diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index adf6522f76a1a..a84f168164946 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -190,10 +190,9 @@ class DatetimeLikeArrayMixin(OpsMixin, NDArrayBackedExtensionArray):
 
     Assumes that __new__/__init__ defines:
         _ndarray
-        _freq
 
-    and that the inheriting class has methods:
-        _generate_range
+    and that inheriting subclass implements:
+        freq
     """
 
     # _infer_matches -> which infer_dtype strings are close enough to our own
@@ -201,6 +200,7 @@ class DatetimeLikeArrayMixin(OpsMixin, NDArrayBackedExtensionArray):
     _is_recognized_dtype: Callable[[DtypeObj], bool]
     _recognized_scalars: tuple[type, ...]
     _ndarray: np.ndarray
+    freq: BaseOffset | None
 
     @cache_readonly
     def _can_hold_na(self) -> bool:
@@ -896,24 +896,6 @@ def _maybe_mask_results(
     # ------------------------------------------------------------------
     # Frequency Properties/Methods
 
-    @property
-    def freq(self):
-        """
-        Return the frequency object if it is set, otherwise None.
-        """
-        return self._freq
-
-    @freq.setter
-    def freq(self, value) -> None:
-        if value is not None:
-            value = to_offset(value)
-            self._validate_frequency(self, value)
-
-            if self.ndim > 1:
-                raise ValueError("Cannot set freq with ndim > 1")
-
-        self._freq = value
-
     @property
     def freqstr(self) -> str | None:
         """
@@ -955,51 +937,6 @@ def resolution(self) -> str:
         # error: Item "None" of "Optional[Any]" has no attribute "attrname"
         return self._resolution_obj.attrname  # type: ignore[union-attr]
 
-    @classmethod
-    def _validate_frequency(cls, index, freq, **kwargs):
-        """
-        Validate that a frequency is compatible with the values of a given
-        Datetime Array/Index or Timedelta Array/Index
-
-        Parameters
-        ----------
-        index : DatetimeIndex or TimedeltaIndex
-            The index on which to determine if the given frequency is valid
-        freq : DateOffset
-            The frequency to validate
-        """
-        # TODO: this is not applicable to PeriodArray, move to correct Mixin
-        inferred = index.inferred_freq
-        if index.size == 0 or inferred == freq.freqstr:
-            return None
-
-        try:
-            on_freq = cls._generate_range(
-                start=index[0], end=None, periods=len(index), freq=freq, **kwargs
-            )
-            if not np.array_equal(index.asi8, on_freq.asi8):
-                raise ValueError
-        except ValueError as e:
-            if "non-fixed" in str(e):
-                # non-fixed frequencies are not meaningful for timedelta64;
-                #  we retain that error message
-                raise e
-            # GH#11587 the main way this is reached is if the `np.array_equal`
-            #  check above is False.  This can also be reached if index[0]
-            #  is `NaT`, in which case the call to `cls._generate_range` will
-            #  raise a ValueError, which we re-raise with a more targeted
-            #  message.
-            raise ValueError(
-                f"Inferred frequency {inferred} from passed values "
-                f"does not conform to passed frequency {freq.freqstr}"
-            ) from e
-
-    @classmethod
-    def _generate_range(
-        cls: type[DatetimeLikeArrayT], start, end, periods, freq, *args, **kwargs
-    ) -> DatetimeLikeArrayT:
-        raise AbstractMethodError(cls)
-
     # monotonicity/uniqueness properties are called via frequencies.infer_freq,
     #  see GH#23789
 
@@ -1953,6 +1890,68 @@ def __init__(
     def _validate_dtype(cls, values, dtype):
         raise AbstractMethodError(cls)
 
+    @property
+    def freq(self):
+        """
+        Return the frequency object if it is set, otherwise None.
+        """
+        return self._freq
+
+    @freq.setter
+    def freq(self, value) -> None:
+        if value is not None:
+            value = to_offset(value)
+            self._validate_frequency(self, value)
+
+            if self.ndim > 1:
+                raise ValueError("Cannot set freq with ndim > 1")
+
+        self._freq = value
+
+    @classmethod
+    def _validate_frequency(cls, index, freq, **kwargs):
+        """
+        Validate that a frequency is compatible with the values of a given
+        Datetime Array/Index or Timedelta Array/Index
+
+        Parameters
+        ----------
+        index : DatetimeIndex or TimedeltaIndex
+            The index on which to determine if the given frequency is valid
+        freq : DateOffset
+            The frequency to validate
+        """
+        inferred = index.inferred_freq
+        if index.size == 0 or inferred == freq.freqstr:
+            return None
+
+        try:
+            on_freq = cls._generate_range(
+                start=index[0], end=None, periods=len(index), freq=freq, **kwargs
+            )
+            if not np.array_equal(index.asi8, on_freq.asi8):
+                raise ValueError
+        except ValueError as err:
+            if "non-fixed" in str(err):
+                # non-fixed frequencies are not meaningful for timedelta64;
+                #  we retain that error message
+                raise err
+            # GH#11587 the main way this is reached is if the `np.array_equal`
+            #  check above is False.  This can also be reached if index[0]
+            #  is `NaT`, in which case the call to `cls._generate_range` will
+            #  raise a ValueError, which we re-raise with a more targeted
+            #  message.
+            raise ValueError(
+                f"Inferred frequency {inferred} from passed values "
+                f"does not conform to passed frequency {freq.freqstr}"
+            ) from err
+
+    @classmethod
+    def _generate_range(
+        cls: type[DatetimeLikeArrayT], start, end, periods, freq, *args, **kwargs
+    ) -> DatetimeLikeArrayT:
+        raise AbstractMethodError(cls)
+
     # --------------------------------------------------------------
 
     @cache_readonly
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 859bb53b6489a..e6682b0dea814 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -353,8 +353,8 @@ def _check_compatible_with(self, other) -> None:
     def dtype(self) -> PeriodDtype:
         return self._dtype
 
-    # error: Read-only property cannot override read-write property
-    @property  # type: ignore[misc]
+    # error: Cannot override writeable attribute with read-only property
+    @property  # type: ignore[override]
     def freq(self) -> BaseOffset:
         """
         Return the frequency object for this PeriodArray.
diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index 1119b6e3b83ad..fde000f84e581 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -82,7 +82,7 @@
     DatetimeLikeArrayMixin,
     cache=True,
 )
-@inherit_names(["mean", "freq", "freqstr"], DatetimeLikeArrayMixin)
+@inherit_names(["mean", "freqstr"], DatetimeLikeArrayMixin)
 class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex):
     """
     Common ops mixin to support a unified interface datetimelike Index.
@@ -90,10 +90,18 @@ class DatetimeIndexOpsMixin(NDArrayBackedExtensionIndex):
 
     _can_hold_strings = False
     _data: DatetimeArray | TimedeltaArray | PeriodArray
-    freq: BaseOffset | None
     freqstr: str | None
     _resolution_obj: Resolution
 
+    @property
+    def freq(self) -> BaseOffset | None:
+        return self._data.freq
+
+    @freq.setter
+    def freq(self, value) -> None:
+        # error: Property "freq" defined in "PeriodArray" is read-only  [misc]
+        self._data.freq = value  # type: ignore[misc]
+
     @property
     def asi8(self) -> npt.NDArray[np.int64]:
         return self._data.asi8