diff --git a/docs/getting-started/first-abn.md b/docs/getting-started/first-abn.md
index 72e88f75..004f2607 100644
--- a/docs/getting-started/first-abn.md
+++ b/docs/getting-started/first-abn.md
@@ -11,7 +11,7 @@ This tutorial describes how to do A/B testing of a backend component using the [
 ***
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     2. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -111,7 +111,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000)
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `default/backend` with the following parameters:
 
diff --git a/docs/getting-started/first-performance.md b/docs/getting-started/first-performance.md
index 5f31235d..a0285984 100644
--- a/docs/getting-started/first-performance.md
+++ b/docs/getting-started/first-performance.md
@@ -9,7 +9,7 @@ Run your first performance test by load testing a Kubernetes HTTP service and vi
 ![Load test HTTP](images/kubernetesusage.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     2. Deploy the sample HTTP service in the Kubernetes cluster.
     ```shell
     kubectl create deploy httpbin --image=kennethreitz/httpbin --port=80
@@ -66,7 +66,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana by going to [http://localhost:3000](http://localhost:3000).
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `httpbin-test` with the following parameters:
 
diff --git a/docs/getting-started/first-release.md b/docs/getting-started/first-release.md
index acd90273..60bff2d0 100644
--- a/docs/getting-started/first-release.md
+++ b/docs/getting-started/first-release.md
@@ -14,7 +14,7 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Blue-green rollout](../tutorials/images/blue-green.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     2. Install [Istio](https://istio.io). It suffices to install the [demo profile](https://istio.io/latest/docs/setup/getting-started/), for example by using: 
     ```shell
     istioctl install --set profile=demo -y
diff --git a/docs/tutorials/integrations/kserve-mm/abn.md b/docs/tutorials/integrations/kserve-mm/abn.md
index 068ca7e9..3fc405f2 100644
--- a/docs/tutorials/integrations/kserve-mm/abn.md
+++ b/docs/tutorials/integrations/kserve-mm/abn.md
@@ -11,11 +11,8 @@ This tutorial describes how to do A/B testing as part of the release of a backen
 ***
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespace to `modelmesh-serving`: 
-    ```shell
-    kubectl config set-context --current --namespace=modelmesh-serving
-    ```
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, your default namespace will be changed to `modelmesh-serving`. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -131,9 +128,9 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000)
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
-[Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `default/backend` with the following parameters:
+[Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `modelmesh-serving/backend` with the following parameters:
 
 * URL: `http://iter8.modelmesh-serving:8080/abnDashboard`
 * Query string: `namespace=modelmesh-serving&application=backend`
diff --git a/docs/tutorials/integrations/kserve-mm/blue-green.md b/docs/tutorials/integrations/kserve-mm/blue-green.md
index 6e62c364..0aff3065 100644
--- a/docs/tutorials/integrations/kserve-mm/blue-green.md
+++ b/docs/tutorials/integrations/kserve-mm/blue-green.md
@@ -14,11 +14,8 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Blue-green rollout](../../images/blue-green.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespace to `modelmesh-serving`: 
-    ```shell
-    kubectl config set-context --current --namespace=modelmesh-serving
-    ```
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, your default namespace will be changed to `modelmesh-serving`. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Install [Istio](https://istio.io). It suffices to install the [demo profile](https://istio.io/latest/docs/setup/getting-started/), for example by using: 
     ```shell
     istioctl install --set profile=demo -y
diff --git a/docs/tutorials/integrations/kserve-mm/canary.md b/docs/tutorials/integrations/kserve-mm/canary.md
index 6f6d0b7b..5b8b2fdd 100644
--- a/docs/tutorials/integrations/kserve-mm/canary.md
+++ b/docs/tutorials/integrations/kserve-mm/canary.md
@@ -14,11 +14,8 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Canary rollout](../../images/canary.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
-    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, change your default namespace to `modelmesh-serving`: 
-    ```shell
-    kubectl config set-context --current --namespace=modelmesh-serving
-    ```
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe ModelMesh Serving](https://github.com/kserve/modelmesh-serving). For example, you can create a modelmesh-serving [Quickstart](https://github.com/kserve/modelmesh-serving/blob/release-0.11/docs/quickstart.md) environment.  If using the Quickstart environment, your default namespace will be changed to `modelmesh-serving`. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Install [Istio](https://istio.io). It suffices to install the [demo profile](https://istio.io/latest/docs/setup/getting-started/), for example by using: 
     ```shell
     istioctl install --set profile=demo -y
diff --git a/docs/tutorials/integrations/kserve/abn-grpc.md b/docs/tutorials/integrations/kserve/abn-grpc.md
index 88dff82b..4e24f59f 100644
--- a/docs/tutorials/integrations/kserve/abn-grpc.md
+++ b/docs/tutorials/integrations/kserve/abn-grpc.md
@@ -11,11 +11,12 @@ This tutorial describes how to do A/B testing as part of the release of a backen
 ***
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
     2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -139,7 +140,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000)
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `default/backend` with the following parameters:
 
diff --git a/docs/tutorials/integrations/kserve/abn-http.md b/docs/tutorials/integrations/kserve/abn-http.md
index b8870fed..4aa65549 100644
--- a/docs/tutorials/integrations/kserve/abn-http.md
+++ b/docs/tutorials/integrations/kserve/abn-http.md
@@ -11,11 +11,12 @@ This tutorial describes how to do A/B testing as part of the release of a backen
 ***
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
     2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     3. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -131,7 +132,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000)
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `default/backend` with the following parameters:
 
diff --git a/docs/tutorials/integrations/kserve/blue-green.md b/docs/tutorials/integrations/kserve/blue-green.md
index c63fc5da..467614fe 100644
--- a/docs/tutorials/integrations/kserve/blue-green.md
+++ b/docs/tutorials/integrations/kserve/blue-green.md
@@ -14,11 +14,12 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Blue-green rollout](../../images/blue-green.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
     2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
 <!-- Istio is installed as part of kserve install -->
 
 ## Install the Iter8 controller
diff --git a/docs/tutorials/integrations/kserve/canary.md b/docs/tutorials/integrations/kserve/canary.md
index 23f3cf89..919c19cd 100644
--- a/docs/tutorials/integrations/kserve/canary.md
+++ b/docs/tutorials/integrations/kserve/canary.md
@@ -14,11 +14,12 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 ![Canary rollout](../../images/canary.png)
 
 ???+ warning "Before you begin"
-    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
     2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
 <!-- Istio is installed as part of kserve install -->
 
 ## Install the Iter8 controller
diff --git a/docs/tutorials/integrations/kserve/grpc.md b/docs/tutorials/integrations/kserve/grpc.md
index 6b6b8ea4..23903ba1 100644
--- a/docs/tutorials/integrations/kserve/grpc.md
+++ b/docs/tutorials/integrations/kserve/grpc.md
@@ -7,12 +7,12 @@ template: main.html
 This tutorial shows how easy it is to run a load test for KServe when using gRPC to make requests. We use a sklearn model to demonstrate. The same approach works for any model type. 
 
 ???+ warning "Before you begin"
-    1. Try [Your first performance test](../../../getting-started/first-performance.md). Understand the main [concepts](../../../getting-started/concepts.md) behind Iter8.
-    2. Ensure that you have the [kubectl](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
-    3. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     4. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -81,7 +81,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana by going to [http://localhost:3000](http://localhost:3000).
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`. 
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `model-test` with the following parameters:
 
diff --git a/docs/tutorials/integrations/kserve/http.md b/docs/tutorials/integrations/kserve/http.md
index 744813f7..b52f0af2 100644
--- a/docs/tutorials/integrations/kserve/http.md
+++ b/docs/tutorials/integrations/kserve/http.md
@@ -7,12 +7,12 @@ template: main.html
 This tutorial shows how easy it is to run a load test for KServe when using HTTP to make requests. We use a sklearn model to demonstrate. The same approach works for any model type. 
 
 ???+ warning "Before you begin"
-    1. Try [Your first performance test](../../../getting-started/first-performance.md). Understand the main [concepts](../../../getting-started/concepts.md) behind Iter8.
-    2. Ensure that you have the [kubectl](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs.
-    3. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
+    1. Ensure that you have the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs installed.
+    2. Have access to a cluster running [KServe](https://kserve.github.io/website). You can create a [KServe Quickstart](https://kserve.github.io/website/0.11/get_started/#before-you-begin) environment as follows:
     ```shell
     curl -s "https://raw.githubusercontent.com/kserve/kserve/release-0.11/hack/quick_install.sh" | bash
     ```
+    If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     4. Have Grafana available. For example, Grafana can be installed on your cluster as follows:
     ```shell
     kubectl create deploy grafana --image=grafana/grafana
@@ -70,7 +70,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana by going to [http://localhost:3000](http://localhost:3000).
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `model-test` with the following parameters:
 
diff --git a/docs/tutorials/integrations/kubernetes-gateway-api/blue-green.md b/docs/tutorials/integrations/kubernetes-gateway-api/blue-green.md
index 7089032d..5f5b7cc1 100644
--- a/docs/tutorials/integrations/kubernetes-gateway-api/blue-green.md
+++ b/docs/tutorials/integrations/kubernetes-gateway-api/blue-green.md
@@ -16,7 +16,7 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 This tutorial uses the Kubernetes Gateway API to allow the use any service mesh that supports this API. In this case, we use demonstrate with [Linkerd](https://linkerd.io/).
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     2. [Install Linkerd](https://linkerd.io/2.14/getting-started/).
 
 ## Install the Iter8 controller
diff --git a/docs/tutorials/integrations/kubernetes-gateway-api/canary.md b/docs/tutorials/integrations/kubernetes-gateway-api/canary.md
index a2c14ed8..10522747 100644
--- a/docs/tutorials/integrations/kubernetes-gateway-api/canary.md
+++ b/docs/tutorials/integrations/kubernetes-gateway-api/canary.md
@@ -16,7 +16,7 @@ The chart provides the configuration needed for Iter8 to automatically deploy ap
 This tutorial uses the Kubernetes Gateway API to allow the use any service mesh that supports this API. In this case, we use demonstrate with [Linkerd](https://linkerd.io/).
 
 ???+ warning "Before you begin"
-    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. You can create a local Kubernetes cluster using tools like [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/).
+    1. Ensure that you have a Kubernetes cluster and the [`kubectl`](https://kubernetes.io/docs/reference/kubectl/) and [`helm`](https://helm.sh/) CLIs. If using a local cluster (for example, [Kind](https://kind.sigs.k8s.io/) or [Minikube](https://minikube.sigs.k8s.io/docs/)), we recommend providing the cluster with at least 16GB of memory.
     2. [Install Linkerd](https://linkerd.io/2.14/getting-started/).
 
 ## Install the Iter8 controller
diff --git a/docs/tutorials/load-test-grpc-multiple.md b/docs/tutorials/load-test-grpc-multiple.md
index 7df78796..f3c53ec3 100644
--- a/docs/tutorials/load-test-grpc-multiple.md
+++ b/docs/tutorials/load-test-grpc-multiple.md
@@ -59,7 +59,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana by going to [http://localhost:3000](http://localhost:3000).
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `routeguide-test` with the following parameters:
 
diff --git a/docs/tutorials/load-test-grpc.md b/docs/tutorials/load-test-grpc.md
index 2d0f66f6..0bb2dcb4 100644
--- a/docs/tutorials/load-test-grpc.md
+++ b/docs/tutorials/load-test-grpc.md
@@ -101,7 +101,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana by going to [http://localhost:3000](http://localhost:3000).
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `routeguide-test` with the following parameters:
 
diff --git a/docs/tutorials/load-test-http-multiple.md b/docs/tutorials/load-test-http-multiple.md
index e6565f21..475a1f3e 100644
--- a/docs/tutorials/load-test-http-multiple.md
+++ b/docs/tutorials/load-test-http-multiple.md
@@ -57,7 +57,7 @@ Inspect the metrics using Grafana. If Grafana is deployed to your cluster, port-
 kubectl port-forward service/grafana 3000:3000
 ```
 
-Open Grafana by going to [http://localhost:3000](http://localhost:3000).
+Open Grafana in a browser by going to [http://localhost:3000](http://localhost:3000) and login. The default username/password are `admin`/`admin`.
 
 [Add a JSON API data source](http://localhost:3000/connections/datasources/marcusolsson-json-datasource) `httpbin-test` with the following parameters: