diff --git a/litellm/router.py b/litellm/router.py
index d195c67b1..512a47a34 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -18,6 +18,7 @@ import inspect, concurrent
 from openai import AsyncOpenAI
 from collections import defaultdict
 from litellm.router_strategy.least_busy import LeastBusyLoggingHandler
+from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler
 from litellm.llms.custom_httpx.azure_dall_e_2 import (
     CustomHTTPTransport,
     AsyncCustomHTTPTransport,
@@ -67,6 +68,7 @@ class Router:
     num_retries: int = 0
     tenacity = None
     leastbusy_logger: Optional[LeastBusyLoggingHandler] = None
+    lowesttpm_logger: Optional[LowestTPMLoggingHandler] = None
 
     def __init__(
         self,
@@ -196,12 +198,14 @@ class Router:
                 litellm.input_callback = [self.leastbusy_logger]  # type: ignore
             if isinstance(litellm.callbacks, list):
                 litellm.callbacks.append(self.leastbusy_logger)  # type: ignore
-        ## USAGE TRACKING ##
-        if isinstance(litellm.success_callback, list):
-            litellm.success_callback.append(self.deployment_callback)
-        else:
-            litellm.success_callback = [self.deployment_callback]
+        elif routing_strategy == "usage-based-routing":
+            self.lowesttpm_logger = LowestTPMLoggingHandler(
+                router_cache=self.cache, model_list=self.model_list
+            )
+            if isinstance(litellm.callbacks, list):
+                litellm.callbacks.append(self.lowesttpm_logger)  # type: ignore
 
+        ## COOLDOWNS ##
         if isinstance(litellm.failure_callback, list):
             litellm.failure_callback.append(self.deployment_callback_on_failure)
         else:
@@ -1012,40 +1016,6 @@ class Router:
 
     ### HELPER FUNCTIONS
 
-    def deployment_callback(
-        self,
-        kwargs,  # kwargs to completion
-        completion_response,  # response from completion
-        start_time,
-        end_time,  # start/end time
-    ):
-        """
-        Function LiteLLM submits a callback to after a successful
-        completion. Purpose of this is to update TPM/RPM usage per model
-        """
-        deployment_id = (
-            kwargs.get("litellm_params", {}).get("model_info", {}).get("id", None)
-        )
-        model_name = kwargs.get("model", None)  # i.e. gpt35turbo
-        custom_llm_provider = kwargs.get("litellm_params", {}).get(
-            "custom_llm_provider", None
-        )  # i.e. azure
-        if custom_llm_provider:
-            model_name = f"{custom_llm_provider}/{model_name}"
-        if kwargs["stream"] is True:
-            if kwargs.get("complete_streaming_response"):
-                total_tokens = kwargs.get("complete_streaming_response")["usage"][
-                    "total_tokens"
-                ]
-                self._set_deployment_usage(deployment_id, total_tokens)
-        else:
-            total_tokens = completion_response["usage"]["total_tokens"]
-            self._set_deployment_usage(deployment_id, total_tokens)
-
-        self.deployment_latency_map[model_name] = (
-            end_time - start_time
-        ).total_seconds()
-
     def deployment_callback_on_failure(
         self,
         kwargs,  # kwargs to completion
@@ -1180,109 +1150,6 @@ class Router:
         self.print_verbose(f"retrieve cooldown models: {cooldown_models}")
         return cooldown_models
 
-    def get_usage_based_available_deployment(
-        self,
-        model: str,
-        messages: Optional[List[Dict[str, str]]] = None,
-        input: Optional[Union[str, List]] = None,
-    ):
-        """
-        Returns a deployment with the lowest TPM/RPM usage.
-        """
-        # get list of potential deployments
-        potential_deployments = []
-        for item in self.model_list:
-            if item["model_name"] == model:
-                potential_deployments.append(item)
-
-        # get current call usage
-        token_count = 0
-        if messages is not None:
-            token_count = litellm.token_counter(model=model, messages=messages)
-        elif input is not None:
-            if isinstance(input, List):
-                input_text = "".join(text for text in input)
-            else:
-                input_text = input
-            token_count = litellm.token_counter(model=model, text=input_text)
-
-        # -----------------------
-        # Find lowest used model
-        # ----------------------
-        lowest_tpm = float("inf")
-        deployment = None
-
-        # load model context map
-        models_context_map = litellm.model_cost
-
-        # return deployment with lowest tpm usage
-        for item in potential_deployments:
-            model_id = item["model_info"].get("id")
-            item_tpm, item_rpm = self._get_deployment_usage(deployment_name=model_id)
-
-            if item_tpm == 0:
-                return item
-            elif (
-                "tpm" in item
-                and item_tpm + token_count > item["tpm"]
-                or "rpm" in item
-                and item_rpm + 1 >= item["rpm"]
-            ):  # if user passed in tpm / rpm in the model_list
-                continue
-            elif item_tpm < lowest_tpm:
-                lowest_tpm = item_tpm
-                deployment = item
-
-        # if none, raise exception
-        if deployment is None:
-            raise ValueError("No models available.")
-
-        # return model
-        return deployment
-
-    def _get_deployment_usage(self, deployment_name: str):
-        # ------------
-        # Setup values
-        # ------------
-        current_minute = datetime.now().strftime("%H-%M")
-        tpm_key = f"{deployment_name}:tpm:{current_minute}"
-        rpm_key = f"{deployment_name}:rpm:{current_minute}"
-
-        # ------------
-        # Return usage
-        # ------------
-        tpm = self.cache.get_cache(key=tpm_key) or 0
-        rpm = self.cache.get_cache(key=rpm_key) or 0
-
-        return int(tpm), int(rpm)
-
-    def increment(self, key: str, increment_value: int):
-        # get value
-        cached_value = self.cache.get_cache(key=key)
-        # update value
-        try:
-            cached_value = cached_value + increment_value
-        except:
-            cached_value = increment_value
-        # save updated value
-        self.cache.set_cache(
-            value=cached_value, key=key, ttl=self.default_cache_time_seconds
-        )
-
-    def _set_deployment_usage(self, model_name: str, total_tokens: int):
-        # ------------
-        # Setup values
-        # ------------
-        current_minute = datetime.now().strftime("%H-%M")
-        tpm_key = f"{model_name}:tpm:{current_minute}"
-        rpm_key = f"{model_name}:rpm:{current_minute}"
-
-        # ------------
-        # Update usage
-        # ------------
-        self.increment(tpm_key, total_tokens)
-        self.increment(rpm_key, 1)
-
     def _start_health_check_thread(self):
         """
         Starts a separate thread to perform health checks periodically.
@@ -1733,10 +1600,11 @@ class Router:
                 )
             returned_item = self.weighted_shuffle_by_latency(items_with_latencies)
             return returned_item
-        elif self.routing_strategy == "usage-based-routing":
-            return self.get_usage_based_available_deployment(
-                model=model, messages=messages, input=input
-            )
+        elif (
+            self.routing_strategy == "usage-based-routing"
+            and self.lowesttpm_logger is not None
+        ):
+            return self.lowesttpm_logger.get_available_deployments(model_group=model)
 
         raise ValueError("No models available.")
 
diff --git a/litellm/router_strategy/lowest_tpm_rpm.py b/litellm/router_strategy/lowest_tpm_rpm.py
new file mode 100644
index 000000000..2e53aae88
--- /dev/null
+++ b/litellm/router_strategy/lowest_tpm_rpm.py
@@ -0,0 +1,169 @@
+#### What this does ####
+#   identifies lowest tpm deployment
+
+import dotenv, os, requests
+from typing import Optional
+from datetime import datetime
+
+dotenv.load_dotenv()  # Loading env variables using dotenv
+import traceback
+from litellm.caching import DualCache
+from litellm.integrations.custom_logger import CustomLogger
+
+
+class LowestTPMLoggingHandler(CustomLogger):
+    test_flag: bool = False
+    logged_success: int = 0
+    logged_failure: int = 0
+    default_cache_time_seconds: int = 1 * 60 * 60  # 1 hour
+
+    def __init__(self, router_cache: DualCache, model_list: list):
+        self.router_cache = router_cache
+        self.model_list = model_list
+
+    def log_success_event(self, kwargs, response_obj, start_time, end_time):
+        try:
+            """
+            Update TPM/RPM usage on success
+            """
+            if kwargs["litellm_params"].get("metadata") is None:
+                pass
+            else:
+                model_group = kwargs["litellm_params"]["metadata"].get(
+                    "model_group", None
+                )
+
+                id = kwargs["litellm_params"].get("model_info", {}).get("id", None)
+                if model_group is None or id is None:
+                    return
+
+                total_tokens = response_obj["usage"]["total_tokens"]
+
+                # ------------
+                # Setup values
+                # ------------
+                current_minute = datetime.now().strftime("%H-%M")
+                tpm_key = f"{model_group}:tpm:{current_minute}"
+                rpm_key = f"{model_group}:rpm:{current_minute}"
+
+                # ------------
+                # Update usage
+                # ------------
+
+                ## TPM
+                request_count_dict = self.router_cache.get_cache(key=tpm_key) or {}
+                request_count_dict[id] = request_count_dict.get(id, 0) + total_tokens
+
+                self.router_cache.set_cache(key=tpm_key, value=request_count_dict)
+
+                ## RPM
+                request_count_dict = self.router_cache.get_cache(key=rpm_key) or {}
+                request_count_dict[id] = request_count_dict.get(id, 0) + 1
+
+                self.router_cache.set_cache(key=rpm_key, value=request_count_dict)
+
+                ### TESTING ###
+                if self.test_flag:
+                    self.logged_success += 1
+        except Exception as e:
+            pass
+
+    async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
+        try:
+            """
+            Update TPM/RPM usage on success
+            """
+            if kwargs["litellm_params"].get("metadata") is None:
+                pass
+            else:
+                model_group = kwargs["litellm_params"]["metadata"].get(
+                    "model_group", None
+                )
+
+                id = kwargs["litellm_params"].get("model_info", {}).get("id", None)
+                if model_group is None or id is None:
+                    return
+
+                total_tokens = response_obj["usage"]["total_tokens"]
+
+                # ------------
+                # Setup values
+                # ------------
+                current_minute = datetime.now().strftime("%H-%M")
+                tpm_key = f"{model_group}:tpm:{current_minute}"
+                rpm_key = f"{model_group}:rpm:{current_minute}"
+
+                # ------------
+                # Update usage
+                # ------------
+                # update cache
+
+                ## TPM
+                request_count_dict = self.router_cache.get_cache(key=tpm_key) or {}
+                request_count_dict[id] = request_count_dict.get(id, 0) + total_tokens
+
+                self.router_cache.set_cache(key=tpm_key, value=request_count_dict)
+
+                ## RPM
+                request_count_dict = self.router_cache.get_cache(key=rpm_key) or {}
+                request_count_dict[id] = request_count_dict.get(id, 0) + 1
+
+                self.router_cache.set_cache(key=rpm_key, value=request_count_dict)
+
+                ### TESTING ###
+                if self.test_flag:
+                    self.logged_success += 1
+        except Exception as e:
+            pass
+
+    def get_available_deployments(self, model_group: str):
+        """
+        Returns a deployment with the lowest TPM/RPM usage.
+        """
+        # get list of potential deployments
+        current_minute = datetime.now().strftime("%H-%M")
+        tpm_key = f"{model_group}:tpm:{current_minute}"
+        rpm_key = f"{model_group}:rpm:{current_minute}"
+
+        tpm_dict = self.router_cache.get_cache(key=tpm_key)
+        rpm_dict = self.router_cache.get_cache(key=rpm_key)
+
+        # -----------------------
+        # Find lowest used model
+        # ----------------------
+        lowest_tpm = float("inf")
+        deployment = None
+
+        for item, item_tpm in tpm_dict.items():
+            ## get the item from model list
+            _deployment = None
+            for m in self.model_list:
+                if item == m["model_info"]["id"]:
+                    _deployment = m
+
+            if _deployment is None:
+                break
+            _deployment_tpm = (
+                _deployment.get("tpm", None)
+                or _deployment.get("litellm_params", {}).get("tpm", None)
+                or _deployment.get("model_info", {}).get("tpm", None)
+                or float("inf")
+            )
+
+            _deployment_rpm = (
+                _deployment.get("rpm", None)
+                or _deployment.get("litellm_params", {}).get("rpm", None)
+                or _deployment.get("model_info", {}).get("rpm", None)
+                or float("inf")
+            )
+
+            if item_tpm == 0:
+                return item
+            elif (
+                item_tpm > _deployment_tpm or rpm_dict[item] + 1 >= _deployment_rpm
+            ):  # if user passed in tpm / rpm in the model_list
+                continue
+            elif item_tpm < lowest_tpm:
+                lowest_tpm = item_tpm
+                deployment = _deployment
+        return deployment
diff --git a/litellm/tests/test_least_busy_routing.py b/litellm/tests/test_least_busy_routing.py
index bd0855ed7..0bc125fe5 100644
--- a/litellm/tests/test_least_busy_routing.py
+++ b/litellm/tests/test_least_busy_routing.py
@@ -1,13 +1,6 @@
 #### What this tests ####
 #    This tests the router's ability to identify the least busy deployment
 
-#
-# How is this achieved?
-# - Before each call, have the router print the state of requests {"deployment": "requests_in_flight"}
-# - use litellm.input_callbacks to log when a request is just about to be made to a model - {"deployment-id": traffic}
-# - use litellm.success + failure callbacks to log when a request completed
-# - in get_available_deployment, for a given model group name -> pick based on traffic
-
 import sys, os, asyncio, time
 import traceback
 from dotenv import load_dotenv
@@ -137,4 +130,4 @@ def test_router_get_available_deployments():
     assert return_dict[3] == 100
 
 
-test_router_get_available_deployments()
+# test_router_get_available_deployments()
diff --git a/litellm/tests/test_tpm_rpm_routing.py b/litellm/tests/test_tpm_rpm_routing.py
new file mode 100644
index 000000000..681ffc04f
--- /dev/null
+++ b/litellm/tests/test_tpm_rpm_routing.py
@@ -0,0 +1,226 @@
+#### What this tests ####
+#    This tests the router's ability to pick deployment with lowest tpm
+
+import sys, os, asyncio, time
+from datetime import datetime
+import traceback
+from dotenv import load_dotenv
+
+load_dotenv()
+import os
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+import pytest
+from litellm import Router
+import litellm
+from litellm.router_strategy.lowest_tpm_rpm import LowestTPMLoggingHandler
+from litellm.caching import DualCache
+
+### UNIT TESTS FOR TPM/RPM ROUTING ###
+
+
+def test_tpm_rpm_updated():
+    test_cache = DualCache()
+    model_list = []
+    lowest_tpm_logger = LowestTPMLoggingHandler(
+        router_cache=test_cache, model_list=model_list
+    )
+    model_group = "gpt-3.5-turbo"
+    deployment_id = "1234"
+    kwargs = {
+        "litellm_params": {
+            "metadata": {
+                "model_group": "gpt-3.5-turbo",
+                "deployment": "azure/chatgpt-v-2",
+            },
+            "model_info": {"id": deployment_id},
+        }
+    }
+    start_time = time.time()
+    response_obj = {"usage": {"total_tokens": 50}}
+    end_time = time.time()
+    lowest_tpm_logger.log_success_event(
+        response_obj=response_obj,
+        kwargs=kwargs,
+        start_time=start_time,
+        end_time=end_time,
+    )
+    current_minute = datetime.now().strftime("%H-%M")
+    tpm_count_api_key = f"{model_group}:tpm:{current_minute}"
+    rpm_count_api_key = f"{model_group}:rpm:{current_minute}"
+    assert (
+        response_obj["usage"]["total_tokens"]
+        == test_cache.get_cache(key=tpm_count_api_key)[deployment_id]
+    )
+    assert 1 == test_cache.get_cache(key=rpm_count_api_key)[deployment_id]
+
+
+# test_tpm_rpm_updated()
+
+
+def test_get_available_deployments():
+    test_cache = DualCache()
+    model_list = [
+        {
+            "model_name": "gpt-3.5-turbo",
+            "litellm_params": {"model": "azure/chatgpt-v-2"},
+            "model_info": {"id": "1234"},
+        },
+        {
+            "model_name": "gpt-3.5-turbo",
+            "litellm_params": {"model": "azure/chatgpt-v-2"},
+            "model_info": {"id": "5678"},
+        },
+    ]
+    lowest_tpm_logger = LowestTPMLoggingHandler(
+        router_cache=test_cache, model_list=model_list
+    )
+    model_group = "gpt-3.5-turbo"
+    ## DEPLOYMENT 1 ##
+    deployment_id = "1234"
+    kwargs = {
+        "litellm_params": {
+            "metadata": {
+                "model_group": "gpt-3.5-turbo",
+                "deployment": "azure/chatgpt-v-2",
+            },
+            "model_info": {"id": deployment_id},
+        }
+    }
+    start_time = time.time()
+    response_obj = {"usage": {"total_tokens": 50}}
+    end_time = time.time()
+    lowest_tpm_logger.log_success_event(
+        response_obj=response_obj,
+        kwargs=kwargs,
+        start_time=start_time,
+        end_time=end_time,
+    )
+    ## DEPLOYMENT 2 ##
+    deployment_id = "5678"
+    kwargs = {
+        "litellm_params": {
+            "metadata": {
+                "model_group": "gpt-3.5-turbo",
+                "deployment": "azure/chatgpt-v-2",
+            },
+            "model_info": {"id": deployment_id},
+        }
+    }
+    start_time = time.time()
+    response_obj = {"usage": {"total_tokens": 20}}
+    end_time = time.time()
+    lowest_tpm_logger.log_success_event(
+        response_obj=response_obj,
+        kwargs=kwargs,
+        start_time=start_time,
+        end_time=end_time,
+    )
+
+    ## CHECK WHAT'S SELECTED ##
+    print(lowest_tpm_logger.get_available_deployments(model_group=model_group))
+    assert (
+        lowest_tpm_logger.get_available_deployments(model_group=model_group)[
+            "model_info"
+        ]["id"]
+        == "5678"
+    )
+
+
+# test_get_available_deployments()
+
+
+def test_router_get_available_deployments():
+    """
+    Test if routers 'get_available_deployments' returns the least busy deployment
+    """
+    model_list = [
+        {
+            "model_name": "azure-model",
+            "litellm_params": {
+                "model": "azure/gpt-turbo",
+                "api_key": "os.environ/AZURE_FRANCE_API_KEY",
+                "api_base": "https://openai-france-1234.openai.azure.com",
+                "rpm": 1440,
+            },
+            "model_info": {"id": 1},
+        },
+        {
+            "model_name": "azure-model",
+            "litellm_params": {
+                "model": "azure/gpt-35-turbo",
+                "api_key": "os.environ/AZURE_EUROPE_API_KEY",
+                "api_base": "https://my-endpoint-europe-berri-992.openai.azure.com",
+                "rpm": 6,
+            },
+            "model_info": {"id": 2},
+        },
+        {
+            "model_name": "azure-model",
+            "litellm_params": {
+                "model": "azure/gpt-35-turbo",
+                "api_key": "os.environ/AZURE_CANADA_API_KEY",
+                "api_base": "https://my-endpoint-canada-berri992.openai.azure.com",
+                "rpm": 6,
+            },
+            "model_info": {"id": 3},
+        },
+    ]
+    router = Router(
+        model_list=model_list,
+        routing_strategy="usage-based-routing",
+        set_verbose=False,
+        num_retries=3,
+    )  # type: ignore
+
+    ## DEPLOYMENT 1 ##
+    deployment_id = 1
+    kwargs = {
+        "litellm_params": {
+            "metadata": {
+                "model_group": "azure-model",
+            },
+            "model_info": {"id": 1},
+        }
+    }
+    start_time = time.time()
+    response_obj = {"usage": {"total_tokens": 50}}
+    end_time = time.time()
+    router.lowesttpm_logger.log_success_event(
+        response_obj=response_obj,
+        kwargs=kwargs,
+        start_time=start_time,
+        end_time=end_time,
+    )
+    ## DEPLOYMENT 2 ##
+    deployment_id = 2
+    kwargs = {
+        "litellm_params": {
+            "metadata": {
+                "model_group": "azure-model",
+            },
+            "model_info": {"id": 2},
+        }
+    }
+    start_time = time.time()
+    response_obj = {"usage": {"total_tokens": 20}}
+    end_time = time.time()
+    router.lowesttpm_logger.log_success_event(
+        response_obj=response_obj,
+        kwargs=kwargs,
+        start_time=start_time,
+        end_time=end_time,
+    )
+
+    ## CHECK WHAT'S SELECTED ##
+    # print(router.lowesttpm_logger.get_available_deployments(model_group="azure-model"))
+    print(router.get_available_deployment(model="azure-model"))
+    assert router.get_available_deployment(model="azure-model")["model_info"]["id"] == 2
+
+
+# test_get_available_deployments()
+
+
+# test_router_get_available_deployments()