diff --git a/src/endpoints/metrics/drift/compare_means.py b/src/endpoints/metrics/drift/compare_means.py
index 2b9a7e9a..6f0df177 100644
--- a/src/endpoints/metrics/drift/compare_means.py
+++ b/src/endpoints/metrics/drift/compare_means.py
@@ -103,18 +103,22 @@ async def compute_compare_means(
         )
 
     if not request.fit_columns:
-        raise HTTPException(
-            status_code=HTTPStatus.BAD_REQUEST,
-            detail="fitColumns is required - specify which features to test for drift",
-        )
-
-    # Validate feature names are not blank/whitespace
-    valid_features = [f.strip() for f in request.fit_columns if f.strip()]
-    if not valid_features:
-        raise HTTPException(
-            status_code=HTTPStatus.BAD_REQUEST,
-            detail="fitColumns must contain at least one non-empty feature name",
+        data_source = get_data_source()
+        metadata = await data_source.get_metadata(request.model_id)
+        request.fit_columns = list(metadata.input_schema.items.keys())
+        logger.info(
+            "fitColumns not specified, using all input columns for model %s: %s",
+            request.model_id,
+            request.fit_columns,
         )
+    else:
+        valid_features = [f.strip() for f in request.fit_columns if f.strip()]
+        if not valid_features:
+            raise HTTPException(
+                status_code=HTTPStatus.BAD_REQUEST,
+                detail="fitColumns must contain at least one non-empty feature name",
+            )
+        request.fit_columns = valid_features
 
     try:
         logger.info("Computing %s for model: %s", METRIC_NAME, request.model_id)
@@ -153,7 +157,7 @@ async def compute_compare_means(
 
         # Multi-feature case: iterate over features
         results = {}
-        for feature_name in valid_features:
+        for feature_name in request.fit_columns:
             if (
                 feature_name not in reference_df.columns
                 or feature_name not in current_df.columns
@@ -254,18 +258,22 @@ async def schedule_compare_means(request: CompareMeansMetricRequest) -> dict[str
         )
 
     if not request.fit_columns:
-        raise HTTPException(
-            status_code=HTTPStatus.BAD_REQUEST,
-            detail="fitColumns is required - specify which features to test for drift",
-        )
-
-    # Validate feature names are not blank/whitespace
-    valid_features = [f.strip() for f in request.fit_columns if f.strip()]
-    if not valid_features:
-        raise HTTPException(
-            status_code=HTTPStatus.BAD_REQUEST,
-            detail="fitColumns must contain at least one non-empty feature name",
+        data_source = get_data_source()
+        metadata = await data_source.get_metadata(request.model_id)
+        request.fit_columns = list(metadata.input_schema.items.keys())
+        logger.info(
+            "fitColumns not specified, using all input columns for model %s: %s",
+            request.model_id,
+            request.fit_columns,
         )
+    else:
+        valid_features = [f.strip() for f in request.fit_columns if f.strip()]
+        if not valid_features:
+            raise HTTPException(
+                status_code=HTTPStatus.BAD_REQUEST,
+                detail="fitColumns must contain at least one non-empty feature name",
+            )
+        request.fit_columns = valid_features
 
     try:
         # Generate UUID for this request
diff --git a/src/endpoints/metrics/drift/jensen_shannon.py b/src/endpoints/metrics/drift/jensen_shannon.py
index 36cef0f2..7977fe83 100644
--- a/src/endpoints/metrics/drift/jensen_shannon.py
+++ b/src/endpoints/metrics/drift/jensen_shannon.py
@@ -106,9 +106,13 @@ async def compute_jensenshannon(
         )
 
     if not request.fit_columns:
-        raise HTTPException(
-            status_code=HTTPStatus.BAD_REQUEST,
-            detail="fitColumns is required - specify which features to test for drift",
+        data_source = get_data_source()
+        metadata = await data_source.get_metadata(request.model_id)
+        request.fit_columns = list(metadata.input_schema.items.keys())
+        logger.info(
+            "fitColumns not specified, using all input columns for model %s: %s",
+            request.model_id,
+            request.fit_columns,
         )
 
     try:
@@ -231,9 +235,13 @@ async def schedule_jensenshannon(request: JensenShannonMetricRequest) -> dict[st
         )
 
     if not request.fit_columns:
-        raise HTTPException(
-            status_code=HTTPStatus.BAD_REQUEST,
-            detail="fitColumns is required - specify which features to test for drift",
+        data_source = get_data_source()
+        metadata = await data_source.get_metadata(request.model_id)
+        request.fit_columns = list(metadata.input_schema.items.keys())
+        logger.info(
+            "fitColumns not specified, using all input columns for model %s: %s",
+            request.model_id,
+            request.fit_columns,
         )
 
     # Get the scheduler and validate availability
diff --git a/src/endpoints/metrics/drift/kolmogorov_smirnov.py b/src/endpoints/metrics/drift/kolmogorov_smirnov.py
index 7875fa02..bda435d3 100644
--- a/src/endpoints/metrics/drift/kolmogorov_smirnov.py
+++ b/src/endpoints/metrics/drift/kolmogorov_smirnov.py
@@ -83,9 +83,13 @@ async def compute_kstest(
         )
 
     if not request.fit_columns:
-        raise HTTPException(
-            status_code=HTTPStatus.BAD_REQUEST,
-            detail="fitColumns is required - specify which features to test for drift",
+        data_source = get_data_source()
+        metadata = await data_source.get_metadata(request.model_id)
+        request.fit_columns = list(metadata.input_schema.items.keys())
+        logger.info(
+            "fitColumns not specified, using all input columns for model %s: %s",
+            request.model_id,
+            request.fit_columns,
         )
 
     try:
@@ -186,6 +190,16 @@ async def get_kstest_definition() -> dict[str, str]:
 @router.post("/metrics/drift/kstest/request")
 async def schedule_kstest(request: KSTestMetricRequest) -> dict[str, str]:
     """Schedule a recurring computation of KSTest metric."""
+    if not request.fit_columns:
+        data_source = get_data_source()
+        metadata = await data_source.get_metadata(request.model_id)
+        request.fit_columns = list(metadata.input_schema.items.keys())
+        logger.info(
+            "fitColumns not specified, using all input columns for model %s: %s",
+            request.model_id,
+            request.fit_columns,
+        )
+
     # Get the scheduler and validate availability
     scheduler = get_prometheus_scheduler()
     if not scheduler:
diff --git a/tests/endpoints/metrics/drift/factory.py b/tests/endpoints/metrics/drift/factory.py
index bd680689..95c37c9d 100644
--- a/tests/endpoints/metrics/drift/factory.py
+++ b/tests/endpoints/metrics/drift/factory.py
@@ -73,16 +73,17 @@ def make_compute_endpoint_test(
     def test_impl(_: object, mock_ds: MagicMock) -> None:
         """Test compute endpoint returns valid response structure."""
         # Create sample dataframe (Pandas or Polars based on df_type)
-        sample_df = _create_sample_dataframe(
-            request_payload.get("fitColumns", ["feature1"]),
-            df_type=df_type,
-        )
+        columns = request_payload.get("fitColumns", ["feature1"])
+        sample_df = _create_sample_dataframe(columns, df_type=df_type)
 
         # Mock data source
         mock_data_source = MagicMock()
         mock_data_source.get_dataframe_by_tag = AsyncMock(return_value=sample_df)
         mock_data_source.get_organic_dataframe = AsyncMock(return_value=sample_df)
         mock_data_source.get_dataframe = AsyncMock(return_value=sample_df)
+        mock_metadata = MagicMock()
+        mock_metadata.input_schema.items.keys.return_value = columns
+        mock_data_source.get_metadata = AsyncMock(return_value=mock_metadata)
         mock_ds.return_value = mock_data_source
 
         # Send request
@@ -338,17 +339,17 @@ def make_compute_endpoint_error_test(
     def test_impl(_: object, mock_ds: MagicMock) -> None:
         """Test compute endpoint error handling."""
         if setup_mocks:
-            # Create sample dataframe
-            sample_df = _create_sample_dataframe(
-                ["feature1", "feature2"],
-                df_type=df_type,
-            )
+            columns = ["feature1", "feature2"]
+            sample_df = _create_sample_dataframe(columns, df_type=df_type)
 
             # Mock data source
             mock_data_source = MagicMock()
             mock_data_source.get_dataframe_by_tag = AsyncMock(return_value=sample_df)
             mock_data_source.get_organic_dataframe = AsyncMock(return_value=sample_df)
             mock_data_source.get_dataframe = AsyncMock(return_value=sample_df)
+            mock_metadata = MagicMock()
+            mock_metadata.input_schema.items.keys.return_value = columns
+            mock_data_source.get_metadata = AsyncMock(return_value=mock_metadata)
             mock_ds.return_value = mock_data_source
 
         # Send request
diff --git a/tests/endpoints/metrics/drift/test_compare_means.py b/tests/endpoints/metrics/drift/test_compare_means.py
index 7ac3ab5b..35d04763 100644
--- a/tests/endpoints/metrics/drift/test_compare_means.py
+++ b/tests/endpoints/metrics/drift/test_compare_means.py
@@ -292,18 +292,18 @@ def test_multi_feature_drift_consistency_deterministic(self) -> None:
         expected_error_substring="referenceTag is required",
     )
 
-    test_compute_missing_fit_columns = factory.make_compute_endpoint_error_test(
-        metric_name="CompareMeans",
-        module_path="src.endpoints.metrics.drift.compare_means",
-        endpoint_path="/metrics/drift/comparemeans",
-        client=client,
-        request_payload={
-            "modelId": "test-model",
-            "referenceTag": "baseline",
-            # Missing fitColumns
-        },
-        expected_status_code=HTTPStatus.BAD_REQUEST,
-        expected_error_substring="fitColumns is required",
+    test_compute_missing_fit_columns_derives_from_metadata = (
+        factory.make_compute_endpoint_test(
+            metric_name="CompareMeans",
+            module_path="src.endpoints.metrics.drift.compare_means",
+            endpoint_path="/metrics/drift/comparemeans",
+            client=client,
+            request_payload={
+                "modelId": "test-model",
+                "referenceTag": "baseline",
+            },
+            expected_response_keys=["status", "value", "drift_detected"],
+        )
     )
 
     test_compute_invalid_feature = factory.make_compute_endpoint_error_test(
diff --git a/tests/endpoints/metrics/drift/test_jensen_shannon.py b/tests/endpoints/metrics/drift/test_jensen_shannon.py
index a86d2901..f474d970 100644
--- a/tests/endpoints/metrics/drift/test_jensen_shannon.py
+++ b/tests/endpoints/metrics/drift/test_jensen_shannon.py
@@ -129,18 +129,18 @@ class TestJensenShannonEndpoints:
         expected_error_substring="referenceTag is required",
     )
 
-    test_compute_missing_fit_columns = factory.make_compute_endpoint_error_test(
-        metric_name="JensenShannon",
-        module_path="src.endpoints.metrics.drift.jensen_shannon",
-        endpoint_path="/metrics/drift/jensenshannon",
-        client=client,
-        request_payload={
-            "modelId": "test-model",
-            "referenceTag": "baseline",
-            # Missing fitColumns
-        },
-        expected_status_code=HTTPStatus.BAD_REQUEST,
-        expected_error_substring="fitColumns is required",
+    test_compute_missing_fit_columns_derives_from_metadata = (
+        factory.make_compute_endpoint_test(
+            metric_name="JensenShannon",
+            module_path="src.endpoints.metrics.drift.jensen_shannon",
+            endpoint_path="/metrics/drift/jensenshannon",
+            client=client,
+            request_payload={
+                "modelId": "test-model",
+                "referenceTag": "baseline",
+            },
+            expected_response_keys=["status", "value", "drift_detected"],
+        )
     )
 
     test_compute_invalid_feature = factory.make_compute_endpoint_error_test(
diff --git a/tests/endpoints/metrics/drift/test_kolmogorov_smirnov.py b/tests/endpoints/metrics/drift/test_kolmogorov_smirnov.py
index 9de2d5d6..08662351 100644
--- a/tests/endpoints/metrics/drift/test_kolmogorov_smirnov.py
+++ b/tests/endpoints/metrics/drift/test_kolmogorov_smirnov.py
@@ -114,18 +114,18 @@ class TestKSTestEndpoints:
         expected_error_substring="referenceTag is required",
     )
 
-    test_compute_missing_fit_columns = factory.make_compute_endpoint_error_test(
-        metric_name="KSTest",
-        module_path="src.endpoints.metrics.drift.kolmogorov_smirnov",
-        endpoint_path="/metrics/drift/kstest",
-        client=client,
-        request_payload={
-            "modelId": "test-model",
-            "referenceTag": "baseline",
-            # Missing fitColumns
-        },
-        expected_status_code=HTTPStatus.BAD_REQUEST,
-        expected_error_substring="fitColumns is required",
+    test_compute_missing_fit_columns_derives_from_metadata = (
+        factory.make_compute_endpoint_test(
+            metric_name="KSTest",
+            module_path="src.endpoints.metrics.drift.kolmogorov_smirnov",
+            endpoint_path="/metrics/drift/kstest",
+            client=client,
+            request_payload={
+                "modelId": "test-model",
+                "referenceTag": "baseline",
+            },
+            expected_response_keys=["status", "value", "drift_detected"],
+        )
     )
 
     test_compute_invalid_feature = factory.make_compute_endpoint_error_test(