diff --git a/config.yaml b/config.yaml
index d17793d1..88d97359 100644
--- a/config.yaml
+++ b/config.yaml
@@ -37,3 +37,13 @@ options:
     description: Deploy the NodePort service for MLFlow
     type: boolean
     default: true
+  serve_artifacts:
+    description: |
+      Enables serving of artifacts requests by routing these request to ./mlartifacts directory.
+    type: boolean
+    default: false
+  artifacts_destination:
+    description: |
+      Base location URI of resolve artifact requests.
+    type: string
+    default: './mlartifacts'
diff --git a/src/charm.py b/src/charm.py
index 3f58f924..46c859d9 100755
--- a/src/charm.py
+++ b/src/charm.py
@@ -50,6 +50,8 @@ def __init__(self, *args):
 
         self.logger = logging.getLogger(__name__)
         self._port = self.model.config["mlflow_port"]
+        self._serve_artifacts = self.model.config.get("serve_artifacts", False)
+        self._artifacts_destination = self.model.config.get("artifacts_destination", "")
         self._exporter_port = self.model.config["mlflow_prometheus_exporter_port"]
         self._container_name = "mlflow-server"
         self._exporter_container_name = "mlflow-prometheus-exporter"
@@ -199,7 +201,11 @@ def _get_env_vars(self, relational_db_data, object_storage_data):
 
     def _charmed_mlflow_layer(self, env_vars, default_artifact_root) -> Layer:
         """Create and return Pebble framework layer."""
-
+        serve_artifacts = ""
+        if self._serve_artifacts:
+            serve_artifacts = (
+                f"--serve-artifacts --artifacts-destination {self._artifacts_destination}"
+            )
         layer_config = {
             "summary": "mlflow-server layer",
             "description": "Pebble config layer for mlflow-server",
@@ -214,6 +220,7 @@ def _charmed_mlflow_layer(self, env_vars, default_artifact_root) -> Layer:
                         "0.0.0.0 "
                         "--port "
                         f"{self._port} "
+                        f"{serve_artifacts} "
                         "--backend-store-uri "
                         f"{env_vars['MLFLOW_TRACKING_URI']} "
                         "--default-artifact-root "
diff --git a/tests/unit/test_operator.py b/tests/unit/test_operator.py
index f95a4c8f..c34c0c8f 100644
--- a/tests/unit/test_operator.py
+++ b/tests/unit/test_operator.py
@@ -21,7 +21,19 @@
             "summary": "Entrypoint of mlflow-server image",
             "startup": "enabled",
             "override": "replace",
-            "command": "mlflow server --host 0.0.0.0 --port 5000 --backend-store-uri test --default-artifact-root s3:/// --expose-prometheus /metrics",  # noqa: E501
+            "command": "mlflow server --host 0.0.0.0 --port 5000  --backend-store-uri test --default-artifact-root s3:/// --expose-prometheus /metrics",  # noqa: E501
+            "environment": {"MLFLOW_TRACKING_URI": "test"},
+        },
+    )
+}
+EXPECTED_SERVICE_ARTIFACTS = {
+    "mlflow-server": Service(
+        "mlflow-server",
+        raw={
+            "summary": "Entrypoint of mlflow-server image",
+            "startup": "enabled",
+            "override": "replace",
+            "command": "mlflow server --host 0.0.0.0 --port 5000 --serve-artifacts --artifacts-destination s3:/// --backend-store-uri test --default-artifact-root s3:/// --expose-prometheus /metrics",  # noqa: E501
             "environment": {"MLFLOW_TRACKING_URI": "test"},
         },
     )
@@ -425,6 +437,24 @@ def test_update_layer_success(
         )
         assert harness.charm.container.get_plan().services == EXPECTED_SERVICE
 
+    @patch(
+        "charm.KubernetesServicePatch",
+        lambda x, y, service_name, service_type, refresh_event: None,
+    )
+    def test_config_artifact_success(
+        self,
+        harness: Harness,
+    ):
+        harness.update_config({"serve_artifacts": True, "artifacts_destination": "s3:///"})
+        harness.begin()
+        harness.charm._update_layer(
+            harness.charm.container,
+            harness.charm._container_name,
+            harness.charm._charmed_mlflow_layer({"MLFLOW_TRACKING_URI": "test"}, ""),
+        )
+        updated_plan = harness.get_container_pebble_plan('mlflow-server').to_dict()
+        assert harness.charm.container.get_plan().services == EXPECTED_SERVICE_ARTIFACTS
+
     @patch(
         "charm.KubernetesServicePatch",
         lambda x, y, service_name, service_type, refresh_event: None,