diff --git a/src/zenml/constants.py b/src/zenml/constants.py
index f904b9d480f..da9c4155416 100644
--- a/src/zenml/constants.py
+++ b/src/zenml/constants.py
@@ -162,7 +162,8 @@ def handle_int_env_var(var: str, default: int = 0) -> int:
 
 # Services
 DEFAULT_SERVICE_START_STOP_TIMEOUT = 60
-DEFAULT_LOCAL_SERVICE_IP_ADDRESS = "127.0.0.1"
+# DEFAULT_LOCAL_SERVICE_IP_ADDRESS = "127.0.0.1"
+DEFAULT_LOCAL_SERVICE_IP_ADDRESS = "0.0.0.0"
 ZEN_SERVER_ENTRYPOINT = "zenml.zen_server.zen_server_api:app"
 
 STEP_SOURCE_PARAMETER_NAME = "step_source"
diff --git a/src/zenml/stack/stack.py b/src/zenml/stack/stack.py
index 2abe2ba852a..7456301d1cb 100644
--- a/src/zenml/stack/stack.py
+++ b/src/zenml/stack/stack.py
@@ -805,15 +805,15 @@ def prepare_pipeline_deployment(
                     f"for more information."
                 )
 
-        if self.requires_remote_server and Client().zen_store.is_local_store():
-            raise RuntimeError(
-                "Stacks with remote components such as remote orchestrators "
-                "and step operators require a remote "
-                "ZenML server. To run a pipeline with this stack you need to "
-                "connect to a remote ZenML server first. Check out "
-                "https://docs.zenml.io/user-guide/starter-guide/switch-to-production "
-                "for more information on how to deploy ZenML."
-            )
+        # if self.requires_remote_server and Client().zen_store.is_local_store():
+        #     raise RuntimeError(
+        #         "Stacks with remote components such as remote orchestrators "
+        #         "and step operators require a remote "
+        #         "ZenML server. To run a pipeline with this stack you need to "
+        #         "connect to a remote ZenML server first. Check out "
+        #         "https://docs.zenml.io/user-guide/starter-guide/switch-to-production "
+        #         "for more information on how to deploy ZenML."
+        #     )
 
         for component in self.components.values():
             component.prepare_pipeline_deployment(