diff --git a/docs/my-website/docs/routing.md b/docs/my-website/docs/routing.md
index 63fac9456..fd4fb8658 100644
--- a/docs/my-website/docs/routing.md
+++ b/docs/my-website/docs/routing.md
@@ -95,7 +95,7 @@ print(response)
 - `router.image_generation()` - completion calls in OpenAI `/v1/images/generations` endpoint format
 - `router.aimage_generation()` - async image generation calls
 
-## Advanced - Routing Strategies
+## Advanced - Routing Strategies ⭐️
 #### Routing Strategies - Weighted Pick, Rate Limit Aware, Least Busy, Latency Based, Cost Based
 
 Router provides 4 strategies for routing your calls across multiple deployments: 
@@ -262,7 +262,7 @@ if response is not None:
 	)
 ```
 
-### Set Time Window 
+#### Set Time Window 
 
 Set time window for how far back to consider when averaging latency for a deployment. 
 
@@ -278,7 +278,7 @@ router_settings:
 	routing_strategy_args: {"ttl": 10}
 ```
 
-### Set Lowest Latency Buffer
+#### Set Lowest Latency Buffer
 
 Set a buffer within which deployments are candidates for making calls to. 
 
@@ -468,6 +468,122 @@ asyncio.run(router_acompletion())
 ```
 
 </TabItem>
+
+<TabItem value="custom" label="Custom Routing Strategy">
+
+**Plugin a custom routing strategy to select deployments**
+
+
+Step 1. Define your custom routing strategy
+
+```python
+
+from litellm.router import CustomRoutingStrategyBase
+class CustomRoutingStrategy(CustomRoutingStrategyBase):
+    async def async_get_available_deployment(
+        self,
+        model: str,
+        messages: Optional[List[Dict[str, str]]] = None,
+        input: Optional[Union[str, List]] = None,
+        specific_deployment: Optional[bool] = False,
+        request_kwargs: Optional[Dict] = None,
+    ):
+        """
+        Asynchronously retrieves the available deployment based on the given parameters.
+
+        Args:
+            model (str): The name of the model.
+            messages (Optional[List[Dict[str, str]]], optional): The list of messages for a given request. Defaults to None.
+            input (Optional[Union[str, List]], optional): The input for a given embedding request. Defaults to None.
+            specific_deployment (Optional[bool], optional): Whether to retrieve a specific deployment. Defaults to False.
+            request_kwargs (Optional[Dict], optional): Additional request keyword arguments. Defaults to None.
+
+        Returns:
+            Returns an element from litellm.router.model_list
+
+        """
+        print("In CUSTOM async get available deployment")
+        model_list = router.model_list
+        print("router model list=", model_list)
+        for model in model_list:
+            if isinstance(model, dict):
+                if model["litellm_params"]["model"] == "openai/very-special-endpoint":
+                    return model
+        pass
+
+    def get_available_deployment(
+        self,
+        model: str,
+        messages: Optional[List[Dict[str, str]]] = None,
+        input: Optional[Union[str, List]] = None,
+        specific_deployment: Optional[bool] = False,
+        request_kwargs: Optional[Dict] = None,
+    ):
+        """
+        Synchronously retrieves the available deployment based on the given parameters.
+
+        Args:
+            model (str): The name of the model.
+            messages (Optional[List[Dict[str, str]]], optional): The list of messages for a given request. Defaults to None.
+            input (Optional[Union[str, List]], optional): The input for a given embedding request. Defaults to None.
+            specific_deployment (Optional[bool], optional): Whether to retrieve a specific deployment. Defaults to False.
+            request_kwargs (Optional[Dict], optional): Additional request keyword arguments. Defaults to None.
+
+        Returns:
+            Returns an element from litellm.router.model_list
+
+        """
+        pass
+```
+
+Step 2. Initialize Router with custom routing strategy
+```python
+from litellm import Router
+
+router = Router(
+    model_list=[
+        {
+            "model_name": "azure-model",
+            "litellm_params": {
+                "model": "openai/very-special-endpoint",
+                "api_base": "https://exampleopenaiendpoint-production.up.railway.app/",  # If you are Krrish, this is OpenAI Endpoint3 on our Railway endpoint :)
+                "api_key": "fake-key",
+            },
+            "model_info": {"id": "very-special-endpoint"},
+        },
+        {
+            "model_name": "azure-model",
+            "litellm_params": {
+                "model": "openai/fast-endpoint",
+                "api_base": "https://exampleopenaiendpoint-production.up.railway.app/",
+                "api_key": "fake-key",
+            },
+            "model_info": {"id": "fast-endpoint"},
+        },
+    ],
+    set_verbose=True,
+    debug_level="DEBUG",
+    timeout=1,
+)  # type: ignore
+
+router.set_custom_routing_strategy(CustomRoutingStrategy()) # 👈 Set your routing strategy here
+```
+
+Step 3. Test your routing strategy. Expect your custom routing strategy to be called when running `router.acompletion` requests
+```python
+for _ in range(10):
+	response = await router.acompletion(
+		model="azure-model", messages=[{"role": "user", "content": "hello"}]
+	)
+	print(response)
+	_picked_model_id = response._hidden_params["model_id"]
+	print("picked model=", _picked_model_id)
+```
+
+
+
+</TabItem>
+
 <TabItem value="lowest-cost" label="Lowest Cost Routing (Async)">
 
 Picks a deployment based on the lowest cost
@@ -563,7 +679,6 @@ asyncio.run(router_acompletion())
 ```
 
 </TabItem>
-
 </Tabs>
 
 ## Basic Reliability
diff --git a/litellm/router.py b/litellm/router.py
index 9200089d5..b4589c9f0 100644
--- a/litellm/router.py
+++ b/litellm/router.py
@@ -69,6 +69,7 @@ from litellm.types.router import (
     AlertingConfig,
     AllowedFailsPolicy,
     AssistantsTypedDict,
+    CustomRoutingStrategyBase,
     Deployment,
     DeploymentTypedDict,
     LiteLLM_Params,
@@ -4814,6 +4815,29 @@ class Router:
         except Exception as e:
             pass
 
+    def set_custom_routing_strategy(
+        self, CustomRoutingStrategy: CustomRoutingStrategyBase
+    ):
+        """
+        Sets get_available_deployment and async_get_available_deployment on an instanced of litellm.Router
+
+        Use this to set your custom routing strategy
+
+        Args:
+            CustomRoutingStrategy: litellm.router.CustomRoutingStrategyBase
+        """
+
+        setattr(
+            self,
+            "get_available_deployment",
+            CustomRoutingStrategy.get_available_deployment,
+        )
+        setattr(
+            self,
+            "async_get_available_deployment",
+            CustomRoutingStrategy.async_get_available_deployment,
+        )
+
     def flush_cache(self):
         litellm.cache = None
         self.cache.flush_cache()
diff --git a/litellm/tests/test_router_custom_routing.py b/litellm/tests/test_router_custom_routing.py
new file mode 100644
index 000000000..afd602b93
--- /dev/null
+++ b/litellm/tests/test_router_custom_routing.py
@@ -0,0 +1,150 @@
+import asyncio
+import os
+import random
+import sys
+import time
+import traceback
+from datetime import datetime, timedelta
+
+from dotenv import load_dotenv
+
+load_dotenv()
+import copy
+import os
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+from typing import Dict, List, Optional, Union
+
+import pytest
+
+import litellm
+from litellm import Router
+
+router = Router(
+    model_list=[
+        {
+            "model_name": "azure-model",
+            "litellm_params": {
+                "model": "openai/very-special-endpoint",
+                "api_base": "https://exampleopenaiendpoint-production.up.railway.app/",  # If you are Krrish, this is OpenAI Endpoint3 on our Railway endpoint :)
+                "api_key": "fake-key",
+            },
+            "model_info": {"id": "very-special-endpoint"},
+        },
+        {
+            "model_name": "azure-model",
+            "litellm_params": {
+                "model": "openai/fast-endpoint",
+                "api_base": "https://exampleopenaiendpoint-production.up.railway.app/",
+                "api_key": "fake-key",
+            },
+            "model_info": {"id": "fast-endpoint"},
+        },
+    ],
+    set_verbose=True,
+    debug_level="DEBUG",
+)
+
+from litellm.router import CustomRoutingStrategyBase
+
+
+class CustomRoutingStrategy(CustomRoutingStrategyBase):
+    async def async_get_available_deployment(
+        self,
+        model: str,
+        messages: Optional[List[Dict[str, str]]] = None,
+        input: Optional[Union[str, List]] = None,
+        specific_deployment: Optional[bool] = False,
+        request_kwargs: Optional[Dict] = None,
+    ):
+        """
+        Asynchronously retrieves the available deployment based on the given parameters.
+
+        Args:
+            model (str): The name of the model.
+            messages (Optional[List[Dict[str, str]]], optional): The list of messages for a given request. Defaults to None.
+            input (Optional[Union[str, List]], optional): The input for a given embedding request. Defaults to None.
+            specific_deployment (Optional[bool], optional): Whether to retrieve a specific deployment. Defaults to False.
+            request_kwargs (Optional[Dict], optional): Additional request keyword arguments. Defaults to None.
+
+        Returns:
+            Returns an element from litellm.router.model_list
+
+        """
+        print("In CUSTOM async get available deployment")
+        model_list = router.model_list
+        print("router model list=", model_list)
+        for model in model_list:
+            if isinstance(model, dict):
+                if model["litellm_params"]["model"] == "openai/very-special-endpoint":
+                    return model
+        pass
+
+    def get_available_deployment(
+        self,
+        model: str,
+        messages: Optional[List[Dict[str, str]]] = None,
+        input: Optional[Union[str, List]] = None,
+        specific_deployment: Optional[bool] = False,
+        request_kwargs: Optional[Dict] = None,
+    ):
+        """
+        Synchronously retrieves the available deployment based on the given parameters.
+
+        Args:
+            model (str): The name of the model.
+            messages (Optional[List[Dict[str, str]]], optional): The list of messages for a given request. Defaults to None.
+            input (Optional[Union[str, List]], optional): The input for a given embedding request. Defaults to None.
+            specific_deployment (Optional[bool], optional): Whether to retrieve a specific deployment. Defaults to False.
+            request_kwargs (Optional[Dict], optional): Additional request keyword arguments. Defaults to None.
+
+        Returns:
+            Returns an element from litellm.router.model_list
+
+        """
+        pass
+
+
+@pytest.mark.asyncio
+async def test_custom_routing():
+    import litellm
+
+    litellm.set_verbose = True
+    router.set_custom_routing_strategy(CustomRoutingStrategy())
+
+    # make 4 requests
+    for _ in range(4):
+        try:
+            response = await router.acompletion(
+                model="azure-model", messages=[{"role": "user", "content": "hello"}]
+            )
+            print(response)
+        except Exception as e:
+            print("got exception", e)
+
+    await asyncio.sleep(1)
+    print("done sending initial requests to collect latency")
+    """
+    Note: for debugging
+    - By this point: slow-endpoint should have timed out 3-4 times and should be heavily penalized :)
+    - The next 10 requests should all be routed to the fast-endpoint
+    """
+
+    deployments = {}
+    # make 10 requests
+    for _ in range(10):
+        response = await router.acompletion(
+            model="azure-model", messages=[{"role": "user", "content": "hello"}]
+        )
+        print(response)
+        _picked_model_id = response._hidden_params["model_id"]
+        if _picked_model_id not in deployments:
+            deployments[_picked_model_id] = 1
+        else:
+            deployments[_picked_model_id] += 1
+    print("deployments", deployments)
+
+    # ALL the Requests should have been routed to the fast-endpoint
+    # assert deployments["fast-endpoint"] == 10
diff --git a/litellm/types/router.py b/litellm/types/router.py
index da3c999dc..206216ef0 100644
--- a/litellm/types/router.py
+++ b/litellm/types/router.py
@@ -451,3 +451,53 @@ class ModelGroupInfo(BaseModel):
 class AssistantsTypedDict(TypedDict):
     custom_llm_provider: Literal["azure", "openai"]
     litellm_params: LiteLLMParamsTypedDict
+
+
+class CustomRoutingStrategyBase:
+    async def async_get_available_deployment(
+        self,
+        model: str,
+        messages: Optional[List[Dict[str, str]]] = None,
+        input: Optional[Union[str, List]] = None,
+        specific_deployment: Optional[bool] = False,
+        request_kwargs: Optional[Dict] = None,
+    ):
+        """
+        Asynchronously retrieves the available deployment based on the given parameters.
+
+        Args:
+            model (str): The name of the model.
+            messages (Optional[List[Dict[str, str]]], optional): The list of messages for a given request. Defaults to None.
+            input (Optional[Union[str, List]], optional): The input for a given embedding request. Defaults to None.
+            specific_deployment (Optional[bool], optional): Whether to retrieve a specific deployment. Defaults to False.
+            request_kwargs (Optional[Dict], optional): Additional request keyword arguments. Defaults to None.
+
+        Returns:
+            Returns an element from litellm.router.model_list
+
+        """
+        pass
+
+    def get_available_deployment(
+        self,
+        model: str,
+        messages: Optional[List[Dict[str, str]]] = None,
+        input: Optional[Union[str, List]] = None,
+        specific_deployment: Optional[bool] = False,
+        request_kwargs: Optional[Dict] = None,
+    ):
+        """
+        Synchronously retrieves the available deployment based on the given parameters.
+
+        Args:
+            model (str): The name of the model.
+            messages (Optional[List[Dict[str, str]]], optional): The list of messages for a given request. Defaults to None.
+            input (Optional[Union[str, List]], optional): The input for a given embedding request. Defaults to None.
+            specific_deployment (Optional[bool], optional): Whether to retrieve a specific deployment. Defaults to False.
+            request_kwargs (Optional[Dict], optional): Additional request keyword arguments. Defaults to None.
+
+        Returns:
+            Returns an element from litellm.router.model_list
+
+        """
+        pass