diff --git a/docs/modelserving/v1beta1/transformer/collocation/README.md b/docs/modelserving/v1beta1/transformer/collocation/README.md
new file mode 100644
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@@ -0,0 +1,116 @@
+# Collocate transformer and predictor in same pod
+
+KServe by default deploys the Transformer and Predictor as separate services, allowing you to deploy them on different devices and scale them independently.
+<br/>Nevertheless, there are certain situations where you might prefer to collocate the transformer and predictor within the same pod. Here are a few scenarios:
+
+1. If your transformer is tightly coupled with the predictor and you want to perform canary deployment together.
+2. If you want to reduce sidecar resources.
+3. If you want to reduce networking latency.
+
+## Before you begin 
+
+1. Your ~/.kube/config should point to a cluster with [KServe installed](../../../../get_started/README.md#install-the-kserve-quickstart-environment).
+2. Your cluster's Istio Ingress gateway must be [network accessible](https://istio.io/latest/docs/tasks/traffic-management/ingress/ingress-control/).
+3. You can find the [code samples](https://github.com/kserve/kserve/tree/master/docs/samples/v1beta1/transformer/collocation) on kserve repository.
+
+## Deploy the InferenceService
+
+Since, the predictor and the transformer are in the same pod, they need to listen on different ports to avoid conflict. `Transformer` is configured to listen on port 8000 and 8081 
+while, `Predictor` listens on port 8080 and 8082. `Transformer` calls `Predictor` on port 8082 via local socket. 
+Deploy the `Inferenceservice` using the below command.
+
+```bash
+cat <<EOF | kubectl apply -f -
+apiVersion: serving.kserve.io/v1beta1
+kind: InferenceService
+metadata:
+  name: custom-transformer-collocation
+spec:
+  predictor:
+    containers:
+      - name: kserve-container
+        image: kserve/custom-model-grpc:latest
+        args:
+          - --model_name=custom-model
+          - --grpc_port=8082
+          - --http_port=8080
+
+      - image: kserve/image-transformer:latest
+        name: transformer-container    # Do not change the container name
+        args:
+          - --model_name=custom-model
+          - --protocol=grpc-v2
+          - --http_port=8000
+          - --grpc_port=8081
+          - --predictor_host=localhost:8082
+        ports:
+          - containerPort: 8000
+            protocol: TCP
+EOF
+```
+!!! success "Expected output"
+    ```{ .bash .no-copy }
+    $ inferenceservice.serving.kserve.io/custom-transformer-collocation created
+    ```
+
+!!! Warning
+    Always use the transformer container name as `transformer-container`. Otherwise, the model volume is not mounted to the transformer 
+    container which may result in an error.
+
+!!! Note
+     Currently, The collocation support is limited to the custom container spec for kserve model container.
+
+## Check InferenceService status
+```bash
+kubectl get isvc custom-transformer-collocation
+```
+!!! success "Expected output"
+    ```{ .bash .no-copy }
+    NAME                             URL                                                         READY   PREV   LATEST   PREVROLLEDOUTREVISION   LATESTREADYREVISION                              AGE
+    custom-transformer-collocation   http://custom-transformer-collocation.default.example.com   True           100                              custom-transformer-collocation-predictor-00001   133m
+    ```
+
+!!! Note
+    If your DNS contains `svc.cluster.local`, then `Inferenceservice` is not exposed through Ingress. you need to [configure DNS](https://knative.dev/docs/install/yaml-install/serving/install-serving-with-yaml/#configure-dns) 
+    or [use a custom domain](https://knative.dev/docs/serving/using-a-custom-domain/) in order to expose the `isvc`.
+
+## Run a prediction
+Prepare the [inputs](https://github.com/kserve/kserve/blob/master/docs/samples/v1beta1/transformer/collocation/input.json) for the inference request. Copy the following Json into a file named `input.json`.
+
+Now, [determine the ingress IP and ports](../../../../get_started/first_isvc.md#4-determine-the-ingress-ip-and-ports
+) and set `INGRESS_HOST` and `INGRESS_PORT`
+
+```bash
+SERVICE_NAME=custom-transformer-collocation
+MODEL_NAME=custom-model
+INPUT_PATH=@./input.json
+SERVICE_HOSTNAME=$(kubectl get inferenceservice $SERVICE_NAME -o jsonpath='{.status.url}' | cut -d "/" -f 3)
+```
+You can use `curl` to send the inference request as:
+```bash
+curl -v -H "Host: ${SERVICE_HOSTNAME}" -H "Content-Type: application/json" -d $INPUT_PATH http://${INGRESS_HOST}:${INGRESS_PORT}/v2/models/$MODEL_NAME/infer
+```
+
+!!! success "Expected output"
+    ```{ .bash .no-copy }
+    *   Trying 127.0.0.1:8080...
+    * Connected to localhost (127.0.0.1) port 8080 (#0)
+    > POST /v2/models/custom-model/infer HTTP/1.1
+    > Host: custom-transformer-collocation.default.example.com
+    > User-Agent: curl/7.85.0
+    > Accept: */*
+    > Content-Type: application/json
+    > Content-Length: 105396
+    > 
+    * We are completely uploaded and fine
+    * Mark bundle as not supporting multiuse
+    < HTTP/1.1 200 OK
+    < content-length: 298
+    < content-type: application/json
+    < date: Thu, 04 May 2023 10:35:30 GMT
+    < server: istio-envoy
+    < x-envoy-upstream-service-time: 1273
+    < 
+    * Connection #0 to host localhost left intact
+    {"model_name":"custom-model","model_version":null,"id":"d685805f-a310-4690-9c71-a2dc38085d6f","parameters":null,"outputs":[{"name":"output-0","shape":[1,5],"datatype":"FP32","parameters":null,"data":[14.975618362426758,14.036808967590332,13.966032028198242,12.252279281616211,12.086268424987793]}]}
+    ```
diff --git a/mkdocs.yml b/mkdocs.yml
index 8eb6c71a0..51bb8bce0 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -47,6 +47,7 @@ nav:
           - Transformers:
                 - Feast: modelserving/v1beta1/transformer/feast/README.md
                 - How to write a custom transformer: modelserving/v1beta1/transformer/torchserve_image_transformer/README.md
+                - Collocate transformer and predictor: modelserving/v1beta1/transformer/collocation/README.md
           - Inference Graph:
                 - Concept: modelserving/inference_graph/README.md
                 - Image classification inference graph: modelserving/inference_graph/image_pipeline/README.md