diff --git a/ads/opctl/operator/lowcode/forecast/const.py b/ads/opctl/operator/lowcode/forecast/const.py
index 3d057b3ec..3299f57d7 100644
--- a/ads/opctl/operator/lowcode/forecast/const.py
+++ b/ads/opctl/operator/lowcode/forecast/const.py
@@ -81,3 +81,4 @@ class ForecastOutputColumns(str, metaclass=ExtendedEnumMeta):
 DEFAULT_TRIALS = 10
 SUMMARY_METRICS_HORIZON_LIMIT = 10
 PROPHET_INTERNAL_DATE_COL = "ds"
+RENDER_LIMIT = 5000
diff --git a/ads/opctl/operator/lowcode/forecast/model/base_model.py b/ads/opctl/operator/lowcode/forecast/model/base_model.py
index e0f16eca1..2cb06665a 100644
--- a/ads/opctl/operator/lowcode/forecast/model/base_model.py
+++ b/ads/opctl/operator/lowcode/forecast/model/base_model.py
@@ -598,10 +598,7 @@ def explain_model(self, datetime_col_name, explain_predict_fn) -> dict:
             data_trimmed[datetime_col_name] = data_trimmed[datetime_col_name].apply(lambda x: x.timestamp())
             kernel_explnr = PermutationExplainer(
                 model=explain_predict_fn,
-                masker=data_trimmed,
-                keep_index=False
-                if self.spec.model == SupportedModels.AutoMLX
-                else True,
+                masker=data_trimmed
             )
 
             kernel_explnr_vals = kernel_explnr.shap_values(data_trimmed)
diff --git a/ads/opctl/operator/lowcode/forecast/utils.py b/ads/opctl/operator/lowcode/forecast/utils.py
index 3d4ad6ee6..8810bd986 100644
--- a/ads/opctl/operator/lowcode/forecast/utils.py
+++ b/ads/opctl/operator/lowcode/forecast/utils.py
@@ -28,7 +28,7 @@
 from ads.dataset.label_encoder import DataFrameLabelEncoder
 from ads.opctl import logger
 
-from .const import SupportedMetrics, SupportedModels
+from .const import SupportedMetrics, SupportedModels, RENDER_LIMIT
 from .errors import ForecastInputDataError, ForecastSchemaYamlError
 from .operator_config import ForecastOperatorSpec, ForecastOperatorConfig
 
@@ -417,6 +417,23 @@ def get_forecast_plots(
     def plot_forecast_plotly(idx, col):
         fig = go.Figure()
         forecast_i = forecast_output.get_target_category(col)
+        actual_length = len(forecast_i)
+        if actual_length > RENDER_LIMIT:
+            forecast_i = forecast_i.tail(RENDER_LIMIT)
+            text = f"<i>To improve rendering speed, subsampled the data from {actual_length}" \
+                   f" rows to {RENDER_LIMIT} rows for this plot.</i>"
+            fig.update_layout(
+                annotations=[
+                    go.layout.Annotation(
+                        x=0.01,
+                        y=1.1,
+                        xref="paper",
+                        yref="paper",
+                        text=text,
+                        showarrow=False
+                    )
+                ]
+            )
         upper_bound = forecast_output.upper_bound_name
         lower_bound = forecast_output.lower_bound_name
         if upper_bound is not None and lower_bound is not None: