diff --git a/docs/gallery/advanced_io/streaming.py b/docs/gallery/advanced_io/streaming.py
index 4b03a9f2c..23c291db6 100644
--- a/docs/gallery/advanced_io/streaming.py
+++ b/docs/gallery/advanced_io/streaming.py
@@ -11,7 +11,7 @@
 using the dandi API library.
 
 Getting the location of the file on DANDI
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+-----------------------------------------
 
 The :py:class:`~dandi.dandiapi.DandiAPIClient` can be used to get the S3 URL of any NWB file stored in the DANDI
 Archive. If you have not already, install the latest release of the ``dandi`` package.
@@ -34,36 +34,8 @@
        s3_url = asset.get_content_url(follow_redirects=1, strip_query=True)
 
 
-Streaming Method 1: ROS3
-~~~~~~~~~~~~~~~~~~~~~~~~
-ROS3 is one of the supported methods for reading data from a remote store. ROS3 stands for "read only S3" and is a
-driver created by the HDF5 Group that allows HDF5 to read HDF5 files stored remotely in s3 buckets. Using this method
-requires that your HDF5 library is installed with the ROS3 driver enabled. This is not the default configuration,
-so you will need to make sure you install the right version of ``h5py`` that has this advanced configuration enabled.
-You can install HDF5 with the ROS3 driver from `conda-forge <https://conda-forge.org/>`_ using ``conda``. You may
-first need to uninstall a currently installed version of ``h5py``.
-
-.. code-block:: bash
-
-   pip uninstall h5py
-   conda install -c conda-forge "h5py>=3.2"
-
-Now instantiate a :py:class:`~pynwb.NWBHDF5IO` object with the S3 URL and specify the driver as "ros3". This
-will download metadata about the file from the S3 bucket to memory. The values of datasets are accessed lazily,
-just like when reading an NWB file stored locally. So, slicing into a dataset will require additional time to
-download the sliced data (and only the sliced data) to memory.
-
-.. code-block:: python
-
-   from pynwb import NWBHDF5IO
-
-   with NWBHDF5IO(s3_url, mode='r', load_namespaces=True, driver='ros3') as io:
-       nwbfile = io.read()
-       print(nwbfile)
-       print(nwbfile.acquisition['lick_times'].time_series['lick_left_times'].data[:])
-
-Streaming Method 2: fsspec
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Streaming Method 1: fsspec
+--------------------------
 fsspec is another data streaming approach that is quite flexible and has several performance advantages. This library
 creates a virtual filesystem for remote stores. With this approach, a virtual file is created for the file and
 the virtual filesystem layer takes care of requesting data from the S3 bucket whenever data is
@@ -113,6 +85,49 @@
 The S3 backend, in particular, may provide additional functionality for accessing data on DANDI. See the
 `fsspec documentation on known implementations <https://filesystem-spec.readthedocs.io/en/latest/api.html?highlight=S3#other-known-implementations>`_
 for a full updated list of supported store formats.
+
+Streaming Method 2: ROS3
+------------------------
+ROS3 is one of the supported methods for reading data from a remote store. ROS3 stands for "read only S3" and is a
+driver created by the HDF5 Group that allows HDF5 to read HDF5 files stored remotely in s3 buckets. Using this method
+requires that your HDF5 library is installed with the ROS3 driver enabled. This is not the default configuration,
+so you will need to make sure you install the right version of ``h5py`` that has this advanced configuration enabled.
+You can install HDF5 with the ROS3 driver from `conda-forge <https://conda-forge.org/>`_ using ``conda``. You may
+first need to uninstall a currently installed version of ``h5py``.
+
+.. code-block:: bash
+
+   pip uninstall h5py
+   conda install -c conda-forge "h5py>=3.2"
+
+Now instantiate a :py:class:`~pynwb.NWBHDF5IO` object with the S3 URL and specify the driver as "ros3". This
+will download metadata about the file from the S3 bucket to memory. The values of datasets are accessed lazily,
+just like when reading an NWB file stored locally. So, slicing into a dataset will require additional time to
+download the sliced data (and only the sliced data) to memory.
+
+.. code-block:: python
+
+   from pynwb import NWBHDF5IO
+
+   with NWBHDF5IO(s3_url, mode='r', load_namespaces=True, driver='ros3') as io:
+       nwbfile = io.read()
+       print(nwbfile)
+       print(nwbfile.acquisition['lick_times'].time_series['lick_left_times'].data[:])
+
+Which streaming method to choose?
+---------------------------------
+
+fsspec has many advantages over ros3:
+
+1. fsspec is easier to install
+2. fsspec supports caching, which will dramatically speed up repeated requests for the
+   same region of data
+3. fsspec automatically retries when s3 fails to return.
+4. fsspec works with other storage backends and
+5. fsspec works with other types of files.
+6. In our hands, fsspec is faster out-of-the-box.
+
+For these reasons, we would recommend use fsspec for most Python users.
 '''
 
 # sphinx_gallery_thumbnail_path = 'figures/gallery_thumbnails_streaming.png'