diff --git a/pymc_marketing/mlflow.py b/pymc_marketing/mlflow.py
index 8fe0ffb6..60c7ffcc 100644
--- a/pymc_marketing/mlflow.py
+++ b/pymc_marketing/mlflow.py
@@ -138,6 +138,7 @@ def log_arviz_summary(
     idata: az.InferenceData,
     path: str | Path,
     var_names: list[str] | None = None,
+    **summary_kwargs,
 ) -> None:
     """Log the ArviZ summary as an artifact on MLflow.
 
@@ -152,9 +153,11 @@ def log_arviz_summary(
     var_names : list[str], optional
         The names of the variables to include in the summary. Default is
         all the variables in the InferenceData object.
+    summary_kwargs : dict
+        Additional keyword arguments to pass to `az.summary`.
 
     """
-    df_summary = az.summary(idata, var_names=var_names)
+    df_summary = az.summary(idata, var_names=var_names, **summary_kwargs)
     df_summary.to_html(path)
     mlflow.log_artifact(str(path))
     os.remove(path)
@@ -394,6 +397,7 @@ def autolog(
     log_datasets: bool = True,
     log_model_info: bool = True,
     summary_var_names: list[str] | None = None,
+    arviz_summary_kwargs: dict | None = None,
     log_mmm: bool = True,
     disable: bool = False,
     silent: bool = False,
@@ -417,6 +421,8 @@ def autolog(
     summary_var_names : list[str], optional
         The names of the variables to include in the ArviZ summary. Default is
         all the variables in the InferenceData object.
+    arviz_summary_kwargs : dict, optional
+        Additional keyword arguments to pass to `az.summary`.
     log_mmm : bool, optional
         Whether to log PyMC-Marketing MMM models. Default is True.
     disable : bool, optional
@@ -500,6 +506,8 @@ def autolog(
 
     """
 
+    arviz_summary_kwargs = arviz_summary_kwargs or {}
+
     def patch_sample(sample):
         @wraps(sample)
         def new_sample(*args, **kwargs):
@@ -510,7 +518,12 @@ def new_sample(*args, **kwargs):
 
             if log_sampler_info:
                 log_sample_diagnostics(idata)
-                log_arviz_summary(idata, "summary.html", var_names=summary_var_names)
+                log_arviz_summary(
+                    idata,
+                    "summary.html",
+                    var_names=summary_var_names,
+                    **arviz_summary_kwargs,
+                )
 
             model = pm.modelcontext(kwargs.get("model"))
             if log_model_info: