diff --git a/composer/algorithms/gradient_clipping/gradient_clipping.py b/composer/algorithms/gradient_clipping/gradient_clipping.py
index abd712b152..73464d9c8e 100644
--- a/composer/algorithms/gradient_clipping/gradient_clipping.py
+++ b/composer/algorithms/gradient_clipping/gradient_clipping.py
@@ -25,7 +25,7 @@ def apply_gradient_clipping(
     clipping_type: str,
     clipping_threshold: float,
     fsdp_enabled: bool,
-):
+) -> Union[torch.Tensor, None]:
     """Clips all gradients in model based on specified clipping_type.
 
     Args:
@@ -41,12 +41,16 @@ def apply_gradient_clipping(
             threshold by which if grad_norm / weight_norm is greater than this threshold then
             scale gradients by this threshold * (weight_norm / grad_norm) (for 'adaptive').
         fsdp_enabled (bool): Bool of if the model is a FSDP model or not.
+
+    Returns:
+        Union[torch.Tensor, None]: The total gradient norm before clipping for 'norm' clipping type,
+            None otherwise.
     """
     if fsdp_enabled:
         for module in model.modules():
             if isinstance(module, FullyShardedDataParallel) and module.check_is_root():
                 if clipping_type == 'norm':
-                    module.clip_grad_norm_(max_norm=clipping_threshold)
+                    return module.clip_grad_norm_(max_norm=clipping_threshold)
                 elif clipping_type == 'value':
                     module.clip_grad_norm_(max_norm=clipping_threshold, norm_type=float('inf'))
                 else:
@@ -56,12 +60,14 @@ def apply_gradient_clipping(
         if clipping_type == 'adaptive':
             _apply_agc(parameters, clipping_threshold=clipping_threshold)
         elif clipping_type == 'norm':
-            torch.nn.utils.clip_grad_norm_(parameters, max_norm=clipping_threshold)
+            return torch.nn.utils.clip_grad_norm_(parameters, max_norm=clipping_threshold)
         elif clipping_type == 'value':
             torch.nn.utils.clip_grad_value_(parameters, clip_value=clipping_threshold)
         else:
             raise ValueError(f"clipping_type must be 'adaptive', 'norm', or 'value' not {clipping_type} ")
 
+    return None
+
 
 def _apply_agc(
     parameters: Union[torch.Tensor, Iterable[torch.Tensor]],
@@ -122,24 +128,51 @@ class GradientClipping(Algorithm):
             to (for 'value'), what values to clip the gradient norms to (for 'norm'), and
             threshold by which if grad_norm / weight_norm is greater than this threshold then
             scale gradients by this threshold * (weight_norm / grad_norm) (for 'adaptive').
+        clipping_frequency_window (int, optional): Number of steps to use for calculating
+            the rolling average of clipping frequency. Only used for 'norm' clipping type.
+            Defaults to 100.
     """
 
-    def __init__(self, clipping_type: str, clipping_threshold: float):
+    def __init__(self, clipping_type: str, clipping_threshold: float, clipping_frequency_window: int = 100):
         self.clipping_type = clipping_type
         self.clipping_threshold = clipping_threshold
+        self.clipping_frequency_window = clipping_frequency_window
+        self._clipping_history = []
 
     def match(self, event: Event, state: State) -> bool:
         return event in [Event.INIT, Event.AFTER_TRAIN_BATCH]
 
     def apply(self, event: Event, state: State, logger: Logger) -> Optional[int]:
         if event == Event.AFTER_TRAIN_BATCH:
-            apply_gradient_clipping(
+            maybe_grad_norm = apply_gradient_clipping(
                 model=state.model,
                 clipping_type=self.clipping_type,
                 clipping_threshold=self.clipping_threshold,
                 fsdp_enabled=state.fsdp_config_version == 1,
             )
 
+            if self.clipping_type == 'norm':
+                if maybe_grad_norm is None:
+                    raise RuntimeError("Expected gradient norm to be returned for 'norm' clipping type, but got None")
+
+                grad_norm = maybe_grad_norm.item()
+
+                # Log the gradient norm before clipping
+                logger.log_metrics({'gradient_norm/unclipped_magnitude': grad_norm})
+
+                # Log whether clipping was applied
+                clipping_applied = grad_norm > self.clipping_threshold
+                logger.log_metrics({'gradient_norm/clipped': float(clipping_applied)})
+
+                # Track clipping frequency
+                self._clipping_history.append(float(clipping_applied))
+                # Keep only last N steps for frequency calculation
+                if len(self._clipping_history) > self.clipping_frequency_window:
+                    self._clipping_history.pop(0)
+
+                clipping_frequency = sum(self._clipping_history) / len(self._clipping_history)
+                logger.log_metrics({'gradient_norm/clipping_frequency': clipping_frequency})
+
 
 def _get_clipped_gradient_coeff(weights: torch.Tensor, grad: torch.Tensor, clipping_threshold: float = 0.01):
     """Clips all gradients in model based on ratio of gradient norms to parameter norms.