diff --git a/solara/website/pages/examples/general/deploy_model.py b/solara/website/pages/examples/general/deploy_model.py
new file mode 100644
index 000000000..38e642250
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+++ b/solara/website/pages/examples/general/deploy_model.py
@@ -0,0 +1,114 @@
+"""# Deploy model demo
+
+This show off a combination of [solara.lab.Menu](/api/menu) and [solara.lab.ConfirmationDialog](/api/confirmation_dialog)
+and [solara.use_thread](/api/use_thread) to demonstrate how to tune, fit and deploy a model.
+The actually deployment is not real, but simulated by a thread that returns a boolean value on success and sleep for a while to
+similate the deployment taking time.
+
+ * Right click on the plot to either fit or reset the model parameters.
+ * Clicking on the button shows a custom menu to deploy to several environments.
+ * A confirmation dialog is used before executing the deployment.
+ * If the deployment fails, a confirmation dialog is shown to retry the deployment.
+"""
+import time
+
+import numpy as np
+import pandas as pd
+import plotly.express as px
+
+import solara
+import solara.lab
+
+slope = solara.reactive(1.5)
+intercept = solara.reactive(0.5)
+
+x = np.arange(0, 100, 2) / 10
+# fake data with noise
+y_data = (slope.value * 0.8) * x + (intercept.value + 0.4) + np.random.normal(0, 0.5, len(x))
+
+
+@solara.component
+def Page():
+    deploy_environment_request = solara.use_reactive("")
+    deploy_environment = solara.use_reactive("")
+    fake_failed = solara.use_reactive(False)
+
+    def deploy_model():
+        if deploy_environment.value == "":
+            return
+
+        # 'dummy' deployment
+        time.sleep(1.5)
+
+        # fake a failure the first time
+        if not fake_failed.value:
+            fake_failed.value = True
+            return False
+        else:
+            return True
+
+    deploy_state: solara.Result[bool] = solara.use_thread(deploy_model, dependencies=[deploy_environment.value])
+
+    x_model = x
+    y_model = (slope.value) * x_model + (intercept.value)
+    df1 = pd.DataFrame({"x": x, "y": y_data})
+    df2 = pd.DataFrame({"x": x_model, "y": y_model})
+    df = pd.concat([df1, df2], keys=["data", "model"], names=["type"]).reset_index()
+    fig = px.line(df, x="x", y="y", color="type")
+    with solara.Card("My fancy model"):
+        fig_element = solara.FigurePlotly(fig)
+
+        def reset():
+            slope.set(1.5)
+            intercept.set(0.5)
+
+        def fit():
+            slope.set(1.5 * 0.8)
+            intercept.set(0.9)
+
+        with solara.lab.ContextMenu(activator=fig_element):
+            solara.Button("Reset", text=True, icon_name="mdi-refresh", on_click=reset)
+            solara.Button("Fit", text=True, icon_name="mdi-brain", on_click=fit)
+
+        solara.FloatSlider("Slope", value=slope, min=-2, max=5, step=0.1)
+        solara.FloatSlider("Intercept", value=intercept, min=-10, max=10, step=0.1)
+
+        solara.lab.ConfirmationDialog(
+            deploy_environment_request.value != "",
+            title="Confirm deployment",
+            content=solara.Markdown(f"Are you sure you want to deploy to **{deploy_environment_request.value}**?"),
+            ok=f"Deploy to {deploy_environment_request.value}",
+            on_ok=lambda: deploy_environment.set(deploy_environment_request.value),
+            on_close=lambda: deploy_environment_request.set(""),
+        )
+
+        solara.lab.ConfirmationDialog(
+            deploy_state.value is False and deploy_state.state == solara.ResultState.FINISHED,
+            title="Deployment failed",
+            on_close=lambda: None,
+            on_ok=lambda: deploy_state.retry(),
+            content=solara.Error(solara.Markdown(f"Deployment to **{deploy_environment.value}** failed. Do you want to try again?")),
+            ok="Retry deployment",
+            cancel="Ignore the failure",
+        )
+
+        def request_deploy(environment):
+            deploy_environment.value = ""
+            deploy_environment_request.set(environment)
+
+        deploying = bool(deploy_environment.value and deploy_state.state == solara.ResultState.RUNNING)
+        deploy_button = solara.Button("Deploy model", color="primary", icon_name="mdi-rocket", loading=deploying)
+        with solara.lab.Menu(activator=deploy_button):
+            with solara.Column(gap="0px"):
+                solara.Button("Deploy to testing environment", text=True, icon_name="mdi-bug", on_click=lambda: request_deploy("testing"))
+                solara.Button("Deploy to staging environment", text=True, icon_name="mdi-test-tube", on_click=lambda: request_deploy("staging"))
+                solara.Button(
+                    "Deploy to production",
+                    text=True,
+                    icon_name="mdi-rocket",
+                    style={"justify-content": "left"},  # make the icon appear on the left
+                    on_click=lambda: request_deploy("production"),
+                )
+        if deploy_state.value is True and deploy_state.state == solara.ResultState.FINISHED:
+            # add some margin in the y direction using my-4
+            solara.Success(solara.Markdown(f"Model deployed successfully to **{deploy_environment.value}**!"), classes=["my-4"])