diff --git a/ngff_zarr/to_ngff_zarr.py b/ngff_zarr/to_ngff_zarr.py
index 90a85d67..b966c05e 100644
--- a/ngff_zarr/to_ngff_zarr.py
+++ b/ngff_zarr/to_ngff_zarr.py
@@ -116,17 +116,22 @@ def _write_with_tensorstore(
     zarr_format,
     dimension_names=None,
     internal_chunk_shape=None,
+    full_array_shape=None,
+    create_dataset=True,
 ) -> None:
     """Write array using tensorstore backend"""
     import tensorstore as ts
 
+    # Use full array shape if provided, otherwise use the region array shape
+    dataset_shape = full_array_shape if full_array_shape is not None else array.shape
+
     spec = {
         "kvstore": {
             "driver": "file",
             "path": store_path,
         },
         "metadata": {
-            "shape": array.shape,
+            "shape": dataset_shape,
         },
     }
     if zarr_format == 2:
@@ -152,8 +157,21 @@ def _write_with_tensorstore(
             ]
     else:
         raise ValueError(f"Unsupported zarr format: {zarr_format}")
-    dataset = ts.open(spec, create=True, dtype=array.dtype).result()
-    dataset[...] = array[region]
+
+    # Try to open existing dataset first, create only if needed
+    try:
+        if create_dataset:
+            dataset = ts.open(spec, create=True, dtype=array.dtype).result()
+        else:
+            dataset = ts.open(spec, create=False, dtype=array.dtype).result()
+    except Exception as e:
+        if "ALREADY_EXISTS" in str(e) and create_dataset:
+            # Dataset already exists, open it without creating
+            dataset = ts.open(spec, create=False, dtype=array.dtype).result()
+        else:
+            raise
+
+    dataset[region] = array
 
 
 def _validate_ngff_parameters(
@@ -317,6 +335,8 @@ def _write_array_with_tensorstore(
     zarr_format: int,
     dimension_names: Optional[Tuple[str, ...]],
     region: Tuple[slice, ...],
+    full_array_shape: Optional[Tuple[int, ...]] = None,
+    create_dataset: bool = True,
     **kwargs,
 ) -> None:
     """Write an array using the TensorStore backend."""
@@ -329,6 +349,8 @@ def _write_array_with_tensorstore(
             chunks,
             zarr_format=zarr_format,
             dimension_names=dimension_names,
+            full_array_shape=full_array_shape,
+            create_dataset=create_dataset,
             **kwargs,
         )
     else:  # Sharding
@@ -340,6 +362,8 @@ def _write_array_with_tensorstore(
             zarr_format=zarr_format,
             dimension_names=dimension_names,
             internal_chunk_shape=internal_chunk_shape,
+            full_array_shape=full_array_shape,
+            create_dataset=create_dataset,
             **kwargs,
         )
 
@@ -472,6 +496,8 @@ def _handle_large_array_writing(
                 zarr_format,
                 dimension_names,
                 region,
+                full_array_shape=arr.shape,
+                create_dataset=(region_index == 0),  # Only create on first region
                 **kwargs,
             )
         else:
@@ -854,6 +880,8 @@ def to_ngff_zarr(
                     zarr_format,
                     dimension_names,
                     region,
+                    full_array_shape=arr.shape,
+                    create_dataset=True,  # Always create for small arrays
                     **kwargs,
                 )
             else:
diff --git a/pixi.lock b/pixi.lock
index 8e070ca0..72e9afa0 100644
--- a/pixi.lock
+++ b/pixi.lock
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   size: 17887
   timestamp: 1741969612334
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+- pypi: https://files.pythonhosted.org/packages/bb/61/78c7b3851add1481b048b5fdc29067397a1784e2910592bc81bb3f608635/fsspec-2025.5.1-py3-none-any.whl
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+  version: 2025.5.1
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   - adlfs ; extra == 'adl'
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+- conda: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.12-pyhd8ed1ab_0.conda
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+  md5: 84463b10c1eb198541cd54125c7efe90
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   - ukkonen
   license: MIT
   license_family: MIT
   purls:
-  - pkg:pypi/identify?source=hash-mapping
-  size: 79057
-  timestamp: 1745098917031
+  - pkg:pypi/identify?source=compressed-mapping
+  size: 78926
+  timestamp: 1748049754416
 - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda
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-  sha256: fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566
+  version: 4.24.0
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@@ -2721,7 +2721,7 @@ packages:
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   - uri-template ; extra == 'format-nongpl'
   - webcolors>=24.6.0 ; extra == 'format-nongpl'
-  requires_python: '>=3.8'
+  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/01/0e/b27cdbaccf30b890c40ed1da9fd4a3593a5cf94dae54fb34f8a4b74fcd3f/jsonschema_specifications-2025.4.1-py3-none-any.whl
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-  md5: aeb98fdeb2e8f25d43ef71fbacbeec80
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda
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+  md5: c7e925f37e3b40d893459e625f6a53f1
   depends:
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-  - libgcc-ng >=12
+  - libgcc >=13
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-  license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libmpdec-4.0.0-h68df207_0.conda
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-  md5: 5a03ba481cb547e6f31a1d81ebc5e319
+  size: 91183
+  timestamp: 1748393666725
+- conda: https://conda.anaconda.org/conda-forge/linux-aarch64/libmpdec-4.0.0-h86ecc28_0.conda
+  sha256: ef8697f934c80b347bf9d7ed45650928079e303bad01bd064995b0e3166d6e7a
+  md5: 78cfed3f76d6f3f279736789d319af76
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+  - libgcc >=13
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   depends:
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@@ -3468,7 +3464,7 @@ packages:
   timestamp: 1738196177597
 - pypi: ./
   name: ngff-zarr
-  version: 0.13.1
+  version: 0.13.2
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   requires_dist:
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@@ -4991,10 +4987,10 @@ packages:
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   - typing-extensions>=4.0.0,<5.0 ; python_full_version < '3.11'
   requires_python: '>=3.8.0'
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+- pypi: https://files.pythonhosted.org/packages/31/f6/5fc0574af5379606ffd57a4b68ed88f9b415eb222047fe023aefcc00a648/rich_argparse-1.7.1-py3-none-any.whl
   name: rich-argparse
-  version: 1.7.0
-  sha256: b8ec8943588e9731967f4f97b735b03dc127c416f480a083060433a97baf2fd3
+  version: 1.7.1
+  sha256: a8650b42e4a4ff72127837632fba6b7da40784842f08d7395eb67a9cbd7b4bf9
   requires_dist:
   - rich>=11.0.0
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@@ -5527,10 +5523,10 @@ packages:
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+- pypi: https://files.pythonhosted.org/packages/be/5b/6e79401acf8a2f10a702d935d940f5445ba6cef280d5bfb881a9b900d7bc/tifffile-2025.5.26-py3-none-any.whl
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-  version: 2025.5.21
-  sha256: de06c487773796bf3ed9503525530f8d039c2db6e4bdafd4118b20ee8d3a338e
+  version: 2025.5.26
+  sha256: c316c9191c935d3a1fd7617e7af1d4367dfe2d8bd74d98d147bc21c074cc3ea9
   requires_dist:
   - numpy
   - imagecodecs>=2024.12.30 ; extra == 'codecs'
@@ -5565,51 +5561,54 @@ packages:
   - xarray ; extra == 'test'
   - zarr>=3 ; extra == 'test'
   requires_python: '>=3.11'
-- conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd72426e_102.conda
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+  md5: a0116df4f4ed05c303811a837d5b39d8
   depends:
-  - libgcc-ng >=12
-  - libzlib >=1.2.13,<2.0.0a0
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libzlib >=1.3.1,<2.0a0
   license: TCL
   license_family: BSD
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-  - libzlib >=1.2.13,<2.0.0a0
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+  - __osx >=10.13
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   license_family: BSD
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+  md5: 7362396c170252e7b7b0c8fb37fe9c78
   depends:
-  - libzlib >=1.2.13,<2.0.0a0
+  - __osx >=11.0
+  - libzlib >=1.3.1,<2.0a0
   license: TCL
   license_family: BSD
   purls: []
-  size: 3145523
-  timestamp: 1699202432999
-- conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h5226925_1.conda
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-  md5: fc048363eb8f03cd1737600a5d08aafe
+  size: 3125538
+  timestamp: 1748388189063
+- conda: https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h2c6b04d_2.conda
+  sha256: e3614b0eb4abcc70d98eae159db59d9b4059ed743ef402081151a948dce95896
+  md5: ebd0e761de9aa879a51d22cc721bd095
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   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
@@ -5617,8 +5616,8 @@ packages:
   license: TCL
   license_family: BSD
   purls: []
-  size: 3503410
-  timestamp: 1699202577803
+  size: 3466348
+  timestamp: 1748388121356
 - pypi: https://files.pythonhosted.org/packages/03/98/eb27cc78ad3af8e302c9d8ff4977f5026676e130d28dd7578132a457170c/toolz-1.0.0-py3-none-any.whl
   name: toolz
   version: 1.0.0
diff --git a/test/test_to_ngff_zarr_tensorstore.py b/test/test_to_ngff_zarr_tensorstore.py
index c24b6a51..b6ff57b4 100644
--- a/test/test_to_ngff_zarr_tensorstore.py
+++ b/test/test_to_ngff_zarr_tensorstore.py
@@ -1,9 +1,14 @@
 import tempfile
 from packaging import version
+import logging
+import pathlib
+import time
+import uuid
 
 import pytest
 import zarr
 from dask_image import imread
+import numpy as np
 
 from ngff_zarr import (
     Methods,
@@ -76,3 +81,65 @@ def test_large_image_serialization(input_images):
     with tempfile.TemporaryDirectory() as tmpdir:
         to_ngff_zarr(tmpdir, multiscales, use_tensorstore=True)
         config.memory_target = default_mem_target
+
+
+def test_tensorstore_already_exists_failure():
+    """
+    Demonstrates the ALREADY_EXISTS failure with use_tensorstore=True during Zarr writing.
+    This failure occurs with large data sizes that trigger regional writing.
+    """
+    pytest.importorskip("tensorstore")
+
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
+    logger = logging.getLogger("TensorstoreAlreadyExistsTest")
+
+    large_shape = (512, 512, 512)  # Large shape for TensorStore test
+    input_dtype = np.float32
+    pixel_size = 1.3
+
+    input_array = np.random.rand(*large_shape).astype(input_dtype)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        test_output_dir = pathlib.Path(tmpdir)
+        scan_id = str(uuid.uuid4())[:8]
+        zarr_path = test_output_dir / f"{scan_id}.zarr"
+        logger.info(f"Test output directory: {test_output_dir}")
+
+        # 1. Create NGFF Image
+        logger.info("  Creating NGFF image object...")
+        image = to_ngff_image(
+            input_array,
+            dims=("z", "y", "x"),
+            scale={"z": pixel_size, "y": pixel_size, "x": pixel_size},
+            axes_units={"z": "micrometer", "y": "micrometer", "x": "micrometer"},
+        )
+
+        # 2. Create Multiscales (Using a method known to work)
+        logger.info("  Creating multiscales...")
+        start_time_multiscale = time.time()
+        multiscales = to_multiscales(
+            image,
+            method=Methods.DASK_IMAGE_GAUSSIAN,
+            chunks=None,  # Default chunking
+            cache=False,
+        )
+        end_time_multiscale = time.time()
+        logger.info(
+            f"  Multiscales created in {end_time_multiscale - start_time_multiscale:.2f} seconds."
+        )
+
+        # 3. Write Zarr (This was failing)
+        logger.info("  Writing NGFF Zarr with TensorStore...")
+        start_time_write = time.time()
+        to_ngff_zarr(
+            store=zarr_path,
+            multiscales=multiscales,
+            use_tensorstore=True,  # Enable TensorStore
+        )
+        end_time_write = time.time()
+        logger.info(
+            f"  Zarr written in {end_time_write - start_time_write:.2f} seconds (UNEXPECTED SUCCESS)."
+        )
\ No newline at end of file