diff --git a/doc/source/whatsnew/v1.4.0.rst b/doc/source/whatsnew/v1.4.0.rst
index ffd32e263aa50..6baed863476a9 100644
--- a/doc/source/whatsnew/v1.4.0.rst
+++ b/doc/source/whatsnew/v1.4.0.rst
@@ -809,6 +809,7 @@ Groupby/resample/rolling
 - Bug in :meth:`GroupBy.nth` failing on ``axis=1`` (:issue:`43926`)
 - Fixed bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` not respecting right bound on centered datetime-like windows, if the index contain duplicates (:issue:`3944`)
 - Bug in :meth:`Series.rolling` and :meth:`DataFrame.rolling` when using a :class:`pandas.api.indexers.BaseIndexer` subclass that returned unequal start and end arrays would segfault instead of raising a ``ValueError`` (:issue:`44470`)
+- Bug in :meth:`Groupby.rolling` when non-monotonic data passed, fails to correctly raise ``ValueError`` (:issue:`43909`)
 - Fixed bug where grouping by a :class:`Series` that has a categorical data type and length unequal to the axis of grouping raised ``ValueError`` (:issue:`44179`)
 
 Reshaping
diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py
index defae3392bfce..49d696f461300 100644
--- a/pandas/core/window/rolling.py
+++ b/pandas/core/window/rolling.py
@@ -2626,8 +2626,9 @@ def _get_window_indexer(self) -> GroupbyIndexer:
     def _validate_monotonic(self):
         """
         Validate that on is monotonic;
-        in this case we have to check only for nans, because
-        monotonicity was already validated at a higher level.
         """
-        if self._on.hasnans:
+        if (
+            not (self._on.is_monotonic_increasing or self._on.is_monotonic_decreasing)
+            or self._on.hasnans
+        ):
             self._raise_monotonic_error()
diff --git a/pandas/tests/window/test_rolling.py b/pandas/tests/window/test_rolling.py
index b60f2e60e1035..814bd6b998182 100644
--- a/pandas/tests/window/test_rolling.py
+++ b/pandas/tests/window/test_rolling.py
@@ -1419,6 +1419,18 @@ def test_groupby_rolling_nan_included():
     tm.assert_frame_equal(result, expected)
 
 
+def test_groupby_rolling_non_monotonic():
+    # GH 43909
+
+    shuffled = [3, 0, 1, 2]
+    sec = 1_000
+    df = DataFrame(
+        [{"t": Timestamp(2 * x * sec), "x": x + 1, "c": 42} for x in shuffled]
+    )
+    with pytest.raises(ValueError, match=r".* must be monotonic"):
+        df.groupby("c").rolling(on="t", window="3s")
+
+
 @pytest.mark.parametrize("method", ["skew", "kurt"])
 def test_rolling_skew_kurt_numerical_stability(method):
     # GH#6929
diff --git a/pandas/tests/window/test_timeseries_window.py b/pandas/tests/window/test_timeseries_window.py
index 7cd319480083b..f2cf7bd47e15b 100644
--- a/pandas/tests/window/test_timeseries_window.py
+++ b/pandas/tests/window/test_timeseries_window.py
@@ -648,6 +648,9 @@ def test_groupby_monotonic(self):
         # GH 15130
         # we don't need to validate monotonicity when grouping
 
+        # GH 43909 we should raise an error here to match
+        # behaviour of non-groupby rolling.
+
         data = [
             ["David", "1/1/2015", 100],
             ["David", "1/5/2015", 500],
@@ -663,6 +666,7 @@ def test_groupby_monotonic(self):
 
         df = DataFrame(data=data, columns=["name", "date", "amount"])
         df["date"] = to_datetime(df["date"])
+        df = df.sort_values("date")
 
         expected = (
             df.set_index("date")
@@ -672,9 +676,11 @@ def test_groupby_monotonic(self):
         result = df.groupby("name").rolling("180D", on="date")["amount"].sum()
         tm.assert_series_equal(result, expected)
 
-    def test_non_monotonic(self):
+    def test_non_monotonic_raises(self):
         # GH 13966 (similar to #15130, closed by #15175)
 
+        # superseded by 43909
+
         dates = date_range(start="2016-01-01 09:30:00", periods=20, freq="s")
         df = DataFrame(
             {
@@ -684,11 +690,13 @@ def test_non_monotonic(self):
             }
         )
 
-        result = df.groupby("A").rolling("4s", on="B").C.mean()
         expected = (
             df.set_index("B").groupby("A").apply(lambda x: x.rolling("4s")["C"].mean())
         )
-        tm.assert_series_equal(result, expected)
+        with pytest.raises(ValueError, match=r".* must be monotonic"):
+            df.groupby("A").rolling(
+                "4s", on="B"
+            ).C.mean()  # should raise for non-monotonic t series
 
         df2 = df.sort_values("B")
         result = df2.groupby("A").rolling("4s", on="B").C.mean()