": "user", "content": "Hey, how's it going?"}],
+            mock_response="Hello world",
+        )
+
+    assert "response_cost" in response._hidden_params
+    assert isinstance(response._hidden_params["response_cost"], float)
+
 def test_vertex_ai_gemini_predict_cost():
     model = "gemini-1.5-flash"
     messages = [{"role": "user", "content": "Hey, hows it going???"}]
     predictive_cost = completion_cost(model=model, messages=messages)
 
     assert predictive_cost > 0
+
diff --git a/litellm/utils.py b/litellm/utils.py
index dbc988bb978..53f5f984864 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -899,6 +899,17 @@ def client(original_function):
                     model=model,
                     optional_params=getattr(logging_obj, "optional_params", {}),
                 )
+                result._hidden_params["response_cost"] = (
+                    litellm.response_cost_calculator(
+                        response_object=result,
+                        model=getattr(logging_obj, "model", ""),
+                        custom_llm_provider=getattr(
+                            logging_obj, "custom_llm_provider", None
+                        ),
+                        call_type=getattr(logging_obj, "call_type", "completion"),
+                        optional_params=getattr(logging_obj, "optional_params", {}),
+                    )
+                )
             result._response_ms = (
                 end_time - start_time
             ).total_seconds() * 1000  # return response latency in ms like openai
@@ -1292,6 +1303,17 @@ def client(original_function):
                     model=model,
                     optional_params=kwargs,
                 )
+                result._hidden_params["response_cost"] = (
+                    litellm.response_cost_calculator(
+                        response_object=result,
+                        model=getattr(logging_obj, "model", ""),
+                        custom_llm_provider=getattr(
+                            logging_obj, "custom_llm_provider", None
+                        ),
+                        call_type=getattr(logging_obj, "call_type", "completion"),
+                        optional_params=getattr(logging_obj, "optional_params", {}),
+                    )
+                )
             if (
                 isinstance(result, ModelResponse)
                 or isinstance(result, EmbeddingResponse)