diff --git a/dataclass_array/ops.py b/dataclass_array/ops.py
index fb13e22..da4f446 100644
--- a/dataclass_array/ops.py
+++ b/dataclass_array/ops.py
@@ -93,6 +93,7 @@ def stack(
     axis: int = 0,
 ) -> DcT:  # _DcT['len(arrays) *shape']:
   """Stack dataclasses together."""
+  arrays = list(arrays)
   return _ops_base(
       arrays,
       axis=axis,
@@ -108,6 +109,7 @@ def stack(
 
 def concat(arrays: Iterable[DcT], *, axis: int = 0) -> DcT:
   """Concatenate dataclasses together."""
+  arrays = list(arrays)
   return _ops_base(
       arrays,
       axis=axis,
diff --git a/dataclass_array/ops_iterable_test.py b/dataclass_array/ops_iterable_test.py
new file mode 100644
index 0000000..48d8ff9
--- /dev/null
+++ b/dataclass_array/ops_iterable_test.py
@@ -0,0 +1,88 @@
+# Copyright 2026 The dataclass_array Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Stack and concat must consume iterable inputs only once."""
+
+from __future__ import annotations
+
+import dataclass_array as dca
+from dataclass_array.typing import FloatArray
+from etils import enp
+import numpy as np
+import pytest
+
+enable_tf_np_mode = enp.testing.set_tnp
+
+
+class Point(dca.DataclassArray):
+  position: FloatArray['*shape 3']
+  mass: FloatArray['*shape']
+
+
+class Samples(dca.DataclassArray):
+  point: Point
+  weight: FloatArray['*shape']
+  label: str = 'sample'
+
+
+def _samples(xnp):
+  values = np.arange(24, dtype=np.float32).reshape(2, 4, 3)
+  return [
+      Samples(
+          point=Point(position=values + i, mass=values[..., 0] + i),
+          weight=values[..., 1] + i,
+      ).as_xnp(xnp)
+      for i in range(3)
+  ]
+
+
+@enp.testing.parametrize_xnp()
+@pytest.mark.parametrize('operation', [dca.stack, dca.concat])
+@pytest.mark.parametrize('axis', [0, 1])
+@pytest.mark.parametrize('iterator_kind', ['iterator', 'generator'])
+def test_single_pass_inputs_match_lists(xnp, operation, axis, iterator_kind):
+  arrays = _samples(xnp)
+  expected = operation(arrays, axis=axis)
+  values = (
+      iter(arrays)
+      if iterator_kind == 'iterator'
+      else (array for array in arrays)
+  )
+  actual = operation(values, axis=axis)
+  dca.testing.assert_array_equal(actual, expected)
+  assert actual.shape == expected.shape
+  assert actual.xnp is expected.xnp
+  assert actual.label == 'sample'
+
+
+@enp.testing.parametrize_xnp()
+@pytest.mark.parametrize('operation', [dca.stack, dca.concat])
+def test_iterable_is_materialized_once(xnp, operation):
+  arrays = _samples(xnp)
+
+  class SinglePass:
+
+    def __init__(self):
+      self.iterations = 0
+
+    def __iter__(self):
+      self.iterations += 1
+      if self.iterations > 1:
+        raise AssertionError('Input iterable traversed more than once')
+      yield from arrays
+
+  values = SinglePass()
+  actual = operation(values)
+  dca.testing.assert_array_equal(actual, operation(arrays))
+  assert values.iterations == 1