diff --git a/numalogic/registry/mlflow_registry.py b/numalogic/registry/mlflow_registry.py
index 075b2ab5..e49b93a2 100644
--- a/numalogic/registry/mlflow_registry.py
+++ b/numalogic/registry/mlflow_registry.py
@@ -164,6 +164,7 @@ def load(
             if latest:
                 cached_artifact = self._load_from_cache(model_key)
                 if cached_artifact:
+                    _LOGGER.debug("Found cached artifact for key: %s", model_key)
                     return cached_artifact
                 version_info = self.client.get_latest_versions(model_key, stages=[self.model_stage])
                 if not version_info:
diff --git a/numalogic/registry/redis_registry.py b/numalogic/registry/redis_registry.py
index b84ae404..35e32529 100644
--- a/numalogic/registry/redis_registry.py
+++ b/numalogic/registry/redis_registry.py
@@ -129,6 +129,7 @@ def __get_artifact_data(
     def __load_latest_artifact(self, key: str) -> ArtifactData:
         cached_artifact = self._load_from_cache(key)
         if cached_artifact:
+            _LOGGER.debug("Found cached artifact for key: %s", key)
             return cached_artifact
         production_key = self.__construct_production_key(key)
         if not self.client.exists(production_key):
diff --git a/tests/registry/test_mlflow_registry.py b/tests/registry/test_mlflow_registry.py
index 09332cb1..6c96a513 100644
--- a/tests/registry/test_mlflow_registry.py
+++ b/tests/registry/test_mlflow_registry.py
@@ -10,7 +10,7 @@
 from sklearn.ensemble import RandomForestRegressor
 
 from numalogic.models.autoencoder.variants import VanillaAE
-from numalogic.registry import MLflowRegistry, ArtifactData
+from numalogic.registry import MLflowRegistry, ArtifactData, LocalLRUCache
 from numalogic.registry.mlflow_registry import ModelStage
 from numalogic.tools.exceptions import ModelVersionError
 from tests.registry._mlflow_utils import (
@@ -338,6 +338,31 @@ def test_no_cache(self):
         self.assertIsNone(registry._load_from_cache("key"))
         self.assertIsNone(registry._clear_cache("key"))
 
+    def test_cache(self):
+        cache_registry = LocalLRUCache()
+        registry = MLflowRegistry(TRACKING_URI, cache_registry=cache_registry)
+        registry._save_in_cache("key", ArtifactData(artifact=self.model, extras={}, metadata={}))
+        self.assertIsNotNone(registry._load_from_cache("key"))
+        self.assertIsNotNone(registry._clear_cache("key"))
+
+    @patch("mlflow.pytorch.log_model", mock_log_model_pytorch())
+    @patch("mlflow.start_run", Mock(return_value=ActiveRun(return_pytorch_rundata_dict())))
+    @patch("mlflow.active_run", Mock(return_value=return_pytorch_rundata_dict()))
+    @patch("mlflow.log_params", {"lr": 0.01})
+    @patch("mlflow.tracking.MlflowClient.transition_model_version_stage", mock_transition_stage)
+    @patch("mlflow.tracking.MlflowClient.get_latest_versions", mock_get_model_version)
+    @patch("mlflow.pytorch.load_model", Mock(return_value=VanillaAE(10)))
+    @patch("mlflow.tracking.MlflowClient.get_run", Mock(return_value=return_pytorch_rundata_dict()))
+    def test_cache_loading(self):
+        cache_registry = LocalLRUCache(ttl=50000)
+        ml = MLflowRegistry(TRACKING_URI, cache_registry=cache_registry)
+        ml.save(skeys=self.skeys, dkeys=self.dkeys, artifact=self.model, **{"lr": 0.01})
+        ml.load(skeys=self.skeys, dkeys=self.dkeys, artifact_type="pytorch")
+        key = MLflowRegistry.construct_key(self.skeys, self.dkeys)
+        self.assertIsNotNone(ml._load_from_cache(key))
+        data = ml.load(skeys=self.skeys, dkeys=self.dkeys, artifact_type="pytorch")
+        self.assertIsNotNone(data)
+
 
 if __name__ == "__main__":
     unittest.main()