diff --git a/docs/tutorials/barycenter/000_Sinkhorn_Barycenters.ipynb b/docs/tutorials/barycenter/000_Sinkhorn_Barycenters.ipynb
index fa6bab8ce..e216e3f4e 100644
--- a/docs/tutorials/barycenter/000_Sinkhorn_Barycenters.ipynb
+++ b/docs/tutorials/barycenter/000_Sinkhorn_Barycenters.ipynb
@@ -17,9 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import nilearn\n",
     "from nilearn import datasets, image, plotting\n",
-    "from nilearn.image import get_data\n",
     "\n",
     "import jax\n",
     "import jax.numpy as jnp\n",
@@ -38,7 +36,7 @@
     "id": "02VJX2uXYHDX"
    },
    "source": [
-    "## Import neuroimaging data using `nilearn`.\n",
+    "## Import neuroimaging data using `nilearn`\n",
     "\n",
     "We recover a few MRI data points..."
    ]
@@ -157,7 +155,7 @@
     }
    ],
    "source": [
-    "a = jnp.array(get_data(gm_imgs)).transpose((3, 0, 1, 2))\n",
+    "a = jnp.array(image.get_data(gm_imgs)).transpose((3, 0, 1, 2))\n",
     "grid_size = a.shape[1:4]\n",
     "a = a.reshape((n_subjects, -1)) + 1e-2\n",
     "a = a / np.sum(a, axis=1)[:, np.newaxis]\n",
@@ -320,9 +318,7 @@
    ],
    "source": [
     "def data_to_nii(x):\n",
-    "    return nilearn.image.new_img_like(\n",
-    "        gm_imgs[0], data=np.array(x.reshape(grid_size))\n",
-    "    )\n",
+    "    return image.new_img_like(gm_imgs[0], data=np.array(x.reshape(grid_size)))\n",
     "\n",
     "\n",
     "plotting.plot_epi(data_to_nii(barycenter.histogram))\n",
diff --git a/docs/tutorials/barycenter/200_gmm_pair_demo.ipynb b/docs/tutorials/barycenter/200_gmm_pair_demo.ipynb
index 6f5af1f92..190a524c4 100644
--- a/docs/tutorials/barycenter/200_gmm_pair_demo.ipynb
+++ b/docs/tutorials/barycenter/200_gmm_pair_demo.ipynb
@@ -69,7 +69,6 @@
     "    fit_gmm_pair,\n",
     "    gaussian_mixture,\n",
     "    gaussian_mixture_pair,\n",
-    "    probabilities,\n",
     ")"
    ]
   },
diff --git a/docs/tutorials/geometry/100_grid.ipynb b/docs/tutorials/geometry/100_grid.ipynb
index 72d3c70bf..b1b83ccfe 100644
--- a/docs/tutorials/geometry/100_grid.ipynb
+++ b/docs/tutorials/geometry/100_grid.ipynb
@@ -241,11 +241,15 @@
     "class MyCost(costs.CostFn):\n",
     "    \"\"\"An unusual cost function.\"\"\"\n",
     "\n",
-    "    def norm(self, x):\n",
+    "    def norm(self, x: jnp.ndarray) -> jnp.ndarray:\n",
     "        return jnp.sum(x**3 + jnp.cos(x) ** 2, axis=-1)\n",
     "\n",
-    "    def pairwise(self, x, y):\n",
-    "        return -jnp.sum(jnp.sin(x + 1) * jnp.sin(y)) * 2"
+    "    def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:\n",
+    "        return (\n",
+    "            self.norm(x)\n",
+    "            + self.norm(y)\n",
+    "            - jnp.sum(jnp.sin(x + 1) * jnp.sin(y)) * 2\n",
+    "        )"
    ]
   },
   {
diff --git a/docs/tutorials/linear/000_One_Sinkhorn.ipynb b/docs/tutorials/linear/000_One_Sinkhorn.ipynb
index e1bcb9f90..4acd7305a 100644
--- a/docs/tutorials/linear/000_One_Sinkhorn.ipynb
+++ b/docs/tutorials/linear/000_One_Sinkhorn.ipynb
@@ -19,7 +19,6 @@
    },
    "outputs": [],
    "source": [
-    "import functools\n",
     "import time\n",
     "\n",
     "import jax\n",
@@ -27,11 +26,9 @@
     "\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "import ott\n",
-    "from ott import problems\n",
     "from ott.geometry import geometry, pointcloud\n",
     "from ott.solvers import linear\n",
-    "from ott.solvers.linear import acceleration, sinkhorn"
+    "from ott.solvers.linear import acceleration"
    ]
   },
   {
@@ -1087,7 +1084,7 @@
     "    plot_results(\n",
     "        out_scaling[i],\n",
     "        leg_scaling[i],\n",
-    "        title=rf\"Decay, $\\varepsilon$=\" + str(epsilon),\n",
+    "        title=r\"Decay, $\\varepsilon$=\" + str(epsilon),\n",
     "        xlabel=\"iterations\",\n",
     "        ylabel=\"error\",\n",
     "    )"
@@ -1276,7 +1273,7 @@
     "    plot_results(\n",
     "        out_mixed[i],\n",
     "        leg_mixed[i],\n",
-    "        title=rf\"Mixed strategy, $\\varepsilon$=\" + str(epsilon),\n",
+    "        title=r\"Mixed strategy, $\\varepsilon$=\" + str(epsilon),\n",
     "        xlabel=\"iterations\",\n",
     "        ylabel=\"error\",\n",
     "    )"
diff --git a/docs/tutorials/linear/100_OTT_&_POT.ipynb b/docs/tutorials/linear/100_OTT_&_POT.ipynb
index bf62bd3b6..af3983e37 100644
--- a/docs/tutorials/linear/100_OTT_&_POT.ipynb
+++ b/docs/tutorials/linear/100_OTT_&_POT.ipynb
@@ -23,21 +23,19 @@
    },
    "outputs": [],
    "source": [
-    "import timeit\n",
-    "\n",
     "import jax\n",
     "import jax.numpy as jnp\n",
     "import numpy as np\n",
     "import ot\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.rc(\"font\", size=20)\n",
     "import mpl_toolkits.axes_grid1\n",
     "\n",
     "from ott.geometry import pointcloud\n",
     "from ott.problems.linear import linear_problem\n",
-    "from ott.solvers.linear import sinkhorn"
+    "from ott.solvers.linear import sinkhorn\n",
+    "\n",
+    "plt.rc(\"font\", size=20)"
    ]
   },
   {
diff --git a/docs/tutorials/linear/200_sinkhorn_divergence_gradient_flow.ipynb b/docs/tutorials/linear/200_sinkhorn_divergence_gradient_flow.ipynb
index caea459a0..1fb201f6d 100644
--- a/docs/tutorials/linear/200_sinkhorn_divergence_gradient_flow.ipynb
+++ b/docs/tutorials/linear/200_sinkhorn_divergence_gradient_flow.ipynb
@@ -29,7 +29,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Any, Callable, Tuple\n",
+    "from typing import Any, Callable\n",
     "\n",
     "import jax\n",
     "import jax.numpy as jnp\n",
diff --git a/docs/tutorials/linear/600_mmsink.ipynb b/docs/tutorials/linear/600_mmsink.ipynb
index 634cbdf24..7fd05f4c1 100644
--- a/docs/tutorials/linear/600_mmsink.ipynb
+++ b/docs/tutorials/linear/600_mmsink.ipynb
@@ -15,8 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import math\n",
-    "from typing import List, Optional, Tuple\n",
+    "from typing import Optional\n",
     "\n",
     "import jax\n",
     "import jax.numpy as jnp\n",
diff --git a/docs/tutorials/misc/000_tracking_progress.ipynb b/docs/tutorials/misc/000_tracking_progress.ipynb
index 2fdfaeb96..ebe09f4bb 100644
--- a/docs/tutorials/misc/000_tracking_progress.ipynb
+++ b/docs/tutorials/misc/000_tracking_progress.ipynb
@@ -29,15 +29,12 @@
     "\n",
     "import jax\n",
     "import jax.numpy as jnp\n",
-    "import numpy as np\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
     "\n",
     "from ott import utils\n",
     "from ott.geometry import pointcloud\n",
     "from ott.problems.linear import linear_problem\n",
     "from ott.problems.quadratic import quadratic_problem\n",
-    "from ott.solvers import linear, quadratic\n",
+    "from ott.solvers import linear\n",
     "from ott.solvers.linear import sinkhorn, sinkhorn_lr\n",
     "from ott.solvers.quadratic import gromov_wasserstein"
    ]
diff --git a/docs/tutorials/misc/200_application_biology.ipynb b/docs/tutorials/misc/200_application_biology.ipynb
index 74c4cab96..2d6a8185b 100644
--- a/docs/tutorials/misc/200_application_biology.ipynb
+++ b/docs/tutorials/misc/200_application_biology.ipynb
@@ -262,8 +262,8 @@
     "for day in DAYS:\n",
     "    tmp = cell_distribution_ipsc[day].copy()\n",
     "    tmp[tmp >= 1e-2] = alpha_bins[0]\n",
-    "    tmp[np.logical_and(1e-2 > tmp, tmp >= 5e-4)] = alpha_bins[1]\n",
-    "    tmp[5e-4 > tmp] = alpha_bins[2]\n",
+    "    tmp[np.logical_and(tmp < 1e-2, tmp >= 5e-4)] = alpha_bins[1]\n",
+    "    tmp[tmp < 5e-4] = alpha_bins[2]\n",
     "    binned_cell_distribution_ipsc[day] = tmp"
    ]
   },
diff --git a/docs/tutorials/neural/000_neural_dual.ipynb b/docs/tutorials/neural/000_neural_dual.ipynb
index 83d31ddfe..43ececa4d 100644
--- a/docs/tutorials/neural/000_neural_dual.ipynb
+++ b/docs/tutorials/neural/000_neural_dual.ipynb
@@ -25,8 +25,6 @@
    "source": [
     "import jax\n",
     "import jax.numpy as jnp\n",
-    "import numpy as np\n",
-    "from torch.utils.data import DataLoader, IterableDataset\n",
     "\n",
     "import optax\n",
     "\n",
diff --git a/docs/tutorials/neural/100_icnn_inits.ipynb b/docs/tutorials/neural/100_icnn_inits.ipynb
index 32c3af85f..a1ff730ca 100644
--- a/docs/tutorials/neural/100_icnn_inits.ipynb
+++ b/docs/tutorials/neural/100_icnn_inits.ipynb
@@ -13,23 +13,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import jax\n",
-    "import jax.numpy as jnp\n",
-    "import numpy as np\n",
-    "\n",
     "import optax\n",
-    "\n",
-    "import matplotlib.pyplot as plt\n",
+    "from flax import linen as nn\n",
     "\n",
     "from ott import datasets\n",
-    "from ott.geometry import pointcloud\n",
     "from ott.neural.methods import neuraldual\n",
-    "from ott.neural.networks import icnn\n",
-    "from ott.tools import plot"
+    "from ott.neural.networks import icnn"
    ]
   },
   {
@@ -50,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,32 +104,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "# initialize models using identity initialization (default)\n",
-    "neural_f = icnn.ICNN(dim_hidden=[64, 64, 64, 64], dim_data=2)\n",
-    "neural_g = icnn.ICNN(dim_hidden=[64, 64, 64, 64], dim_data=2)"
+    "neural_f = icnn.ICNN(\n",
+    "    dim_hidden=[64, 64, 64, 64], dim_data=2, init_fn=nn.initializers.normal()\n",
+    ")\n",
+    "neural_g = icnn.ICNN(\n",
+    "    dim_hidden=[64, 64, 64, 64], dim_data=2, init_fn=nn.initializers.normal()\n",
+    ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/michal/Projects/dott/src/ott/neural/methods/neuraldual.py:154: UserWarning: Setting of ICNN and the positive weights setting of the `W2NeuralDual` are not consistent. Proceeding with the `W2NeuralDual` setting, with positive weights being True.\n",
+      "/Users/michal/Projects/ott/src/ott/neural/methods/neuraldual.py:155: UserWarning: Setting of ICNN and the positive weights setting of the `W2NeuralDual` are not consistent. Proceeding with the `W2NeuralDual` setting, with positive weights being True.\n",
       "  self.setup(\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "62abc21c2f8b47c09c328cb9ef44efd1",
+       "model_id": "a454b7953086409b8991dbeadb4628f7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -164,7 +161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -173,13 +170,13 @@
        "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -194,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -203,13 +200,13 @@
        "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -240,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -257,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -266,31 +263,33 @@
     "    dim_hidden=[64, 64, 64, 64],\n",
     "    dim_data=2,\n",
     "    gaussian_map_samples=(samples_source, samples_target),\n",
+    "    init_fn=nn.initializers.normal(),\n",
     ")\n",
     "neural_g = icnn.ICNN(\n",
     "    dim_hidden=[64, 64, 64, 64],\n",
     "    dim_data=2,\n",
     "    gaussian_map_samples=(samples_target, samples_source),\n",
+    "    init_fn=nn.initializers.normal(),\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/michal/Projects/dott/src/ott/neural/methods/neuraldual.py:154: UserWarning: Setting of ICNN and the positive weights setting of the `W2NeuralDual` are not consistent. Proceeding with the `W2NeuralDual` setting, with positive weights being True.\n",
+      "/Users/michal/Projects/ott/src/ott/neural/methods/neuraldual.py:155: UserWarning: Setting of ICNN and the positive weights setting of the `W2NeuralDual` are not consistent. Proceeding with the `W2NeuralDual` setting, with positive weights being True.\n",
       "  self.setup(\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fdf9e1aeda2b473c93d15d4815247286",
+       "model_id": "65751b1d1f9e41469dec9407f2982113",
        "version_major": 2,
        "version_minor": 0
       },
@@ -318,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -327,13 +326,13 @@
        "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -348,7 +347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "tags": []
    },
@@ -359,13 +358,13 @@
        "(<Figure size 640x480 with 1 Axes>, <Axes: >)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/docs/tutorials/neural/200_Monge_Gap.ipynb b/docs/tutorials/neural/200_Monge_Gap.ipynb
index 36f7c900f..5dac8c025 100644
--- a/docs/tutorials/neural/200_Monge_Gap.ipynb
+++ b/docs/tutorials/neural/200_Monge_Gap.ipynb
@@ -16,7 +16,7 @@
     "import dataclasses\n",
     "from collections.abc import Iterator, Mapping\n",
     "from types import MappingProxyType\n",
-    "from typing import Any, Dict, Literal, Optional, Tuple, Union\n",
+    "from typing import Any, Literal, Optional\n",
     "\n",
     "import jax\n",
     "import jax.numpy as jnp\n",
@@ -31,7 +31,6 @@
     "from ott.geometry import costs, pointcloud\n",
     "from ott.neural.methods import monge_gap\n",
     "from ott.neural.networks import potentials\n",
-    "from ott.solvers.linear import acceleration\n",
     "from ott.tools import sinkhorn_divergence"
    ]
   },
@@ -587,7 +586,7 @@
     }
    ],
    "source": [
-    "plot_fit_map(rf\"with $\\ell_2$ Monge gap\", out_nn_l2, logs_l2)"
+    "plot_fit_map(r\"with $\\ell_2$ Monge gap\", out_nn_l2, logs_l2)"
    ]
   },
   {
@@ -659,7 +658,7 @@
     }
    ],
    "source": [
-    "plot_fit_map(rf\"with $\\ell_2^2$ Monge gap\", out_nn_l22, logs_l22)"
+    "plot_fit_map(r\"with $\\ell_2^2$ Monge gap\", out_nn_l22, logs_l22)"
    ]
   }
  ],
diff --git a/docs/tutorials/quadratic/000_gromov_wasserstein.ipynb b/docs/tutorials/quadratic/000_gromov_wasserstein.ipynb
index 55e03c7d6..991c73e8f 100644
--- a/docs/tutorials/quadratic/000_gromov_wasserstein.ipynb
+++ b/docs/tutorials/quadratic/000_gromov_wasserstein.ipynb
@@ -24,9 +24,8 @@
     "import numpy as np\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "import mpl_toolkits.mplot3d.axes3d as p3\n",
     "from IPython import display\n",
-    "from matplotlib import animation, cm\n",
+    "from matplotlib import animation\n",
     "\n",
     "from ott.geometry import pointcloud\n",
     "from ott.problems.quadratic import quadratic_problem\n",
diff --git a/docs/utils.rst b/docs/utils.rst
index 797626784..c0ffcc7c5 100644
--- a/docs/utils.rst
+++ b/docs/utils.rst
@@ -11,3 +11,4 @@ function for :class:`~ott.solvers.linear.sinkhorn.Sinkhorn`.
 
     default_progress_fn
     tqdm_progress_fn
+    batched_vmap
diff --git a/pyproject.toml b/pyproject.toml
index 1ddcf2e26..2bec8e3b7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "ott-jax"
-description = "Optimal Transport Tools in JAX."
+description = "Optimal Transport Tools in JAX"
 requires-python = ">=3.9"
 dynamic = ["version"]
 readme = {file = "README.md", content-type = "text/markdown"}
@@ -17,6 +17,7 @@ dependencies = [
     "jaxopt>=0.8",
     "lineax>=0.0.5",
     "numpy>=1.20.0",
+    "typing_extensions; python_version <= '3.9'",
 ]
 keywords = [
     "optimal transport",
@@ -107,7 +108,7 @@ multi_line_output = 3
 sections = ["FUTURE", "STDLIB", "THIRDPARTY", "TEST", "NUMERIC", "NEURAL", "PLOTTING", "FIRSTPARTY", "LOCALFOLDER"]
 # also contains what we import in notebooks/tests
 known_neural = ["flax", "optax", "diffrax", "orbax"]
-known_numeric = ["numpy", "scipy", "jax", "flax", "optax", "jaxopt", "ot", "torch", "torchvision", "pandas", "sklearn", "tslearn"]
+known_numeric = ["numpy", "scipy", "jax", "chex", "flax", "optax", "jaxopt", "ot", "torch", "torchvision", "pandas", "sklearn", "tslearn"]
 known_test = ["_pytest", "pytest"]
 known_plotting = ["IPython", "matplotlib", "mpl_toolkits", "seaborn"]
 
@@ -120,12 +121,6 @@ markers = [
     "cpu: Mark tests as CPU only.",
     "fast: Mark tests as fast.",
 ]
-filterwarnings = [
-    "ignore:\\n*.*scipy.sparse array",
-    "ignore:jax.random.KeyArray is deprecated:DeprecationWarning",
-    "ignore:.*jax.config:DeprecationWarning",
-    "ignore:jax.core.Shape is deprecated:DeprecationWarning:chex",
-]
 
 [tool.coverage.run]
 branch = true
diff --git a/src/ott/geometry/costs.py b/src/ott/geometry/costs.py
index a9aeef3be..67ab58c79 100644
--- a/src/ott/geometry/costs.py
+++ b/src/ott/geometry/costs.py
@@ -14,7 +14,7 @@
 import abc
 import functools
 import math
-from typing import Any, Callable, Dict, Optional, Tuple, Union
+from typing import Any, Callable, Dict, Optional, Tuple
 
 import jax
 import jax.numpy as jnp
@@ -39,29 +39,16 @@
     "SoftDTW",
 ]
 
+# TODO(michalk8): norm check
 Func = Callable[[jnp.ndarray], float]
 
 
 @jtu.register_pytree_node_class
 class CostFn(abc.ABC):
-  """Base class for all costs.
-
-  Cost functions evaluate a function on a pair of inputs. For convenience,
-  that function is split into two norms -- evaluated on each input separately --
-  followed by a pairwise cost that involves both inputs, as in:
-
-  .. math::
-    c(x, y) = norm(x) + norm(y) + pairwise(x, y)
-
-  If the :attr:`norm` function is not implemented, that value is handled as
-  :math:`0`, and only :func:`pairwise` is used.
-  """
-
-  # no norm function created by default.
-  norm: Optional[Callable[[jnp.ndarray], Union[float, jnp.ndarray]]] = None
+  """Base class for all costs."""
 
   @abc.abstractmethod
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     """Compute cost between :math:`x` and :math:`y`.
 
     Args:
@@ -99,22 +86,6 @@ def _padder(cls, dim: int) -> jnp.ndarray:
     """
     return jnp.zeros((1, dim))
 
-  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
-    """Compute cost between :math:`x` and :math:`y`.
-
-    Args:
-      x: Array.
-      y: Array.
-
-    Returns:
-      The cost, optionally including the :attr:`norms <norm>` of
-      :math:`x`/:math:`y`.
-    """
-    cost = self.pairwise(x, y)
-    if self.norm is None:
-      return cost
-    return cost + self.norm(x) + self.norm(y)
-
   def all_pairs(self, x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
     """Compute matrix of all pairwise costs, including the :attr:`norms <norm>`.
 
@@ -127,18 +98,6 @@ def all_pairs(self, x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
     """
     return jax.vmap(lambda x_: jax.vmap(lambda y_: self(x_, y_))(y))(x)
 
-  def all_pairs_pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
-    """Compute matrix of all pairwise costs, excluding the :attr:`norms <norm>`.
-
-    Args:
-      x: Array of shape ``[n, ...]``.
-      y: Array of shape ``[m, ...]``.
-
-    Returns:
-      Array of shape ``[n, m]`` of cost evaluations.
-    """
-    return jax.vmap(lambda x_: jax.vmap(lambda y_: self.pairwise(x_, y_))(y))(x)
-
   def twist_operator(
       self, vec: jnp.ndarray, dual_vec: jnp.ndarray, variable: bool
   ) -> jnp.ndarray:
@@ -200,7 +159,7 @@ def h_legendre(self, z: jnp.ndarray) -> float:
     """Legendre transform of :func:`h` when it is convex."""
     raise NotImplementedError("Legendre transform of `h` is not implemented.")
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     """Compute cost as evaluation of :func:`h` on :math:`x-y`."""
     return self.h(x - y)
 
@@ -539,8 +498,8 @@ class Euclidean(CostFn):
   because the function is not strictly convex (it is linear on rays).
   """
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
-    """Compute Euclidean norm using custom jvp implementation.
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
+    """Compute sq. Euclidean distance using a custom jvp implementation.
 
     Here we use a custom jvp implementation for the norm that does not yield
     `NaN` gradients when differentiating the norm of `(x-x)`, but defaults
@@ -556,13 +515,14 @@ class SqEuclidean(TICost):
   Implemented as a translation invariant cost, :math:`h(z) = \|z\|^2`.
   """
 
-  def norm(self, x: jnp.ndarray) -> Union[float, jnp.ndarray]:
+  def norm(self, x: jnp.ndarray) -> jnp.ndarray:
     """Compute squared Euclidean norm for vector."""
     return jnp.sum(x ** 2, axis=-1)
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     """Compute minus twice the dot-product between vectors."""
-    return -2.0 * jnp.vdot(x, y)
+    cross_term = -2.0 * jnp.vdot(x, y)
+    return self.norm(x) + self.norm(y) + cross_term
 
   def h(self, z: jnp.ndarray) -> float:  # noqa: D102
     return jnp.sum(z ** 2)
@@ -588,7 +548,7 @@ def __init__(self, ridge: float = 1e-8):
     super().__init__()
     self._ridge = ridge
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     """Cosine distance between vectors, denominator regularized with ridge."""
     x_norm = jnp.linalg.norm(x, axis=-1)
     y_norm = jnp.linalg.norm(y, axis=-1)
@@ -624,7 +584,7 @@ def __init__(self, n: int, ridge: float = 1e-8):
     self.n = n
     self._ridge = ridge
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray):  # noqa: D102
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray):  # noqa: D102
     x_norm = jnp.linalg.norm(x, axis=-1)
     y_norm = jnp.linalg.norm(y, axis=-1)
     cosine_similarity = jnp.vdot(x, y) / (x_norm * y_norm + self._ridge)
@@ -688,7 +648,7 @@ def norm(self, x: jnp.ndarray) -> jnp.ndarray:
     norm += jnp.trace(cov, axis1=-2, axis2=-1)
     return norm
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     """Compute - 2 x Bures dot-product."""
     mean_x, cov_x = x_to_means_and_covs(x, self._dimension)
     mean_y, cov_y = x_to_means_and_covs(y, self._dimension)
@@ -698,7 +658,10 @@ def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     sq__sq_x_y_sq_x = matrix_square_root.sqrtm(
         sq_x_y_sq_x, self._dimension, **self._sqrtm_kw
     )[0]
-    return -2 * (mean_dot_prod + jnp.trace(sq__sq_x_y_sq_x, axis1=-2, axis2=-1))
+    cross_term = -2.0 * (
+        mean_dot_prod + jnp.trace(sq__sq_x_y_sq_x, axis1=-2, axis2=-1)
+    )
+    return self.norm(x) + self.norm(y) + cross_term
 
   def covariance_fixpoint_iter(
       self,
@@ -883,7 +846,7 @@ def norm(self, x: jnp.ndarray) -> jnp.ndarray:
     """
     return self._gamma * x[..., 0]
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     """Compute dot-product for unbalanced Bures.
 
     Args:
@@ -939,12 +902,13 @@ def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     log_m_pi += -0.5 * ldet_c_ab
 
     # if all logdet signs are 1, output value, nan otherwise
-    pos_signs = (sldet_c + sldet_c_ab + sldet_t_ab + sldet_t_ab) == 4
+    pos_signs = (sldet_c + sldet_c_ab + sldet_ab + sldet_t_ab) == 4
 
-    return jax.lax.cond(
+    cross_term = jax.lax.cond(
         pos_signs, lambda: 2 * sig2 * mass_x * mass_y - 2 *
         (sig2 + gam) * jnp.exp(log_m_pi), lambda: jnp.nan
     )
+    return self.norm(x) + self.norm(y) + cross_term
 
   def tree_flatten(self):  # noqa: D102
     return (), (self._dimension, self._sigma, self._gamma, self._sqrtm_kw)
@@ -977,7 +941,7 @@ def __init__(
     self.ground_cost = SqEuclidean() if ground_cost is None else ground_cost
     self.debiased = debiased
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:  # noqa: D102
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:  # noqa: D102
     c_xy = self._soft_dtw(x, y)
     if self.debiased:
       return c_xy - 0.5 * (self._soft_dtw(x, x) + self._soft_dtw(y, y))
diff --git a/src/ott/geometry/distrib_costs.py b/src/ott/geometry/distrib_costs.py
index 638694e72..7dbf2a478 100644
--- a/src/ott/geometry/distrib_costs.py
+++ b/src/ott/geometry/distrib_costs.py
@@ -51,7 +51,7 @@ def __init__(
     )
     self._solve_fn = solve_fn
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     """Wasserstein distance between :math:`x` and :math:`y` seen as a 1D dist.
 
     Args:
diff --git a/src/ott/geometry/geodesic.py b/src/ott/geometry/geodesic.py
index d08ccc0ad..b4aa1b495 100644
--- a/src/ott/geometry/geodesic.py
+++ b/src/ott/geometry/geodesic.py
@@ -16,6 +16,7 @@
 import jax
 import jax.experimental.sparse as jesp
 import jax.numpy as jnp
+import jax.tree_util as jtu
 import numpy as np
 from scipy.special import ive
 
@@ -28,7 +29,7 @@
 Array_g = Union[jnp.ndarray, jesp.BCOO]
 
 
-@jax.tree_util.register_pytree_node_class
+@jtu.register_pytree_node_class
 class Geodesic(geometry.Geometry):
   r"""Graph distance approximation using heat kernel :cite:`huguet:2023`.
 
@@ -134,14 +135,14 @@ def from_graph(
 
   def apply_kernel(
       self,
-      scaling: jnp.ndarray,
+      vec: jnp.ndarray,
       eps: Optional[float] = None,
       axis: int = 0,
   ) -> jnp.ndarray:
-    r"""Apply :attr:`kernel_matrix` on positive scaling vector.
+    r"""Apply :attr:`kernel_matrix` on a positive vector.
 
     Args:
-      scaling: Scaling to apply the kernel to.
+      vec: Vector to which the kernel is applied.
       eps: passed for consistency, not used yet.
       axis: passed for consistency, not used yet.
 
@@ -149,7 +150,7 @@ def apply_kernel(
       Kernel applied to ``scaling``.
     """
     return expm_multiply(
-        self.scaled_laplacian, scaling, self.chebyshev_coeffs, 0.5 * self.eigval
+        self.scaled_laplacian, vec, self.chebyshev_coeffs, 0.5 * self.eigval
     )
 
   @property
diff --git a/src/ott/geometry/geometry.py b/src/ott/geometry/geometry.py
index c9ef42ef7..79a074ee2 100644
--- a/src/ott/geometry/geometry.py
+++ b/src/ott/geometry/geometry.py
@@ -26,7 +26,7 @@
 from ott.geometry import epsilon_scheduler
 from ott.math import utils as mu
 
-__all__ = ["Geometry", "is_linear", "is_affine"]
+__all__ = ["Geometry"]
 
 
 @jax.tree_util.register_pytree_node_class
@@ -83,8 +83,8 @@ def __init__(
       kernel_matrix: Optional[jnp.ndarray] = None,
       epsilon: Optional[Union[float, epsilon_scheduler.Epsilon]] = None,
       relative_epsilon: Optional[Union[bool, Literal["mean", "std"]]] = None,
-      scale_cost: Union[int, float, Literal["mean", "max_cost", "median",
-                                            "std"]] = 1.0,
+      scale_cost: Union[float, Literal["mean", "max_cost", "median",
+                                       "std"]] = 1.0,
       src_mask: Optional[jnp.ndarray] = None,
       tgt_mask: Optional[jnp.ndarray] = None,
   ):
@@ -163,7 +163,7 @@ def _epsilon(self) -> epsilon_scheduler.Epsilon:
     rel = self._relative_epsilon
 
     # If nothing passed, default to STD
-    if (rel is None and target is None and scale_eps is None):
+    if rel is None and target is None and scale_eps is None:
       scale_eps = jax.lax.stop_gradient(self.std_cost_matrix)
     # If instructions passed change, otherwise (notably if False) skip.
     elif rel is not None:
@@ -277,7 +277,7 @@ def apply_lse_kernel(
       eps: float,
       vec: jnp.ndarray = None,
       axis: int = 0
-  ) -> jnp.ndarray:
+  ) -> Tuple[jnp.ndarray, jnp.ndarray]:
     r"""Apply :attr:`kernel_matrix` in log domain.
 
     This function applies the ground geometry's kernel in log domain, using
@@ -313,14 +313,14 @@ def apply_lse_kernel(
 
   def apply_kernel(
       self,
-      scaling: jnp.ndarray,
+      vec: jnp.ndarray,
       eps: Optional[float] = None,
       axis: int = 0,
   ) -> jnp.ndarray:
     """Apply :attr:`kernel_matrix` on positive scaling vector.
 
     Args:
-      scaling: jnp.ndarray [num_a or num_b] , scaling of size num_rows or
+      vec: jnp.ndarray [num_a or num_b] , scaling of size num_rows or
         num_cols of kernel_matrix
       eps: passed for consistency, not used yet.
       axis: standard kernel product if axis is 1, transpose if 0.
@@ -334,7 +334,7 @@ def apply_kernel(
       kernel = self.kernel_matrix ** (self.epsilon / eps)
     kernel = kernel if axis == 1 else kernel.T
 
-    return jnp.dot(kernel, scaling)
+    return jnp.dot(kernel, vec)
 
   def marginal_from_potentials(
       self,
@@ -583,7 +583,7 @@ def apply_cost(
       arr: jnp.ndarray,
       axis: int = 0,
       fn: Optional[Callable[[jnp.ndarray], jnp.ndarray]] = None,
-      **kwargs: Any
+      is_linear: bool = False,
   ) -> jnp.ndarray:
     """Apply :attr:`cost_matrix` to array (vector or matrix).
 
@@ -597,23 +597,25 @@ def apply_cost(
         the cost matrix.
       axis: standard cost matrix if axis=1, transpose if 0
       fn: function to apply to cost matrix element-wise before the dot product
-      kwargs: Keyword arguments for :meth:`_apply_cost_to_vec`.
+      is_linear: Whether ``fn`` is linear.
 
     Returns:
       An array, [num_b, p] if axis=0 or [num_a, p] if axis=1
     """
     if arr.ndim == 1:
-      arr = arr.reshape(-1, 1)
-
-    app = functools.partial(self._apply_cost_to_vec, axis=axis, fn=fn, **kwargs)
+      return self._apply_cost_to_vec(arr, axis=axis, fn=fn, is_linear=is_linear)
+    app = functools.partial(
+        self._apply_cost_to_vec, axis=axis, fn=fn, is_linear=is_linear
+    )
+    # TODO(michalk8): vmap over multiple dims?
     return jax.vmap(app, in_axes=1, out_axes=1)(arr)
 
   def _apply_cost_to_vec(
       self,
       vec: jnp.ndarray,
       axis: int = 0,
-      fn=None,
-      **_: Any,
+      fn: Optional[Callable[[jnp.ndarray], jnp.ndarray]] = None,
+      is_linear: bool = False,
   ) -> jnp.ndarray:
     """Apply ``[num_a, num_b]`` fn(cost) (or transpose) to vector.
 
@@ -622,12 +624,15 @@ def _apply_cost_to_vec(
       axis: axis on which the reduction is done.
       fn: function optionally applied to cost matrix element-wise, before the
         doc product
+      is_linear: Whether ``fn`` is linear.
 
     Returns:
       A jnp.ndarray corresponding to cost x vector
     """
+    del is_linear
     matrix = self.cost_matrix.T if axis == 0 else self.cost_matrix
-    matrix = fn(matrix) if fn is not None else matrix
+    if fn is not None:
+      matrix = fn(matrix)
     return jnp.dot(matrix, vec)
 
   @classmethod
@@ -929,15 +934,3 @@ def tree_unflatten(cls, aux_data, children):  # noqa: D102
     return cls(
         cost, kernel, eps, src_mask=src_mask, tgt_mask=tgt_mask, **aux_data
     )
-
-
-def is_affine(fn) -> bool:
-  """Test heuristically if a function is affine."""
-  x = jnp.arange(10.0)
-  out = jax.vmap(jax.grad(fn))(x)
-  return jnp.sum(jnp.diff(jnp.abs(out))) == 0.0
-
-
-def is_linear(fn) -> bool:
-  """Test heuristically if a function is linear."""
-  return jnp.logical_and(fn(0.0) == 0.0, is_affine(fn))
diff --git a/src/ott/geometry/graph.py b/src/ott/geometry/graph.py
index 4a9770715..d89191887 100644
--- a/src/ott/geometry/graph.py
+++ b/src/ott/geometry/graph.py
@@ -16,6 +16,7 @@
 import jax
 import jax.numpy as jnp
 import jax.scipy as jsp
+import jax.tree_util as jtu
 
 from ott.geometry import geometry
 from ott.math import fixed_point_loop
@@ -24,7 +25,7 @@
 __all__ = ["Graph"]
 
 
-@jax.tree_util.register_pytree_node_class
+@jtu.register_pytree_node_class
 class Graph(geometry.Geometry):
   r"""Graph distance approximation using heat kernel :cite:`heitz:21,crane:13`.
 
@@ -113,14 +114,14 @@ def from_graph(
 
   def apply_kernel(
       self,
-      scaling: jnp.ndarray,
+      vec: jnp.ndarray,
       eps: Optional[float] = None,
       axis: int = 0,
   ) -> jnp.ndarray:
-    r"""Apply :attr:`kernel_matrix` on positive scaling vector.
+    r"""Apply :attr:`kernel_matrix` on a positive vector.
 
     Args:
-      scaling: Scaling to apply the kernel to.
+      vec: Vector to which the kernel is applied.
       eps: passed for consistency, not used yet.
       axis: passed for consistency, not used yet.
 
@@ -168,7 +169,7 @@ def body_fn(
         if force_scan else fixed_point_loop.fixpoint_iter_backprop
     )
 
-    state = (jnp.full_like(scaling, jnp.nan), scaling)
+    state = (jnp.full_like(vec, jnp.nan), vec)
     L = jsp.linalg.cholesky(self._M, lower=True)
     if self.numerical_scheme == "crank_nicolson":
       constants = L, self._scaled_laplacian
diff --git a/src/ott/geometry/grid.py b/src/ott/geometry/grid.py
index 06a7dae69..6df230046 100644
--- a/src/ott/geometry/grid.py
+++ b/src/ott/geometry/grid.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import itertools
-from typing import Any, List, NoReturn, Optional, Sequence, Tuple
+from typing import Any, Callable, List, NoReturn, Optional, Sequence, Tuple
 
 import jax
 import jax.numpy as jnp
@@ -209,7 +209,11 @@ def _apply_lse_kernel_one_dimension(self, dimension, f, g, eps, vec=None):
     return jnp.transpose(softmax_res, indices), None
 
   def _apply_cost_to_vec(
-      self, vec: jnp.ndarray, axis: int = 0, fn=None
+      self,
+      vec: jnp.ndarray,
+      axis: int = 0,
+      fn: Optional[Callable[[jnp.ndarray], jnp.ndarray]] = None,
+      is_linear: bool = False,
   ) -> jnp.ndarray:
     r"""Apply grid's cost matrix (without instantiating it) to a vector.
 
@@ -233,10 +237,13 @@ def _apply_cost_to_vec(
       axis: axis 0 if applying transpose costs, 1 if using the original cost.
       fn: function optionally applied to cost matrix element-wise, before the
         dot product.
+      is_linear: TODO.
 
     Returns:
       A jnp.ndarray corresponding to cost x matrix
     """
+    # TODO(michalk8):
+    del fn, is_linear
     vec = jnp.reshape(vec, self.grid_size)
     accum_vec = jnp.zeros_like(vec)
     indices = list(range(1, self.grid_dimension))
@@ -255,7 +262,7 @@ def _apply_cost_to_vec(
 
   def apply_kernel(
       self,
-      scaling: jnp.ndarray,
+      vec: jnp.ndarray,
       eps: Optional[float] = None,
       axis: Optional[int] = None
   ) -> jnp.ndarray:
@@ -269,24 +276,22 @@ def apply_kernel(
     More implementation details in :cite:`schmitz:18`,
 
     Args:
-      scaling: jnp.ndarray, a vector of scaling (>0) values.
+      vec: jnp.ndarray, a vector of scaling (>0) values.
       eps: float, regularization strength
       axis: axis (0 or 1) along which summation should be carried out.
 
     Returns:
       a vector, the result of kernel applied onto scaling.
     """
-    scaling = jnp.reshape(scaling, self.grid_size)
+    vec = jnp.reshape(vec, self.grid_size)
     indices = list(range(1, self.grid_dimension))
     for dimension, geom in enumerate(self.geometries):
       kernel = geom.kernel_matrix
       kernel = kernel if eps is None else kernel ** (self.epsilon / eps)
       ind = indices.copy()
       ind.insert(dimension, 0)
-      scaling = jnp.tensordot(
-          kernel, scaling, axes=([0], [dimension])
-      ).transpose(ind)
-    return scaling.ravel()
+      vec = jnp.tensordot(kernel, vec, axes=([0], [dimension])).transpose(ind)
+    return vec.ravel()
 
   def transport_from_potentials(
       self, f: jnp.ndarray, g: jnp.ndarray, axis: int = 0
diff --git a/src/ott/geometry/low_rank.py b/src/ott/geometry/low_rank.py
index 2ab5aba27..7a776e17f 100644
--- a/src/ott/geometry/low_rank.py
+++ b/src/ott/geometry/low_rank.py
@@ -43,11 +43,6 @@ class LRCGeometry(geometry.Geometry):
       'max_bound', 'mean' and 'max_cost'. Alternatively, a float
       factor can be given to rescale the cost such that
       ``cost_matrix /= scale_cost``.
-    batch_size: optional size of the batch to compute online (without
-      instantiating the matrix) the scale factor ``scale_cost`` of the
-      :attr:`cost_matrix` when ``scale_cost = 'max_cost'``. If `None`, the batch
-      size is set to `1024` or to the largest number of samples between
-      :attr:`cost_1` and :attr:`cost_2` if smaller than `1024`.
     kwargs: keyword arguments for :class:`~ott.geometry.geometry.Geometry`.
   """
 
@@ -57,9 +52,7 @@ def __init__(
       cost_2: jnp.ndarray,
       bias: float = 0.0,
       scale_factor: float = 1.0,
-      scale_cost: Union[int, float, Literal["mean", "max_bound",
-                                            "max_cost"]] = 1.0,
-      batch_size: Optional[int] = None,
+      scale_cost: Union[float, Literal["mean", "max_bound", "max_cost"]] = 1.0,
       **kwargs: Any,
   ):
     super().__init__(**kwargs)
@@ -68,7 +61,6 @@ def __init__(
     self._bias = bias
     self._scale_factor = scale_factor
     self._scale_cost = scale_cost
-    self.batch_size = batch_size
 
   @property
   def cost_1(self) -> jnp.ndarray:
@@ -115,15 +107,14 @@ def inv_scale_cost(self) -> float:  # noqa: D102
     if self._scale_cost == "max_bound":
       x_norm = self._cost_1[:, 0].max()
       y_norm = self._cost_2[:, 1].max()
-      max_bound = x_norm + y_norm + 2 * jnp.sqrt(x_norm * y_norm)
+      max_bound = x_norm + y_norm + 2.0 * jnp.sqrt(x_norm * y_norm)
       return 1.0 / (max_bound + self._bias)
     if self._scale_cost == "mean":
-      factor1 = jnp.dot(self._n_normed_ones, self._cost_1)
-      factor2 = jnp.dot(self._cost_2.T, self._m_normed_ones)
-      mean = jnp.dot(factor1, factor2) + self._bias
-      return 1.0 / mean
+      a, b = self._n_normed_ones, self._m_normed_ones
+      mean = jnp.linalg.multi_dot([a, self._cost_1, self._cost_2.T, b])
+      return 1.0 / (mean + self._bias)
     if self._scale_cost == "max_cost":
-      return 1.0 / self.compute_max_cost()
+      return 1.0 / self._max_cost_matrix
     raise ValueError(f"Scaling {self._scale_cost} not implemented.")
 
   def apply_square_cost(self, arr: jnp.ndarray, axis: int = 0) -> jnp.ndarray:
@@ -157,75 +148,35 @@ def _apply_cost_to_vec(
       fn: function optionally applied to cost matrix element-wise, before the
         doc product
       is_linear: Whether ``fn`` is a linear function to enable efficient
-        implementation. See :func:`ott.geometry.geometry.is_linear`
-        for a heuristic to help determine if a function is linear.
+        implementation.
 
     Returns:
       A jnp.ndarray corresponding to cost x vector
     """
-
-    def linear_apply(
-        vec: jnp.ndarray, axis: int, fn: Callable[[jnp.ndarray], jnp.ndarray]
-    ) -> jnp.ndarray:
-      c1 = self.cost_1 if axis == 1 else self.cost_2
-      c2 = self.cost_2 if axis == 1 else self.cost_1
-      c2 = fn(c2) if fn is not None else c2
-      bias = fn(self.bias) if fn is not None else self.bias
-      out = jnp.dot(c1, jnp.dot(c2.T, vec))
-      return out + bias * jnp.sum(vec) * jnp.ones_like(out)
-
     if fn is None or is_linear:
-      return linear_apply(vec, axis, fn=fn)
-    return super()._apply_cost_to_vec(vec, axis, fn=fn)
-
-  def compute_max_cost(self) -> float:
-    """Compute the maximum of the :attr:`cost_matrix`.
-
-    Three cases are taken into account:
-
-    - If the number of samples of ``cost_1`` and ``cost_2`` are both smaller
-      than 1024 and if ``batch_size`` is `None`, the ``cost_matrix`` is
-      computed to obtain its maximum entry.
-    - If one of the number of samples of ``cost_1`` or ``cost_2`` is larger
-      than 1024 and if ``batch_size`` is `None`, then the maximum of the
-      cost matrix is calculated by batch. The batches are created on the
-      longest axis of the cost matrix and their size is fixed to 1024.
-    - If ``batch_size`` is provided as a float, then the maximum of the cost
-      matrix is calculated by batch. The batches are created on the longest
-      axis of the cost matrix and their size if fixed by ``batch_size``.
-
-    Returns:
-      Maximum of the cost matrix.
-    """
-    batch_for_y = self.shape[1] > self.shape[0]
-
-    n = self.shape[1] if batch_for_y else self.shape[0]
-    p = self._cost_2.shape[1] if batch_for_y else self._cost_1.shape[1]
-    carry = ((self._cost_1, self._cost_2) if batch_for_y else
-             (self._cost_2, self._cost_1))
-
-    if self.batch_size:
-      batch_size = min(self.batch_size, n)
-    else:
-      batch_size = min(1024, max(self.shape[0], self.shape[1]))
-    n_batch = n // batch_size
-
-    def body(carry, slice_idx):
-      cost1, cost2 = carry
-      cost2_slice = jax.lax.dynamic_slice(
-          cost2, (slice_idx * batch_size, 0), (batch_size, p)
-      )
-      out_slice = jnp.max(jnp.dot(cost2_slice, cost1.T))
-      return carry, out_slice
+      return self._apply_cost_to_vec_fast(vec, axis, fn=fn)
+    return super()._apply_cost_to_vec(vec, axis, fn=fn, is_linear=is_linear)
 
-    def finalize(carry):
-      cost1, cost2 = carry
-      return jnp.dot(cost2[n_batch * batch_size:], cost1.T)
+  def _apply_cost_to_vec_fast(
+      self,
+      vec: jnp.ndarray,
+      axis: int = 0,
+      fn: Optional[Callable[[jnp.ndarray], jnp.ndarray]] = None,
+  ) -> jnp.ndarray:
+    c1, c2 = (self.cost_1,
+              self.cost_2) if axis == 1 else (self.cost_2, self.cost_1)
+    bias = self.bias
+    if fn is not None:
+      c2, bias = fn(c2), fn(bias)
+    out = jnp.linalg.multi_dot([c1, c2.T, vec])
+    return out + bias * jnp.sum(vec) * jnp.ones_like(out)
 
-    _, out = jax.lax.scan(body, carry, jnp.arange(n_batch))
-    last_slice = finalize(carry)
-    max_value = jnp.max(jnp.concatenate((out, last_slice.reshape(-1))))
-    return max_value + self._bias
+  @property
+  def _max_cost_matrix(self) -> jnp.ndarray:
+    fn = utils.batched_vmap(
+        lambda c1, c2: jnp.max(c1 @ c2.T), batch_size=1024, in_axes=(0, None)
+    )
+    return jnp.max(fn(self._cost_1, self._cost_2)) + self._bias
 
   def to_LRCGeometry(
       self,
@@ -329,7 +280,6 @@ def tree_flatten(self):  # noqa: D102
         self._scale_factor,
     ), {
         "scale_cost": self._scale_cost,
-        "batch_size": self.batch_size
     }
 
   @classmethod
@@ -422,13 +372,13 @@ def from_pointcloud(
 
   def apply_kernel(  # noqa: D102
       self,
-      scaling: jnp.ndarray,
+      vec: jnp.ndarray,
       eps: Optional[float] = None,
       axis: int = 0,
   ) -> jnp.ndarray:
     if axis == 0:
-      return self.k2 @ (self.k1.T @ scaling)
-    return self.k1 @ (self.k2.T @ scaling)
+      return self.k2 @ (self.k1.T @ vec)
+    return self.k1 @ (self.k2.T @ vec)
 
   @property
   def kernel_matrix(self) -> jnp.ndarray:  # noqa: D102
diff --git a/src/ott/geometry/pointcloud.py b/src/ott/geometry/pointcloud.py
index 3c6b64375..7ac9f4425 100644
--- a/src/ott/geometry/pointcloud.py
+++ b/src/ott/geometry/pointcloud.py
@@ -11,12 +11,12 @@
 # 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.
-import math
 from typing import Any, Callable, Literal, Optional, Tuple, Union
 
 import jax
 import jax.numpy as jnp
 
+from ott import utils
 from ott.geometry import costs, geometry, low_rank
 from ott.math import utils as mu
 
@@ -60,133 +60,20 @@ def __init__(
       y: Optional[jnp.ndarray] = None,
       cost_fn: Optional[costs.CostFn] = None,
       batch_size: Optional[int] = None,
-      scale_cost: Union[int, float, Literal["mean", "max_norm", "max_bound",
-                                            "max_cost", "median"]] = 1.0,
-      **kwargs: Any
+      scale_cost: Union[float, Literal["mean", "max_norm", "max_bound",
+                                       "max_cost", "median"]] = 1.0,
+      **kwargs: Any,
   ):
     super().__init__(**kwargs)
     self.x = x
     self.y = self.x if y is None else y
 
     self.cost_fn = costs.SqEuclidean() if cost_fn is None else cost_fn
-    self._axis_norm = 0 if callable(self.cost_fn.norm) else None
     if batch_size is not None:
       assert batch_size > 0, f"`batch_size={batch_size}` must be positive."
     self._batch_size = batch_size
     self._scale_cost = scale_cost
 
-  @property
-  def _norm_x(self) -> Union[float, jnp.ndarray]:
-    if self._axis_norm == 0:
-      return self.cost_fn.norm(self.x)
-    return 0.0
-
-  @property
-  def _norm_y(self) -> Union[float, jnp.ndarray]:
-    if self._axis_norm == 0:
-      return self.cost_fn.norm(self.y)
-    return 0.0
-
-  @property
-  def can_LRC(self):  # noqa: D102
-    return self.is_squared_euclidean and self._check_LRC_dim
-
-  @property
-  def _check_LRC_dim(self):
-    (n, m), d = self.shape, self.x.shape[1]
-    return n * m > (n + m) * d
-
-  @property
-  def cost_matrix(self) -> Optional[jnp.ndarray]:  # noqa: D102
-    if self.is_online:
-      return None
-    cost_matrix = self._compute_cost_matrix()
-    return cost_matrix * self.inv_scale_cost
-
-  @property
-  def kernel_matrix(self) -> Optional[jnp.ndarray]:  # noqa: D102
-    if self.is_online:
-      return None
-    return jnp.exp(-self.cost_matrix / self.epsilon)
-
-  @property
-  def shape(self) -> Tuple[int, int]:  # noqa: D102
-    # in the process of flattening/unflattening in vmap, `__init__`
-    # can be called with dummy objects
-    # we optionally access `shape` in order to get the batch size
-    if self.x is None or self.y is None:
-      return 0, 0
-    return self.x.shape[0], self.y.shape[0]
-
-  @property
-  def is_symmetric(self) -> bool:  # noqa: D102
-    return self.y is None or (
-        jnp.all(self.x.shape == self.y.shape) and jnp.all(self.x == self.y)
-    )
-
-  @property
-  def is_squared_euclidean(self) -> bool:  # noqa: D102
-    return isinstance(self.cost_fn, costs.SqEuclidean)
-
-  @property
-  def is_online(self) -> bool:
-    """Whether the cost/kernel is computed on-the-fly."""
-    return self.batch_size is not None
-
-  @property
-  def cost_rank(self) -> int:  # noqa: D102
-    return self.x.shape[1]
-
-  @property
-  def inv_scale_cost(self) -> float:  # noqa: D102
-    if isinstance(self._scale_cost, (int, float, jax.Array)):
-      return 1.0 / self._scale_cost
-    self = self._masked_geom()
-    if self._scale_cost == "max_cost":
-      if self.is_online:
-        return 1.0 / self._compute_summary_online(self._scale_cost)
-      return 1.0 / jnp.max(self._compute_cost_matrix())
-    if self._scale_cost == "mean":
-      if self.is_online:
-        return 1.0 / self._compute_summary_online(self._scale_cost)
-      if self.shape[0] > 0:
-        geom = self._masked_geom(mask_value=jnp.nan)._compute_cost_matrix()
-        return 1.0 / jnp.nanmean(geom)
-      return 1.0
-    if self._scale_cost == "median":
-      if not self.is_online:
-        geom = self._masked_geom(mask_value=jnp.nan)
-        return 1.0 / jnp.nanmedian(geom._compute_cost_matrix())
-      raise NotImplementedError(
-          "Using the median as scaling factor for "
-          "the cost matrix with the online mode is not implemented."
-      )
-    if self._scale_cost == "max_norm":
-      if self.cost_fn.norm is not None:
-        return 1.0 / jnp.maximum(self._norm_x.max(), self._norm_y.max())
-      return 1.0
-    if self._scale_cost == "max_bound":
-      if self.is_squared_euclidean:
-        x_argmax = jnp.argmax(self._norm_x)
-        y_argmax = jnp.argmax(self._norm_y)
-        max_bound = (
-            self._norm_x[x_argmax] + self._norm_y[y_argmax] +
-            2 * jnp.sqrt(self._norm_x[x_argmax] * self._norm_y[y_argmax])
-        )
-        return 1.0 / max_bound
-      raise NotImplementedError(
-          "Using max_bound as scaling factor for "
-          "the cost matrix when the cost is not squared euclidean "
-          "is not implemented."
-      )
-    raise ValueError(f"Scaling {self._scale_cost} not implemented.")
-
-  def _compute_cost_matrix(self) -> jnp.ndarray:
-    cost_matrix = self.cost_fn.all_pairs_pairwise(self.x, self.y)
-    if self._axis_norm is not None:
-      cost_matrix += self._norm_x[:, jnp.newaxis] + self._norm_y[jnp.newaxis, :]
-    return cost_matrix
-
   def apply_lse_kernel(  # noqa: D102
       self,
       f: jnp.ndarray,
@@ -194,233 +81,116 @@ def apply_lse_kernel(  # noqa: D102
       eps: float,
       vec: Optional[jnp.ndarray] = None,
       axis: int = 0
-  ) -> jnp.ndarray:
-
-    def body0(carry, i: int):
-      f, g, eps, vec = carry
-      y, g_ = self._leading_slice(self.y, i), self._leading_slice(g, i)
-      norm_y = self._norm_y if self._axis_norm is None else self._leading_slice(
-          self._norm_y, i
-      )
-      h_res, h_sgn = app(
-          self.x, y, self._norm_x, norm_y, f, g_, eps, vec, cost_fn,
-          self.inv_scale_cost
-      )
-      return carry, (h_res, h_sgn)
-
-    def body1(carry, i: int):
-      f, g, eps, vec = carry
-      x, f_ = self._leading_slice(self.x, i), self._leading_slice(f, i)
-      norm_x = self._norm_x if self._axis_norm is None else self._leading_slice(
-          self._norm_x, i
-      )
-      h_res, h_sgn = app(
-          self.y, x, self._norm_y, norm_x, g, f_, eps, vec, cost_fn,
-          self.inv_scale_cost
-      )
-      return carry, (h_res, h_sgn)
-
-    def rest(i: int):
-      if axis == 0:
-        norm_y = self._norm_y if self._axis_norm is None else self._norm_y[i:]
-        return app(
-            self.x, self.y[i:], self._norm_x, norm_y, f, g[i:], eps, vec,
-            cost_fn, self.inv_scale_cost
-        )
-      norm_x = self._norm_x if self._axis_norm is None else self._norm_x[i:]
-      return app(
-          self.y, self.x[i:], self._norm_y, norm_x, g, f[i:], eps, vec, cost_fn,
-          self.inv_scale_cost
-      )
-
+  ) -> Tuple[jnp.ndarray, jnp.ndarray]:
     if not self.is_online:
       return super().apply_lse_kernel(f, g, eps, vec, axis)
 
-    app = jax.vmap(
-        _apply_lse_kernel_xy,
-        in_axes=[
-            None, 0, None, self._axis_norm, None, 0, None, None, None, None
-        ]
-    )
-
-    if axis == 0:
-      fun, cost_fn = body0, self.cost_fn.pairwise
-      v, n = g, self._y_nsplit
-    elif axis == 1:
-      fun, cost_fn = body1, lambda y, x: self.cost_fn.pairwise(x, y)
-      v, n = f, self._x_nsplit
-    else:
-      raise ValueError(axis)
-
-    _, (h_res, h_sign) = jax.lax.scan(
-        fun, init=(f, g, eps, vec), xs=jnp.arange(n)
+    def apply(x: jnp.ndarray, y: jnp.ndarray, f: jnp.ndarray,
+              g: jnp.ndarray) -> Tuple[jnp.ndarray, jnp.ndarray]:
+      x, y = jnp.atleast_2d(x), jnp.atleast_2d(y)
+      cost = self.cost_fn.all_pairs(x, y) * inv_scale_cost
+      cost = cost.squeeze(1 - axis)
+      # axis=-1
+      res, sgn = mu.logsumexp((f + g - cost) / eps, b=vec, return_sign=True)
+      return eps * res, sgn
+
+    inv_scale_cost = self.inv_scale_cost
+    in_axes = (None, 0, None, 0) if axis == 0 else (0, None, 0, None)
+    batched_apply = utils.batched_vmap(
+        apply,
+        batch_size=self.batch_size,
+        in_axes=in_axes,
     )
-    h_res, h_sign = jnp.concatenate(h_res), jnp.concatenate(h_sign)
-    h_res_rest, h_sign_rest = rest(n * self.batch_size)
-    h_res = jnp.concatenate([h_res, h_res_rest])
-    h_sign = jnp.concatenate([h_sign, h_sign_rest])
-
-    return eps * h_res - jnp.where(jnp.isfinite(v), v, 0), h_sign
+    w_res, w_sgn = batched_apply(self.x, self.y, f, g)
+    remove = f if axis == 1 else g
+    return w_res - jnp.where(jnp.isfinite(remove), remove, 0), w_sgn
 
   def apply_kernel(  # noqa: D102
       self,
-      scaling: jnp.ndarray,
+      vec: jnp.ndarray,
       eps: Optional[float] = None,
       axis: int = 0
   ) -> jnp.ndarray:
     if eps is None:
       eps = self.epsilon
-
-    if not self.is_online:
-      return super().apply_kernel(scaling, eps, axis)
-
-    # TODO(michalk8): batch this properly
-    app = jax.vmap(
-        _apply_kernel_xy,
-        in_axes=[None, 0, None, self._axis_norm, None, None, None, None]
-    )
-    if axis == 0:
-      return app(
-          self.x, self.y, self._norm_x, self._norm_y, scaling, eps,
-          self.cost_fn.pairwise, self.inv_scale_cost
-      )
-    # for non-symmetric costs
-    cost_fn = lambda y, x: self.cost_fn.pairwise(x, y)
-    return app(
-        self.y, self.x, self._norm_y, self._norm_x, scaling, eps, cost_fn,
-        self.inv_scale_cost
-    )
-
-  def transport_from_potentials(  # noqa: D102
-      self, f: jnp.ndarray, g: jnp.ndarray
-  ) -> jnp.ndarray:
     if not self.is_online:
-      return super().transport_from_potentials(f, g)
-    in_axes = [None, 0, None, self._axis_norm, None, 0, None, None, None]
-    transport = jax.vmap(_transport_from_potentials_xy, in_axes=in_axes)
-    cost_fn = lambda y, x: self.cost_fn.pairwise(x, y)
-    return transport(
-        self.y, self.x, self._norm_y, self._norm_x, g, f, self.epsilon, cost_fn,
-        self.inv_scale_cost
+      return super().apply_kernel(vec, eps, axis)
+
+    def apply(x: jnp.ndarray, y: jnp.ndarray, vec: jnp.ndarray) -> jnp.ndarray:
+      x, y = jnp.atleast_2d(x), jnp.atleast_2d(y)
+      cost = self.cost_fn.all_pairs(x, y) * inv_scale_cost
+      cost = cost.squeeze(1 - axis)
+      return jnp.dot(jnp.exp(-cost / eps), vec)
+
+    inv_scale_cost = self.inv_scale_cost
+    in_axes = (None, 0, None) if axis == 0 else (0, None, None)
+    batched_apply = utils.batched_vmap(
+        apply, batch_size=self.batch_size, in_axes=in_axes
     )
+    return batched_apply(self.x, self.y, vec)
 
-  def transport_from_scalings(  # noqa: D102
-      self, u: jnp.ndarray, v: jnp.ndarray
-  ) -> jnp.ndarray:
-    if not self.is_online:
-      return super().transport_from_scalings(u, v)
-    in_axes = [None, 0, None, self._axis_norm, None, 0, None, None, None]
-    transport = jax.vmap(_transport_from_scalings_xy, in_axes=in_axes)
-    cost_fn = lambda y, x: self.cost_fn.pairwise(x, y)
-    return transport(
-        self.y, self.x, self._norm_y, self._norm_x, v, u, self.epsilon, cost_fn,
-        self.inv_scale_cost
-    )
-
-  def apply_cost(
+  def _apply_cost_to_vec(
       self,
-      arr: jnp.ndarray,
+      vec: jnp.ndarray,
       axis: int = 0,
       fn: Optional[Callable[[jnp.ndarray], jnp.ndarray]] = None,
       is_linear: bool = False,
+      scale_cost: Optional[float] = None,
   ) -> jnp.ndarray:
-    """Apply cost matrix to array (vector or matrix).
-
-    This function applies the geometry's cost matrix, to perform either
-    output = C arr (if axis=1)
-    output = C' arr (if axis=0)
-    where C is [num_a, num_b] matrix resulting from the (optional) elementwise
-    application of fn to each entry of the :attr:`cost_matrix`.
 
-    Args:
-      arr: jnp.ndarray [num_a or num_b, batch], vector that will be multiplied
-        by the cost matrix.
-      axis: standard cost matrix if axis=1, transpose if 0.
-      fn: function optionally applied to cost matrix element-wise, before the
-        apply.
-      is_linear: Whether ``fn`` is a linear function.
-        If true and :attr:`is_squared_euclidean` is ``True``, efficient
-        implementation is used. See :func:`ott.geometry.geometry.is_linear`
-        for a heuristic to help determine if a function is linear.
-
-    Returns:
-      A jnp.ndarray, [num_b, batch] if axis=0 or [num_a, batch] if axis=1
-    """
-    # switch to efficient computation for the squared euclidean case.
+    def apply(x: jnp.ndarray, y: jnp.ndarray, arr: jnp.ndarray) -> jnp.ndarray:
+      x, y = jnp.atleast_2d(x), jnp.atleast_2d(y)
+      cost = self.cost_fn.all_pairs(x, y) * scale_cost
+      cost = cost.squeeze(1 - axis)
+      if fn is not None:
+        cost = fn(cost)
+      return jnp.dot(cost, arr)
+
+    # when computing the online properties, this is set to 1.0
+    if scale_cost is None:
+      scale_cost = self.inv_scale_cost
+    # switch to an efficient computation for the squared Euclidean case
     if self.is_squared_euclidean and (fn is None or is_linear):
-      return self.vec_apply_cost(arr, axis, fn=fn)
-
-    return self._apply_cost(arr, axis, fn=fn)
+      return self._apply_sqeucl_cost(
+          vec,
+          scale_cost,
+          axis=axis,
+          fn=fn,
+      )
 
-  def _apply_cost(
-      self, arr: jnp.ndarray, axis: int = 0, fn=None
-  ) -> jnp.ndarray:
-    """See :meth:`apply_cost`."""
+    # materialize the cost
     if not self.is_online:
-      return super().apply_cost(arr, axis, fn)
-
-    # TODO(michalk8): batch this properly
-    app = jax.vmap(
-        _apply_cost_xy,
-        in_axes=[None, 0, None, self._axis_norm, None, None, None, None]
-    )
-    if arr.ndim == 1:
-      arr = arr.reshape(-1, 1)
-
-    if axis == 0:
-      return app(
-          self.x, self.y, self._norm_x, self._norm_y, arr,
-          self.cost_fn.pairwise, self.inv_scale_cost, fn
+      return super()._apply_cost_to_vec(
+          vec, axis=axis, fn=fn, is_linear=is_linear
       )
-    cost_fn = lambda y, x: self.cost_fn.pairwise(x, y)
-    return app(
-        self.y, self.x, self._norm_y, self._norm_x, arr, cost_fn,
-        self.inv_scale_cost, fn
+
+    in_axes = (None, 0, None) if axis == 0 else (0, None, None)
+    batched_apply = utils.batched_vmap(
+        apply, batch_size=self.batch_size, in_axes=in_axes
     )
+    return batched_apply(self.x, self.y, vec)
 
-  def vec_apply_cost(
+  def _apply_sqeucl_cost(
       self,
-      arr: jnp.ndarray,
+      vec: jnp.ndarray,
+      scale_cost: float,
       axis: int = 0,
-      fn: Optional[Callable[[jnp.ndarray], jnp.ndarray]] = None
+      fn: Optional[Callable[[jnp.ndarray], jnp.ndarray]] = None,
   ) -> jnp.ndarray:
-    """Apply the geometry's cost matrix in a vectorized way.
-
-    This function can be used when the cost matrix is squared euclidean
-    and ``fn`` is a linear function.
-
-    Args:
-      arr: jnp.ndarray [num_a or num_b, p], vector that will be multiplied
-        by the cost matrix.
-      axis: standard cost matrix if axis=1, transport if 0.
-      fn: function optionally applied to cost matrix element-wise, before the
-        application.
-
-    Returns:
-      A jnp.ndarray, [num_b, p] if axis=0 or [num_a, p] if axis=1
-    """
+    assert vec.ndim == 1, vec.shape
     assert self.is_squared_euclidean, "Cost matrix is not a squared Euclidean."
-    rank = arr.ndim
     x, y = (self.x, self.y) if axis == 0 else (self.y, self.x)
-    nx, ny = jnp.asarray(self._norm_x), jnp.asarray(self._norm_y)
-    nx, ny = (nx, ny) if axis == 0 else (ny, nx)
+    nx, ny = self.cost_fn.norm(x), self.cost_fn.norm(y)
 
-    applied_cost = jnp.dot(nx, arr).reshape(1, -1)
-    applied_cost += ny.reshape(-1, 1) * jnp.sum(arr, axis=0).reshape(1, -1)
-    cross_term = -2.0 * jnp.dot(y, jnp.dot(x.T, arr))
-    applied_cost += cross_term[:, None] if rank == 1 else cross_term
+    applied_cost = jnp.dot(nx, vec) + ny * jnp.sum(vec, axis=0)
+    applied_cost = applied_cost - 2.0 * jnp.dot(y, jnp.dot(x.T, vec))
     if fn is not None:
       applied_cost = fn(applied_cost)
-    return self.inv_scale_cost * applied_cost
-
-  def _leading_slice(self, t: jnp.ndarray, i: int) -> jnp.ndarray:
-    start_indices = [i * self.batch_size] + (t.ndim - 1) * [0]
-    slice_sizes = [self.batch_size] + list(t.shape[1:])
-    return jax.lax.dynamic_slice(t, start_indices, slice_sizes)
+    return scale_cost * applied_cost
 
   def _compute_summary_online(
       self, summary: Literal["mean", "max_cost"]
-  ) -> float:
+  ) -> jnp.ndarray:
     """Compute mean or max of cost matrix online, i.e. without instantiating it.
 
     Args:
@@ -429,66 +199,22 @@ def _compute_summary_online(
     Returns:
       summary statistics
     """
-    scale_cost = 1.0
-
-    def body0(vec: jnp.ndarray, i: int):
-      y = self._leading_slice(self.y, i)
-      norm_y = self._norm_y if self._axis_norm is None else self._leading_slice(
-          self._norm_y, i
-      )
-      h_res = app(self.x, y, self._norm_x, norm_y, vec, cost_fn, scale_cost)
-      return vec, h_res
 
-    def body1(vec: jnp.ndarray, i: int):
-      x = self._leading_slice(self.x, i)
-      norm_x = self._norm_x if self._axis_norm is None else self._leading_slice(
-          self._norm_x, i
-      )
-      h_res = app(self.y, x, self._norm_y, norm_x, vec, cost_fn, scale_cost)
-      return vec, h_res
-
-    def rest(i: int) -> jnp.ndarray:
-      if batch_for_y:
-        norm_y = self._norm_y if self._axis_norm is None else self._norm_y[i:]
-        return app(
-            self.x, self.y[i:], self._norm_x, norm_y, vec, cost_fn, scale_cost
-        )
-      norm_x = self._norm_x if self._axis_norm is None else self._norm_x[i:]
-      return app(
-          self.y, self.x[i:], self._norm_y, norm_x, vec, cost_fn, scale_cost
-      )
+    def compute_max(x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
+      x, y = jnp.atleast_2d(x), jnp.atleast_2d(y)
+      cost = self.cost_fn.all_pairs(x, y)
+      return jnp.max(jnp.abs(cost))
 
     if summary == "mean":
-      fn = _apply_cost_xy
-    elif summary == "max_cost":
-      fn = _apply_max_xy
-    else:
-      raise ValueError(
-          f"Scaling method {summary} does not exist for online mode."
-      )
-    app = jax.vmap(
-        fn, in_axes=[None, 0, None, self._axis_norm, None, None, None]
-    )
-
-    batch_for_y = self.shape[0] < self.shape[1]
-    if batch_for_y:
-      fun, cost_fn = body0, self.cost_fn.pairwise
-      n = self._y_nsplit
-      vec, other = self._n_normed_ones, self._m_normed_ones
-    else:
-      fun, cost_fn = body1, lambda y, x: self.cost_fn.pairwise(x, y)
-      n = self._x_nsplit
-      vec, other = self._m_normed_ones, self._n_normed_ones
-
-    _, val = jax.lax.scan(fun, init=vec, xs=jnp.arange(n))
-    val = jnp.concatenate(val).squeeze()
-    val_rest = rest(n * self.batch_size)
-    val_res = jnp.concatenate([val, val_rest])
+      a, b = self._n_normed_ones, self._m_normed_ones
+      return jnp.sum(self._apply_cost_to_vec(a, scale_cost=1.0) * b)
 
-    if summary == "mean":
-      return jnp.sum(val_res * other)
     if summary == "max_cost":
-      return jnp.max(val_res)
+      fn = utils.batched_vmap(
+          compute_max, batch_size=self.batch_size, in_axes=[0, None]
+      )
+      return jnp.max(fn(self.x, self.y))
+
     raise ValueError(
         f"Scaling method {summary} does not exist for online mode."
     )
@@ -669,89 +395,101 @@ def _mask_subset_helper(
     )
 
   @property
-  def dtype(self) -> jnp.dtype:  # noqa: D102
-    return self.x.dtype
+  def cost_matrix(self) -> Optional[jnp.ndarray]:  # noqa: D102
+    return self.inv_scale_cost * self._unscaled_cost_matrix
 
   @property
-  def batch_size(self) -> Optional[int]:
-    """Batch size for online mode."""
-    if self._batch_size is None:
-      return None
-    n, m = self.shape
-    return min(n, m, self._batch_size)
+  def _unscaled_cost_matrix(self) -> jnp.ndarray:
+    return self.cost_fn.all_pairs(self.x, self.y)
 
   @property
-  def _x_nsplit(self) -> Optional[int]:
-    if self.batch_size is None:
-      return None
-    n, _ = self.shape
-    return int(math.floor(n / self.batch_size))
+  def inv_scale_cost(self) -> float:  # noqa: D102
+    if isinstance(self._scale_cost, (int, float, jax.Array)):
+      return 1.0 / self._scale_cost
+    self = self._masked_geom()
+    if self._scale_cost == "max_cost":
+      if self.is_online:
+        return 1.0 / self._compute_summary_online(self._scale_cost)
+      return 1.0 / jnp.max(self._unscaled_cost_matrix)
+    if self._scale_cost == "mean":
+      if self.is_online:
+        return 1.0 / self._compute_summary_online(self._scale_cost)
+      geom = self._masked_geom(mask_value=jnp.nan)._unscaled_cost_matrix
+      return 1.0 / jnp.nanmean(geom)
+    if self._scale_cost == "median":
+      if not self.is_online:
+        geom = self._masked_geom(mask_value=jnp.nan)
+        return 1.0 / jnp.nanmedian(geom._unscaled_cost_matrix)
+      raise NotImplementedError(
+          "Using the median as scaling factor for "
+          "the cost matrix with the online mode is not implemented."
+      )
+    if not hasattr(self.cost_fn, "norm"):
+      raise ValueError("Cost function has no norm method.")
+    norm_x = self.cost_fn.norm(self.x)
+    norm_y = self.cost_fn.norm(self.y)
+    if self._scale_cost == "max_norm":
+      return 1.0 / jnp.maximum(norm_x.max(), norm_y.max())
+    if self._scale_cost == "max_bound":
+      if self.is_squared_euclidean:
+        x_argmax = jnp.argmax(norm_x)
+        y_argmax = jnp.argmax(norm_y)
+        max_bound = (
+            norm_x[x_argmax] + norm_y[y_argmax] +
+            2 * jnp.sqrt(norm_x[x_argmax] * norm_y[y_argmax])
+        )
+        return 1.0 / max_bound
+      raise NotImplementedError(
+          "Using max_bound as scaling factor for "
+          "the cost matrix when the cost is not squared euclidean "
+          "is not implemented."
+      )
+    raise ValueError(f"Scaling {self._scale_cost} not implemented.")
 
   @property
-  def _y_nsplit(self) -> Optional[int]:
-    if self.batch_size is None:
-      return None
-    _, m = self.shape
-    return int(math.floor(m / self.batch_size))
-
-
-def _apply_lse_kernel_xy(
-    x, y, norm_x, norm_y, f, g, eps, vec, cost_fn, scale_cost
-):
-  c = _cost(x, y, norm_x, norm_y, cost_fn, scale_cost)
-  return mu.logsumexp((f + g - c) / eps, b=vec, return_sign=True, axis=-1)
-
-
-def _transport_from_potentials_xy(
-    x, y, norm_x, norm_y, f, g, eps, cost_fn, scale_cost
-):
-  c = _cost(x, y, norm_x, norm_y, cost_fn, scale_cost)
-  return jnp.exp((f + g - c) / eps)
-
-
-def _apply_kernel_xy(x, y, norm_x, norm_y, vec, eps, cost_fn, scale_cost):
-  c = _cost(x, y, norm_x, norm_y, cost_fn, scale_cost)
-  return jnp.dot(jnp.exp(-c / eps), vec)
-
-
-def _transport_from_scalings_xy(
-    x, y, norm_x, norm_y, u, v, eps, cost_fn, scale_cost
-):
-  c = _cost(x, y, norm_x, norm_y, cost_fn, scale_cost)
-  return jnp.exp(-c * scale_cost / eps) * u * v
+  def kernel_matrix(self) -> Optional[jnp.ndarray]:  # noqa: D102
+    return jnp.exp(-self.cost_matrix / self.epsilon)
 
+  @property
+  def shape(self) -> Tuple[int, int]:  # noqa: D102
+    return self.x.shape[0], self.y.shape[0]
 
-def _cost(x, y, norm_x, norm_y, cost_fn, scale_cost):
-  one_line_pairwise = jax.vmap(cost_fn, in_axes=[0, None])
-  cost = norm_x + norm_y + one_line_pairwise(x, y)
-  return cost * scale_cost
+  @property
+  def dtype(self) -> jnp.dtype:  # noqa: D102
+    return self.x.dtype
 
+  @property
+  def is_symmetric(self) -> bool:  # noqa: D102
+    return self.y is None or (
+        jnp.all(self.x.shape == self.y.shape) and jnp.all(self.x == self.y)
+    )
 
-def _apply_cost_xy(x, y, norm_x, norm_y, vec, cost_fn, scale_cost, fn=None):
-  """Apply [num_b, num_a] fn(cost) matrix (or transpose) to vector.
+  @property
+  def is_squared_euclidean(self) -> bool:  # noqa: D102
+    return isinstance(self.cost_fn, costs.SqEuclidean)
 
-  Applies [num_b, num_a] ([num_a, num_b] if axis=1 from `apply_cost`)
-  fn(cost) matrix (or transpose) to vector.
+  @property
+  def can_LRC(self):  # noqa: D102
+    return self.is_squared_euclidean and self._check_LRC_dim
 
-  Args:
-    x: jnp.ndarray [num_a, d], first pointcloud
-    y: jnp.ndarray [num_b, d], second pointcloud
-    norm_x: jnp.ndarray [num_a,], (squared) norm as defined in by cost_fn
-    norm_y: jnp.ndarray [num_b,], (squared) norm as defined in by cost_fn
-    vec: jnp.ndarray [num_a,] ([num_b,] if axis=1 from `apply_cost`) vector
-    cost_fn: a CostFn function between two points in dimension d.
-    scale_cost: scaling factor of the cost matrix.
-    fn: function optionally applied to cost matrix element-wise, before the
-      apply.
+  @property
+  def _check_LRC_dim(self):
+    (n, m), d = self.shape, self.x.shape[1]
+    return n * m > (n + m) * d
 
-  Returns:
-    A jnp.ndarray corresponding to cost x vector
-  """
-  c = _cost(x, y, norm_x, norm_y, cost_fn, scale_cost)
-  return jnp.dot(c, vec) if fn is None else jnp.dot(fn(c), vec)
+  @property
+  def cost_rank(self) -> int:  # noqa: D102
+    return self.x.shape[1]
 
+  @property
+  def batch_size(self) -> Optional[int]:
+    """Batch size for online mode."""
+    if self._batch_size is None:
+      return None
+    n, m = self.shape
+    return min(n, m, self._batch_size)
 
-def _apply_max_xy(x, y, norm_x, norm_y, vec, cost_fn, scale_cost):
-  del vec
-  c = _cost(x, y, norm_x, norm_y, cost_fn, scale_cost)
-  return jnp.max(jnp.abs(c))
+  @property
+  def is_online(self) -> bool:
+    """Whether the cost/kernel is computed on-the-fly."""
+    return self.batch_size is not None
diff --git a/src/ott/neural/methods/monge_gap.py b/src/ott/neural/methods/monge_gap.py
index 140fad4a1..cad282bc8 100644
--- a/src/ott/neural/methods/monge_gap.py
+++ b/src/ott/neural/methods/monge_gap.py
@@ -47,7 +47,7 @@ def monge_gap(
     cost_fn: Optional[costs.CostFn] = None,
     epsilon: Optional[float] = None,
     relative_epsilon: Optional[bool] = None,
-    scale_cost: Union[int, float, Literal["mean", "max_cost", "median"]] = 1.0,
+    scale_cost: Union[float, Literal["mean", "max_cost", "median"]] = 1.0,
     return_output: bool = False,
     **kwargs: Any
 ) -> Union[float, Tuple[float, sinkhorn.SinkhornOutput]]:
@@ -112,7 +112,7 @@ def monge_gap_from_samples(
     cost_fn: Optional[costs.CostFn] = None,
     epsilon: Optional[float] = None,
     relative_epsilon: Optional[bool] = None,
-    scale_cost: Union[int, float, Literal["mean", "max_cost", "median"]] = 1.0,
+    scale_cost: Union[float, Literal["mean", "max_cost", "median"]] = 1.0,
     return_output: bool = False,
     **kwargs: Any
 ) -> Union[float, Tuple[float, sinkhorn.SinkhornOutput]]:
diff --git a/src/ott/problems/linear/potentials.py b/src/ott/problems/linear/potentials.py
index 060c1c7af..9eb08a179 100644
--- a/src/ott/problems/linear/potentials.py
+++ b/src/ott/problems/linear/potentials.py
@@ -59,7 +59,7 @@ def __init__(
     self._f = f
     self._g = g
     assert (
-        not corr or type(cost_fn) == costs.SqEuclidean
+        not corr or type(cost_fn) is costs.SqEuclidean
     ), "Duals in `corr` form can only be used with a squared-Euclidean cost."
     self.cost_fn = cost_fn
     self._corr = corr
diff --git a/src/ott/problems/quadratic/gw_barycenter.py b/src/ott/problems/quadratic/gw_barycenter.py
index 7acf96217..7498710be 100644
--- a/src/ott/problems/quadratic/gw_barycenter.py
+++ b/src/ott/problems/quadratic/gw_barycenter.py
@@ -67,7 +67,7 @@ def __init__(
       y_fused: Optional[jnp.ndarray] = None,
       fused_penalty: float = 1.0,
       gw_loss: Literal["sqeucl", "kl"] = "sqeucl",
-      scale_cost: Union[int, float, Literal["mean", "max_cost"]] = 1.0,
+      scale_cost: Union[float, Literal["mean", "max_cost"]] = 1.0,
       **kwargs: Any,
   ):
     assert y is None or costs is None, "Cannot specify both `y` and `costs`."
diff --git a/src/ott/problems/quadratic/quadratic_problem.py b/src/ott/problems/quadratic/quadratic_problem.py
index e2bd58fa6..b16970aa6 100644
--- a/src/ott/problems/quadratic/quadratic_problem.py
+++ b/src/ott/problems/quadratic/quadratic_problem.py
@@ -162,8 +162,8 @@ def marginal_dependent_cost(
       f1, f2 = self.linear_loss
       tmp1 = apply_cost(geom_xx, marginal_1, axis=1, fn=f1)
       tmp2 = apply_cost(geom_yy, marginal_2, axis=1, fn=f2)
-    x_term = jnp.concatenate((tmp1, jnp.ones_like(tmp1)), axis=1)
-    y_term = jnp.concatenate((jnp.ones_like(tmp2), tmp2), axis=1)
+    x_term = jnp.stack([tmp1, jnp.ones_like(tmp1)], axis=-1)
+    y_term = jnp.stack([jnp.ones_like(tmp2), tmp2], axis=-1)
     return low_rank.LRCGeometry(cost_1=x_term, cost_2=y_term)
 
   def cost_unbalanced_correction(
@@ -350,13 +350,7 @@ def update_lr_linearization(
 
   @property
   def _fused_cost_matrix(self) -> Union[float, jnp.ndarray]:
-    if not self.is_fused:
-      return 0.0
-    geom_xy = self.geom_xy
-
-    if isinstance(geom_xy, pointcloud.PointCloud) and geom_xy.is_online:
-      return geom_xy._compute_cost_matrix() * geom_xy.inv_scale_cost
-    return geom_xy.cost_matrix
+    return self.geom_xy.cost_matrix if self.is_fused else 0.0
 
   @property
   def _is_low_rank_convertible(self) -> bool:
diff --git a/src/ott/solvers/linear/discrete_barycenter.py b/src/ott/solvers/linear/discrete_barycenter.py
index dcfdc1470..2c1277aa4 100644
--- a/src/ott/solvers/linear/discrete_barycenter.py
+++ b/src/ott/solvers/linear/discrete_barycenter.py
@@ -105,6 +105,7 @@ def __call__(
           dual_initialization, weights=weights, axis=0
       )[jnp.newaxis, :]
 
+    # TODO(michalk8): geom.is_symmetric is not static
     if self.debiased and not geom.is_symmetric:
       raise ValueError("Geometry must be symmetric to use debiased option.")
     norm_error = (self.norm_error,)
diff --git a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py
index 0237cb2af..d98cba103 100644
--- a/src/ott/solvers/quadratic/gromov_wasserstein_lr.py
+++ b/src/ott/solvers/quadratic/gromov_wasserstein_lr.py
@@ -381,11 +381,15 @@ def _get_costs(
     tmp = lin_geom.apply_cost(r, axis=1)
     grad_q = tmp * inv_g
     if ot_prob.tau_a != 1.0:  # unbalanced grad
-      grad_q += 2.0 * ot_prob.geom_xx.apply_square_cost(q.sum(1), axis=1)
+      grad_q += 2.0 * ot_prob.geom_xx.apply_square_cost(
+          q.sum(1), axis=1
+      )[:, None]
 
     grad_r = lin_geom.apply_cost(q, axis=0) * inv_g
     if ot_prob.tau_b != 1.0:  # unbalanced grad
-      grad_r += 2.0 * ot_prob.geom_yy.apply_square_cost(r.sum(1), axis=1)
+      grad_r += 2.0 * ot_prob.geom_yy.apply_square_cost(
+          r.sum(1), axis=1
+      )[:, None]
 
     omega_quad = jnp.sum(q * tmp, axis=0)
     grad_g = -omega_quad / (g ** 2)
diff --git a/src/ott/tools/gaussian_mixture/scale_tril.py b/src/ott/tools/gaussian_mixture/scale_tril.py
index b26ddedd4..3fe415842 100644
--- a/src/ott/tools/gaussian_mixture/scale_tril.py
+++ b/src/ott/tools/gaussian_mixture/scale_tril.py
@@ -151,9 +151,7 @@ def _flatten_cov(cov: jnp.ndarray) -> jnp.ndarray:
     x0 = _flatten_cov(self.covariance())
     x1 = _flatten_cov(other.covariance())
     cost_fn = costs.Bures(dimension=dimension)
-    return (cost_fn.norm(x0) + cost_fn.norm(x1) + cost_fn.pairwise(x0, x1))[
-        ...,
-    ]
+    return cost_fn(x0, x1)
 
   def gaussian_map(self, dest_scale: "ScaleTriL") -> jnp.ndarray:
     """Scaling matrix used in transport between 0-mean Gaussians.
diff --git a/src/ott/tools/sinkhorn_divergence.py b/src/ott/tools/sinkhorn_divergence.py
index 8cd15ecd4..135a1c4ba 100644
--- a/src/ott/tools/sinkhorn_divergence.py
+++ b/src/ott/tools/sinkhorn_divergence.py
@@ -231,7 +231,7 @@ def _sinkhorn_divergence(
         momentum=acceleration.Momentum(start=0, value=0.5),
         anderson=None,
     )
-    implicit_diff = kwargs.get("implicit_diff", None)
+    implicit_diff = kwargs.get("implicit_diff")
     if implicit_diff is not None:
       kwargs_symmetric["implicit_diff"] = implicit_diff.replace(symmetric=True)
 
diff --git a/src/ott/utils.py b/src/ott/utils.py
index 2a65a21d7..42c8132a4 100644
--- a/src/ott/utils.py
+++ b/src/ott/utils.py
@@ -15,10 +15,27 @@
 import functools
 import io
 import warnings
-from typing import Any, Callable, NamedTuple, Optional, Tuple
+from collections.abc import Sequence
+from typing import (
+    Any,
+    Callable,
+    List,
+    NamedTuple,
+    Optional,
+    Tuple,
+    TypeVar,
+    Union,
+)
+
+try:
+  from typing import ParamSpec
+except ImportError:
+  from typing_extensions import ParamSpec
 
 import jax
+import jax.numpy as jnp
 import numpy as np
+from jax.interpreters import batching
 
 try:
   from tqdm import tqdm
@@ -31,10 +48,13 @@
     "default_prng_key",
     "default_progress_fn",
     "tqdm_progress_fn",
+    "batched_vmap",
 ]
 
-Status_t = Tuple[np.ndarray, np.ndarray, np.ndarray, NamedTuple]
-IOCallback_t = Callable[[Status_t], None]
+IOStatus = Tuple[np.ndarray, np.ndarray, np.ndarray, NamedTuple]
+IOCallback = Callable[[IOStatus], None]
+P = ParamSpec("P")
+R = TypeVar("R")
 
 
 def register_pytree_node(cls: type) -> type:
@@ -50,10 +70,10 @@ def deprecate(  # noqa: D103
     *,
     version: Optional[str] = None,
     alt: Optional[str] = None,
-    func: Optional[Callable[[Any], Any]] = None
-) -> Callable[[Any], Any]:
+    func: Optional[Callable[P, R]] = None
+) -> Callable[P, R]:
 
-  def wrapper(*args: Any, **kwargs: Any) -> Any:
+  def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
     warnings.warn(msg, category=DeprecationWarning, stacklevel=2)
     return func(*args, **kwargs)
 
@@ -84,7 +104,7 @@ def default_prng_key(rng: Optional[jax.Array] = None) -> jax.Array:
 def default_progress_fn(
     fmt: str = "{iter} / {max_iter} -- {error}",
     stream: Optional[io.TextIOBase] = None,
-) -> IOCallback_t:
+) -> IOCallback:
   """Return a callback that prints the progress when solving
   :mod:`linear problems <ott.problems.linear>`.
 
@@ -123,7 +143,7 @@ def default_progress_fn(
       out = solve_fn(geom, progress_fn=progress_fn)
   """  # noqa: D205
 
-  def progress_callback(status: Status_t) -> None:
+  def progress_callback(status: IOStatus) -> None:
     iteration, inner_iterations, total_iter, errors = _prepare_info(status)
     # Avoid reporting error on each iteration,
     # because errors are only computed every `inner_iterations`.
@@ -142,7 +162,7 @@ def progress_callback(status: Status_t) -> None:
 def tqdm_progress_fn(
     pbar: tqdm,
     fmt: str = "error: {error:0.6e}",
-) -> IOCallback_t:
+) -> IOCallback:
   """Return a callback that updates a progress bar when solving
   :mod:`linear problems <ott.problems.linear>`.
 
@@ -184,7 +204,7 @@ def tqdm_progress_fn(
         out = solve_fn(geom, progress_fn=progress_fn)
   """  # noqa: D205
 
-  def progress_callback(status: Status_t) -> None:
+  def progress_callback(status: IOStatus) -> None:
     iteration, inner_iterations, total_iter, errors = _prepare_info(status)
     # Avoid reporting error on each iteration,
     # because errors are only computed every `inner_iterations`.
@@ -200,7 +220,7 @@ def progress_callback(status: Status_t) -> None:
   return progress_callback
 
 
-def _prepare_info(status: Status_t) -> Tuple[int, int, int, np.ndarray]:
+def _prepare_info(status: IOStatus) -> Tuple[int, int, int, np.ndarray]:
   iteration, inner_iterations, total_iter, state = status
   iteration = int(iteration) + 1
   inner_iterations = int(inner_iterations)
@@ -208,3 +228,197 @@ def _prepare_info(status: Status_t) -> Tuple[int, int, int, np.ndarray]:
   errors = np.array(state.errors).ravel()
 
   return iteration, inner_iterations, total_iter, errors
+
+
+def _canonicalize_axis(axis: int, num_dims: int, *, has_extra_dim: bool) -> int:
+  num_dims -= has_extra_dim
+  if not -num_dims <= axis < num_dims:
+    raise ValueError(
+        f"axis {axis} is out of bounds for array of dimension {num_dims}"
+    )
+  if axis < 0:
+    axis = axis + num_dims
+  return axis
+
+
+def _prepare_axes(
+    name: str,
+    leaves: Any,
+    treedef: Any,
+    axes: Any,
+    *,
+    return_flat: bool,
+    has_extra_dim: bool,
+) -> Any:
+  axes = jax.api_util.flatten_axes(name, treedef, axes, kws=False)
+  assert len(leaves) == len(axes), (len(leaves), len(axes))
+  axes = [
+      axis if axis is None else
+      _canonicalize_axis(axis, jnp.ndim(leaf), has_extra_dim=has_extra_dim)
+      for axis, leaf in zip(axes, leaves)
+  ]
+  return axes if return_flat else treedef.unflatten(axes)
+
+
+def _batch_and_remainder(
+    args: Any,
+    *,
+    batch_size: int,
+    in_axes: Optional[Union[int, Sequence[int], Any]],
+) -> Tuple[Any, Any]:
+  assert batch_size > 0, f"Batch size must be positive, got {batch_size}."
+  leaves, treedef = jax.tree.flatten(args, is_leaf=batching.is_vmappable)
+  in_axes = _prepare_axes(
+      "vmap in_axes",
+      leaves,
+      treedef,
+      in_axes,
+      return_flat=True,
+      has_extra_dim=False,
+  )
+  assert not all(
+      axis is None for axis in in_axes
+  ), "vmap must have at least one non-None value in in_axes"
+
+  num_splits = None
+  has_scan, has_remainder = False, False
+  scan_leaves, remainder_leaves = [], []
+
+  for leaf, axis in zip(leaves, in_axes):
+    if axis is None:
+      scan_leaf = remainder_leaf = leaf
+    else:
+      if num_splits is None:
+        num_splits = leaf.shape[axis] // batch_size
+      else:
+        curr_num_splits = leaf.shape[axis] // batch_size
+        assert num_splits == curr_num_splits, \
+          f"Expected {num_splits} splits, got {curr_num_splits}."
+      num_elems = num_splits * batch_size
+
+      scan_leaf = jax.lax.slice_in_dim(leaf, None, num_elems, axis=axis)
+      new_shape = leaf.shape[:axis] + (-1, batch_size) + leaf.shape[axis + 1:]
+      scan_leaf = scan_leaf.reshape(new_shape)
+      remainder_leaf = jax.lax.slice_in_dim(leaf, num_elems, None, axis=axis)
+
+      has_scan = has_scan or scan_leaf.shape[axis]
+      has_remainder = has_remainder or remainder_leaf.shape[axis]
+
+    scan_leaves.append(scan_leaf)
+    remainder_leaves.append(remainder_leaf)
+
+  scan_tree = treedef.unflatten(scan_leaves) if has_scan else None
+  remainder_tree = treedef.unflatten(
+      remainder_leaves
+  ) if has_remainder else None
+  return scan_tree, remainder_tree
+
+
+def _apply_scan(
+    vmapped_fun: Callable[P, R], in_axes: Optional[Union[int, Sequence[int],
+                                                         Any]]
+) -> Callable[P, R]:
+
+  def num_steps(axes: List[Any], args: Tuple[Any, ...]) -> int:
+    ix = next(ix for ix, axis in enumerate(axes) if axis is not None)
+    leaf, *_ = jax.tree.leaves(args[ix])
+    axis, *_ = jax.tree.leaves(axes[ix])
+    return leaf.shape[axis]
+
+  def select_batch(arg: Any, index: int, *, axis: int) -> Any:
+    fn = functools.partial(
+        jax.lax.dynamic_index_in_dim, index=index, axis=axis, keepdims=False
+    )
+    return jax.tree.map(fn, arg)
+
+  def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+
+    def body_fn(carry: None, index: int) -> Tuple[None, R]:
+      del carry
+      new_args = treedef.unflatten([
+          arg if axis is None else select_batch(arg, index, axis=axis)
+          for arg, axis in zip(leaves, axes)
+      ])
+      return None, vmapped_fun(*new_args, **kwargs)
+
+    leaves, treedef = jax.tree.flatten(args, is_leaf=batching.is_vmappable)
+    axes = _prepare_axes(
+        "vmap in_axes",
+        leaves,
+        treedef,
+        in_axes,
+        return_flat=True,
+        has_extra_dim=True,
+    )
+    n = num_steps(axes, args)
+
+    _, result = jax.lax.scan(body_fn, init=None, xs=jnp.arange(n))
+    return result
+
+  return wrapper
+
+
+def batched_vmap(
+    fun: Callable[P, R],
+    *,
+    batch_size: int,
+    in_axes: Optional[Union[int, Sequence[int], Any]] = 0,
+    out_axes: Any = 0,
+) -> Callable[P, R]:
+  """Batched version of :func:`~jax.vmap`.
+
+  Args:
+    fun: Function to be mapped over additional axes.
+    batch_size: Size of the batch.
+    in_axes: Values specifying which input array axes to map over.
+    out_axes: Values specifying where the mapped axis should appear
+      in the output.
+
+  Returns:
+    The vectorized function.
+  """
+
+  def unbatch(axis: int, x: jnp.ndarray) -> jnp.ndarray:
+    x = jnp.moveaxis(x, 0, axis)
+    return jax.lax.collapse(x, axis, axis + 2)
+
+  def concat(axis: int, x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
+    return jnp.concatenate([x, y], axis=axis)
+
+  @functools.wraps(fun)
+  def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+    batched, remainder = _batch_and_remainder(
+        args, batch_size=batch_size, in_axes=in_axes
+    )
+    has_batched = batched is not None
+    has_remainder = remainder is not None
+
+    if has_batched:
+      batched = batched_fun(*batched, **kwargs)
+      leaves, treedef = jax.tree.flatten(batched, is_leaf=batching.is_vmappable)
+      out_axes_ = _prepare_axes(
+          "vmap out_axes",
+          leaves,
+          treedef,
+          out_axes,
+          return_flat=False,
+          has_extra_dim=True,
+      )
+      batched = jax.tree.map(unbatch, out_axes_, batched)
+    if has_remainder:
+      remainder = vmapped_fun(*remainder, **kwargs)
+
+    if has_batched and has_remainder:
+      return jax.tree.map(concat, out_axes_, batched, remainder)
+    if has_batched:
+      return batched
+    # TODO(michalk8): check for empty arrays
+    return remainder
+
+  if isinstance(in_axes, list):
+    in_axes = tuple(in_axes)
+
+  vmapped_fun = jax.vmap(fun, in_axes=in_axes, out_axes=out_axes)
+  batched_fun = _apply_scan(vmapped_fun, in_axes=in_axes)
+
+  return wrapper
diff --git a/tests/geometry/costs_test.py b/tests/geometry/costs_test.py
index 0f675458b..5a2574ae0 100644
--- a/tests/geometry/costs_test.py
+++ b/tests/geometry/costs_test.py
@@ -39,17 +39,18 @@ def test_cosine(self, rng: jax.Array):
     """Test the cosine cost function."""
     x = jnp.array([0, 0])
     y = jnp.array([0, 0])
-    dist_x_y = costs.Cosine().pairwise(x, y)
+    cost_fn = costs.Cosine()
+    dist_x_y = cost_fn(x, y)
     np.testing.assert_allclose(dist_x_y, 1.0 - 0.0, rtol=1e-5, atol=1e-5)
 
     x = jnp.array([1.0, 0])
     y = jnp.array([1.0, 0])
-    dist_x_y = costs.Cosine().pairwise(x, y)
+    dist_x_y = cost_fn(x, y)
     np.testing.assert_allclose(dist_x_y, 1.0 - 1.0, rtol=1e-5, atol=1e-5)
 
     x = jnp.array([1.0, 0])
     y = jnp.array([-1.0, 0])
-    dist_x_y = costs.Cosine().pairwise(x, y)
+    dist_x_y = cost_fn(x, y)
     np.testing.assert_allclose(dist_x_y, 1.0 - -1.0, rtol=1e-5, atol=1e-5)
 
     n, m, d = 10, 12, 7
@@ -57,24 +58,20 @@ def test_cosine(self, rng: jax.Array):
     x = jax.random.normal(rngs[0], (n, d))
     y = jax.random.normal(rngs[1], (m, d))
 
-    cosine_fn = costs.Cosine()
     normalize = lambda v: v / jnp.sqrt(jnp.sum(v ** 2))
     for i in range(n):
       for j in range(m):
         exp_sim_xi_yj = jnp.sum(normalize(x[i]) * normalize(y[j]))
         exp_dist_xi_yj = 1.0 - exp_sim_xi_yj
         np.testing.assert_allclose(
-            cosine_fn.pairwise(x[i], y[j]),
-            exp_dist_xi_yj,
-            rtol=1e-5,
-            atol=1e-5
+            cost_fn(x[i], y[j]), exp_dist_xi_yj, rtol=1e-5, atol=1e-5
         )
 
-    all_pairs = cosine_fn.all_pairs_pairwise(x, y)
+    all_pairs = cost_fn.all_pairs(x, y)
     for i in range(n):
       for j in range(m):
         np.testing.assert_allclose(
-            cosine_fn.pairwise(x[i], y[j]),
+            cost_fn(x[i], y[j]),
             all_pairs[i, j],
             rtol=1e-5,
             atol=1e-5,
diff --git a/tests/geometry/pointcloud_test.py b/tests/geometry/pointcloud_test.py
index 23cd896e7..10267c1db 100644
--- a/tests/geometry/pointcloud_test.py
+++ b/tests/geometry/pointcloud_test.py
@@ -24,7 +24,7 @@
 
 class NonSymCost(costs.CostFn):
 
-  def pairwise(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
+  def __call__(self, x: jnp.ndarray, y: jnp.ndarray) -> float:
     z = x - y
     return jnp.sum(z ** 2 * (jnp.sign(z) + 0.5) ** 2)
 
@@ -112,11 +112,10 @@ def test_apply_cost_without_norm(self, rng: jax.Array, axis: 1):
     pc = pointcloud.PointCloud(x, y, cost_fn=costs.Cosine())
     arr = jnp.ones((pc.shape[0],)) if axis == 0 else jnp.ones((pc.shape[1],))
 
-    assert pc.cost_fn.norm is None
     with pytest.raises(
         AssertionError, match=r"Cost matrix is not a squared Euclidean\."
     ):
-      _ = pc.vec_apply_cost(arr, axis=axis)
+      _ = pc._apply_sqeucl_cost(arr, axis=axis, scale_cost=1.0)
 
     expected = pc.cost_matrix @ arr if axis == 1 else pc.cost_matrix.T @ arr
     actual = pc.apply_cost(arr, axis=axis).squeeze()
@@ -126,9 +125,7 @@ def test_apply_cost_without_norm(self, rng: jax.Array, axis: 1):
 
 class TestPointCloudCosineConversion:
 
-  @pytest.mark.parametrize(
-      "scale_cost", ["mean", "median", "max_cost", "max_norm", 41]
-  )
+  @pytest.mark.parametrize("scale_cost", ["mean", "median", "max_cost", 41])
   def test_cosine_to_sqeucl_conversion(
       self, rng: jax.Array, scale_cost: Union[str, float]
   ):
@@ -158,9 +155,7 @@ def test_cosine_to_sqeucl_conversion(
         eucl.cost_matrix, cosine.cost_matrix, rtol=1e-6, atol=1e-6
     )
 
-  @pytest.mark.parametrize(
-      "scale_cost", ["mean", "median", "max_cost", "max_norm", 2.0]
-  )
+  @pytest.mark.parametrize("scale_cost", ["mean", "median", "max_cost", 2.0])
   @pytest.mark.parametrize("axis", [0, 1])
   def test_apply_cost_cosine_to_sqeucl(
       self, rng: jax.Array, axis: int, scale_cost: Union[str, float]
diff --git a/tests/geometry/scaling_cost_test.py b/tests/geometry/scaling_cost_test.py
index 4f58c66ea..1cfd7293b 100644
--- a/tests/geometry/scaling_cost_test.py
+++ b/tests/geometry/scaling_cost_test.py
@@ -188,18 +188,6 @@ def apply_sinkhorn(cost1, cost2, scale_cost):
     if scale == "max_cost":
       np.testing.assert_allclose(1.0, geom.cost_matrix.max(), rtol=1e-4)
 
-  @pytest.mark.parametrize("batch_size", [5, 12])
-  def test_max_scale_cost_low_rank_with_batch(self, batch_size: int):
-    """Test max_cost options for low rank with batch_size fixed."""
-
-    geom0 = low_rank.LRCGeometry(
-        self.cost1, self.cost2, scale_cost="max_cost", batch_size=batch_size
-    )
-
-    np.testing.assert_allclose(
-        geom0.inv_scale_cost, 1.0 / jnp.max(self.cost_lr), rtol=1e-4
-    )
-
   def test_max_scale_cost_low_rank_large_array(self):
     """Test max_cost options for large matrices."""
 
diff --git a/tests/geometry/subsetting_test.py b/tests/geometry/subsetting_test.py
index 360b830f9..3a934cb48 100644
--- a/tests/geometry/subsetting_test.py
+++ b/tests/geometry/subsetting_test.py
@@ -78,17 +78,19 @@ def test_mask(
 
     if clazz is geometry.Geometry:
       geom = clazz(cost_matrix=x @ y.T, scale_cost="mean")
-    else:
+    elif clazz is pointcloud.PointCloud:
       geom = clazz(x, y, scale_cost="max_cost", batch_size=5)
+    else:
+      geom = clazz(x, y, scale_cost="max_cost")
     n = geom.shape[0] if src_ixs is None else len(src_ixs)
     m = geom.shape[1] if tgt_ixs is None else len(tgt_ixs)
 
-    if clazz is geometry.Geometry:
-      geom_sub = geom.subset(src_ixs, tgt_ixs)
-    else:
+    if clazz is pointcloud.PointCloud:
       geom_sub = geom.subset(src_ixs, tgt_ixs, batch_size=new_batch_size)
+    else:
+      geom_sub = geom.subset(src_ixs, tgt_ixs)
 
-    assert type(geom_sub) == type(geom)
+    assert type(geom_sub) is type(geom)
     np.testing.assert_array_equal(geom_sub.shape, (n, m))
     assert geom_sub._scale_cost == geom._scale_cost
     if clazz is pointcloud.PointCloud:
diff --git a/tests/solvers/linear/sinkhorn_grid_test.py b/tests/solvers/linear/sinkhorn_grid_test.py
index d73bc124b..aa1eaa1d7 100644
--- a/tests/solvers/linear/sinkhorn_grid_test.py
+++ b/tests/solvers/linear/sinkhorn_grid_test.py
@@ -140,7 +140,7 @@ def test_apply_cost(self, rng: jax.Array):
     )
 
     np.testing.assert_allclose(
-        geom_grid.apply_cost(vec)[:, 0],
+        geom_grid.apply_cost(vec),
         np.dot(geom_mat.cost_matrix.T, vec),
         rtol=1e-4,
         atol=1e-4
diff --git a/tests/solvers/linear/sinkhorn_misc_test.py b/tests/solvers/linear/sinkhorn_misc_test.py
index e15115e61..8d58b5695 100644
--- a/tests/solvers/linear/sinkhorn_misc_test.py
+++ b/tests/solvers/linear/sinkhorn_misc_test.py
@@ -13,10 +13,9 @@
 # limitations under the License.
 from typing import Optional
 
-import chex
-
 import pytest
 
+import chex
 import jax
 import jax.numpy as jnp
 import numpy as np
diff --git a/tests/solvers/quadratic/gw_barycenter_test.py b/tests/solvers/quadratic/gw_barycenter_test.py
index 3775316b0..300f19e41 100644
--- a/tests/solvers/quadratic/gw_barycenter_test.py
+++ b/tests/solvers/quadratic/gw_barycenter_test.py
@@ -78,7 +78,7 @@ def test_gw_barycenter(
         self.random_pc(n, d=self.ndim, rng=rng)
         for n, rng in zip(num_per_segment, rngs)
     ]
-    costs = [pc._compute_cost_matrix() for pc, n in zip(pcs, num_per_segment)]
+    costs = [pc.cost_matrix for pc, n in zip(pcs, num_per_segment)]
     costs, cbs = self.pad_cost_matrices(costs)
     ys = jnp.concatenate([pc.x for pc in pcs])
     bs = jnp.concatenate([jnp.ones(n) / n for n in num_per_segment])
diff --git a/tests/utils_test.py b/tests/utils_test.py
index 192ed59f4..b48729703 100644
--- a/tests/utils_test.py
+++ b/tests/utils_test.py
@@ -11,13 +11,198 @@
 # 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.
-from typing import Optional
+import functools
+from typing import Any, Optional
 
 import pytest
 
+import chex
+import jax
+import jax.numpy as jnp
+import numpy as np
+
 from ott import utils
 
 
+@pytest.mark.fast()
+class TestBatchedVmap:
+
+  @pytest.mark.parametrize("batch_size", [1, 11, 32, 33])
+  def test_batch_size(self, rng: jax.Array, batch_size: int):
+    x = jax.random.normal(rng, (32, 2))
+    gt_fn = jax.jit(jax.vmap(jnp.sum))
+    fn = jax.jit(utils.batched_vmap(jnp.sum, batch_size=batch_size))
+
+    np.testing.assert_array_equal(gt_fn(x), fn(x))
+
+  def test_pytree(self, rng: jax.Array):
+
+    def f(x: Any) -> jnp.ndarray:
+      return x["foo"]["bar"].std() + x["baz"].mean(
+      ) + x["quux"][0] * x["quux"][1]
+
+    rng1, rng2, rng3 = jax.random.split(rng, 3)
+    x = {
+        "foo": {
+            "bar": jax.random.normal(rng1, (5, 3, 3))
+        },
+        "baz": jax.random.normal(rng2, (2, 5)),
+        "quux": (2.0, 3.0),
+    }
+    in_axes = [{"foo": {"bar": 0}, "baz": 1, "quux": (None, None)}]
+
+    gt_fn = jax.vmap(f, in_axes=in_axes)
+    fn = utils.batched_vmap(f, in_axes=in_axes, batch_size=2)
+
+    np.testing.assert_array_equal(gt_fn(x), fn(x))
+
+  @pytest.mark.parametrize("batch_size", [1, 7, 67, 133])
+  @pytest.mark.parametrize("in_axes", [0, 1, -1, -2, [0, None], (0, -2)])
+  def test_in_axes(self, rng: jax.Array, in_axes: Any, batch_size: int):
+
+    def f(x: jnp.ndarray, y: jnp.ndarray) -> jnp.ndarray:
+      x = jnp.atleast_2d(x)
+      y = jnp.atleast_2d(y)
+      return jnp.dot(x, y.T)
+
+    rng1, rng2 = jax.random.split(rng, 2)
+    x = jax.random.normal(rng1, (133, 71)) + 10.0
+    y = jax.random.normal(rng2, (133, 71))
+
+    gt_fn = jax.jit(jax.vmap(f, in_axes=in_axes))
+    fn = jax.jit(utils.batched_vmap(f, batch_size=batch_size, in_axes=in_axes))
+
+    np.testing.assert_allclose(gt_fn(x, y), fn(x, y), rtol=1e-4, atol=1e-4)
+
+  @pytest.mark.parametrize(
+      "in_axes", [((0, None, None),), (({
+          "foo": 1,
+          "baz": -1
+      }, -1, None),), [
+          ({
+              "foo": {
+                  "bar": 0
+              },
+              "baz": -2
+          }, None, None),
+      ]]
+  )
+  def test_in_axes_pytree(self, rng: jax.Array, in_axes: Any):
+
+    def f(tree: Any) -> jnp.ndarray:
+      x = tree[0]["foo"]["bar"]
+      y = tree[0]["baz"]
+      z, ((v,), w) = tree[1], tree[2]
+      return x.mean() - y.std() + z.sum() - y * (v + w)
+
+    batch_size = 1
+    rng1, rng2, rng3 = jax.random.split(rng, 3)
+    tree = (
+        {
+            "foo": {
+                "bar": jax.random.normal(rng1, (13, 5))
+            },
+            "baz": jax.random.normal(rng2, (13, 5))
+        },
+        jax.random.normal(rng3, (10, 5)),
+        ((2,), 13.0),
+    )
+
+    gt_fn = jax.jit(jax.vmap(f, in_axes=in_axes))
+    fn = jax.jit(utils.batched_vmap(f, in_axes=in_axes, batch_size=batch_size))
+
+    chex.assert_trees_all_close(gt_fn(tree), fn(tree), rtol=1e-4, atol=1e-4)
+
+  @pytest.mark.parametrize("out_axes", [0, 1, 2, -1, -2, -3])
+  def test_out_axes(self, rng: jax.Array, out_axes: int):
+
+    def f(x: jnp.ndarray, y: jnp.ndarray) -> Any:
+      return (x.sum() + y.sum()).reshape(1, 1)
+
+    rng1, rng2 = jax.random.split(rng, 2)
+    x = jax.random.normal(rng1, (31, 13))
+    y = jax.random.normal(rng2, (31, 6)) - 15.0
+
+    gt_fn = jax.vmap(f, out_axes=out_axes)
+    fn = utils.batched_vmap(f, batch_size=5, out_axes=out_axes)
+
+    chex.assert_trees_all_equal(gt_fn(x, y), fn(x, y))
+
+  @pytest.mark.parametrize(
+      "out_axes", [0, (0, 0, 1), (0, {
+          "x": {
+              "y": 1
+          }
+      }, (1,))]
+  )
+  def test_out_axes_pytree(self, rng: jax.Array, out_axes: Any):
+
+    def f(x: jnp.ndarray) -> Any:
+      z = jnp.arange(9).reshape(3, 3)
+      return x.mean(), {"x": {"y": jnp.ones(13)}}, (z,)
+
+    x = jax.random.normal(rng, (13, 5))
+
+    fn = utils.batched_vmap(f, batch_size=12, out_axes=out_axes)
+    gt_fn = jax.vmap(f, out_axes=out_axes)
+
+    chex.assert_trees_all_equal(gt_fn(x), fn(x))
+
+  @pytest.mark.parametrize("n", [16, 7])
+  @pytest.mark.parametrize("batch_size", [1, 4, 5, 7, 16])
+  def test_max_traces(self, rng: jax.Array, batch_size: int, n: int):
+    max_traces = 1 + (n % batch_size != 0)
+
+    @jax.jit
+    @functools.partial(utils.batched_vmap, batch_size=batch_size)
+    @chex.assert_max_traces(n=max_traces)
+    def fn(x: jnp.ndarray) -> jnp.ndarray:
+      return x.sum()
+
+    chex.clear_trace_counter()
+    x = jax.random.normal(rng, (n, 3))
+
+    np.testing.assert_array_equal(fn(x), x.sum(1))
+
+  @pytest.mark.limit_memory("15MB")
+  def test_vmap_max_memory(self, rng: jax.Array):
+    n, m, d = 2 ** 16, 2 ** 11, 3
+    rng, rng_data = jax.random.split(rng, 2)
+    y = jax.random.normal(rng_data, (m, d))
+
+    fn = utils.batched_vmap(
+        lambda x, y: jnp.dot(y, x).sum(), in_axes=[0, None], batch_size=128
+    )
+    fn = jax.jit(fn)
+
+    rng, rng_data = jax.random.split(rng, 2)
+    x = jax.random.normal(rng_data, (n, d))
+    res = fn(x, y)
+    assert res.shape == (n,)
+
+  @pytest.mark.parametrize("batch_size", [1, 5, 10])
+  def test_inconsistent_array_sizes(self, rng: jax.Array, batch_size: int):
+    rng1, rng2 = jax.random.split(rng, 2)
+
+    x = jax.random.normal(rng1, (5, 2))
+    y = jax.random.normal(rng2, (10, 2))
+
+    gt_fn = jax.vmap(lambda x, y: (x + y).sum(), in_axes=0)
+    fn = utils.batched_vmap(
+        lambda x, y: (x + y).sum(), batch_size=batch_size, in_axes=0
+    )
+
+    with pytest.raises(ValueError, match=r"^vmap got inconsistent"):
+      _ = gt_fn(x, y)
+    num_splits = x.shape[0] // batch_size
+    wrong_num_splits = y.shape[0] // batch_size
+    with pytest.raises(
+        AssertionError,
+        match=rf"^Expected {num_splits} splits, got {wrong_num_splits}\."
+    ):
+      _ = fn(x, y)
+
+
 @pytest.mark.parametrize(("version", "msg"), [(None, "foo, bar, baz"),
                                               ("quux", None)])
 def test_deprecation_warning(version: Optional[str], msg: Optional[str]):