diff --git a/src/spatialdata_plot/pl/render.py b/src/spatialdata_plot/pl/render.py
index f0e7a1e8..af312595 100644
--- a/src/spatialdata_plot/pl/render.py
+++ b/src/spatialdata_plot/pl/render.py
@@ -375,7 +375,7 @@ def _render_shapes(
     # When groups are specified, filter out non-matching elements by default.
     # Only show non-matching elements if the user explicitly sets na_color.
     _na = render_params.cmap_params.na_color
-    if groups is not None and color_source_vector is not None and (_na.default_color_set or _na.alpha == "00"):
+    if groups is not None and color_source_vector is not None and (_na.default_color_set or _na.is_fully_transparent()):
         keep, color_source_vector, color_vector = _filter_groups_transparent_na(
             groups, color_source_vector, color_vector
         )
@@ -530,6 +530,8 @@ def _render_shapes(
 
         agg, color_span = _apply_ds_norm(agg, norm)
         na_color_hex = _hex_no_alpha(render_params.cmap_params.na_color.get_hex())
+        if render_params.cmap_params.na_color.is_fully_transparent():
+            nan_agg = None
         color_key = _build_color_key(
             transformed_element,
             col_for_color,
@@ -832,7 +834,7 @@ def _render_points(
     # When groups are specified, filter out non-matching elements by default.
     # Only show non-matching elements if the user explicitly sets na_color.
     _na = render_params.cmap_params.na_color
-    if groups is not None and color_source_vector is not None and (_na.default_color_set or _na.alpha == "00"):
+    if groups is not None and color_source_vector is not None and (_na.default_color_set or _na.is_fully_transparent()):
         keep, color_source_vector, color_vector = _filter_groups_transparent_na(
             groups, color_source_vector, color_vector
         )
@@ -925,6 +927,8 @@ def _render_points(
 
         agg, color_span = _apply_ds_norm(agg, norm)
         na_color_hex = _hex_no_alpha(render_params.cmap_params.na_color.get_hex())
+        if render_params.cmap_params.na_color.is_fully_transparent():
+            nan_agg = None
         color_key = _build_color_key(
             transformed_element,
             col_for_color,
@@ -1385,7 +1389,7 @@ def _render_labels(
         groups is not None
         and categorical
         and color_source_vector is not None
-        and (_na.default_color_set or _na.alpha == "00")
+        and (_na.default_color_set or _na.is_fully_transparent())
     ):
         keep_vec = color_source_vector.isin(groups)
         matching_ids = instance_id[keep_vec]
diff --git a/src/spatialdata_plot/pl/render_params.py b/src/spatialdata_plot/pl/render_params.py
index 24ba1d4f..8f7977be 100644
--- a/src/spatialdata_plot/pl/render_params.py
+++ b/src/spatialdata_plot/pl/render_params.py
@@ -138,6 +138,10 @@ def alpha_is_user_defined(self) -> bool:
         """Get whether an alpha was set during object creation."""
         return self.user_defined_alpha
 
+    def is_fully_transparent(self) -> bool:
+        """Check whether this color is fully transparent (alpha == 0)."""
+        return self.alpha == "00"
+
 
 @dataclass
 class CmapParams:
diff --git a/tests/pl/test_render_points.py b/tests/pl/test_render_points.py
index 0cd7a40d..eba38fbd 100644
--- a/tests/pl/test_render_points.py
+++ b/tests/pl/test_render_points.py
@@ -913,3 +913,22 @@ def test_shade_categorical_cmap_used_when_no_color_key():
     shaded_blue = _ds_shade_categorical(agg, None, np.array(["#0000ff"] * 100), alpha=1.0)
     # Different color_vector[0] values should produce different shaded output
     assert not np.array_equal(np.asarray(shaded_red), np.asarray(shaded_blue))
+
+
+def test_datashader_na_color_none_no_nan_overlay_points(sdata_blobs: SpatialData):
+    """NaN overlay is skipped when na_color is fully transparent (#565)."""
+    pts = sdata_blobs.points["blobs_points"].compute()
+    n = len(pts)
+    values = np.full(n, np.nan)
+    values[: n // 2] = np.random.default_rng(0).uniform(0, 100, n // 2)
+    pts["val"] = values
+    sdata_blobs.points["blobs_points"] = PointsModel.parse(pts)
+
+    fig, ax = plt.subplots()
+    sdata_blobs.pl.render_points("blobs_points", color="val", na_color=None, method="datashader").pl.show(ax=ax)
+
+    assert len(ax.get_images()) == 1, (
+        f"Expected 1 image (no NaN overlay), got {len(ax.get_images())}; "
+        "datashader is still rendering an opaque NaN overlay despite na_color=None"
+    )
+    plt.close(fig)
diff --git a/tests/pl/test_render_shapes.py b/tests/pl/test_render_shapes.py
index 409d4487..87fa2949 100644
--- a/tests/pl/test_render_shapes.py
+++ b/tests/pl/test_render_shapes.py
@@ -1210,3 +1210,24 @@ def test_datashader_colorbar_range_matches_data(sdata_blobs: SpatialData):
     )
     assert cbar_vmin >= data_min * 0.99 - 0.01, f"Colorbar min ({cbar_vmin:.2f}) is below data min ({data_min:.2f})"
     plt.close(fig)
+
+
+@pytest.mark.parametrize(
+    ("na_color", "expected_images"),
+    [(None, 1), ("red", 2)],
+    ids=["transparent_skips_overlay", "opaque_renders_overlay"],
+)
+def test_datashader_na_color_nan_overlay(sdata_blobs: SpatialData, na_color: str | None, expected_images: int):
+    """NaN overlay is rendered only when na_color is opaque (#565)."""
+    n = len(sdata_blobs.shapes["blobs_circles"])
+    values = np.full(n, np.nan)
+    values[: n // 2] = np.random.default_rng(0).uniform(0, 100, n // 2)
+    sdata_blobs.shapes["blobs_circles"]["val"] = values
+
+    fig, ax = plt.subplots()
+    sdata_blobs.pl.render_shapes("blobs_circles", color="val", na_color=na_color, method="datashader").pl.show(ax=ax)
+
+    assert len(ax.get_images()) == expected_images, (
+        f"Expected {expected_images} image(s), got {len(ax.get_images())} for na_color={na_color!r}"
+    )
+    plt.close(fig)