diff --git a/CHANGELOG.md b/CHANGELOG.md
index eddbea39..84f93e5b 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,7 @@
 - Improves handling of Conda based environments in metadata collection.
 - Adds additional options to `Client.get_runs`.
 - Added ability to include environment variables within metadata for runs.
+- Added new feature allowing users to log tensors as multidimensional metrics after defining a grid.
 
 ## [v2.1.2](https://github.com/simvue-io/client/releases/tag/v2.1.2) - 2025-06-25
 
diff --git a/poetry.lock b/poetry.lock
index 38084522..9b0938a3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -34,116 +34,103 @@ tests-mypy = ["mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" a
 
 [[package]]
 name = "certifi"
-version = "2025.7.14"
+version = "2025.8.3"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
 files = [
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-    {file = "certifi-2025.7.14.tar.gz", hash = "sha256:8ea99dbdfaaf2ba2f9bac77b9249ef62ec5218e7c2b2e903378ed5fccf765995"},
+    {file = "certifi-2025.8.3-py3-none-any.whl", hash = "sha256:f6c12493cfb1b06ba2ff328595af9350c65d6644968e5d3a2ffd78699af217a5"},
+    {file = "certifi-2025.8.3.tar.gz", hash = "sha256:e564105f78ded564e3ae7c923924435e1daa7463faeab5bb932bc53ffae63407"},
 ]
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.2"
+version = "3.4.3"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
 groups = ["main"]
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@@ -181,7 +168,7 @@ description = "Python library for calculating contours of 2D quadrilateral grids
 optional = true
 python-versions = ">=3.10"
 groups = ["main"]
-markers = "extra == \"plot\""
+markers = "python_version == \"3.10\" and extra == \"plot\""
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 test-no-images = ["pytest", "pytest-cov", "pytest-rerunfailures", "pytest-xdist", "wurlitzer"]
 
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 [package.dependencies]
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+    {file = "numpy-2.3.2-pp311-pypy311_pp73-macosx_10_15_x86_64.whl", hash = "sha256:14a91ebac98813a49bc6aa1a0dfc09513dcec1d97eaf31ca21a87221a1cdcb15"},
+    {file = "numpy-2.3.2-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:71669b5daae692189540cffc4c439468d35a3f84f0c88b078ecd94337f6cb0ec"},
+    {file = "numpy-2.3.2-pp311-pypy311_pp73-macosx_14_0_arm64.whl", hash = "sha256:69779198d9caee6e547adb933941ed7520f896fd9656834c300bdf4dd8642712"},
+    {file = "numpy-2.3.2-pp311-pypy311_pp73-macosx_14_0_x86_64.whl", hash = "sha256:2c3271cc4097beb5a60f010bcc1cc204b300bb3eafb4399376418a83a1c6373c"},
+    {file = "numpy-2.3.2-pp311-pypy311_pp73-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:8446acd11fe3dc1830568c941d44449fd5cb83068e5c70bd5a470d323d448296"},
+    {file = "numpy-2.3.2-pp311-pypy311_pp73-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:aa098a5ab53fa407fded5870865c6275a5cd4101cfdef8d6fafc48286a96e981"},
+    {file = "numpy-2.3.2-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:6936aff90dda378c09bea075af0d9c675fe3a977a9d2402f95a87f440f59f619"},
+    {file = "numpy-2.3.2.tar.gz", hash = "sha256:e0486a11ec30cdecb53f184d496d1c6a20786c81e55e41640270130056f8ee48"},
 ]
 
 [[package]]
@@ -1364,15 +1546,15 @@ xmp = ["defusedxml"]
 
 [[package]]
 name = "plotly"
-version = "6.2.0"
+version = "6.3.0"
 description = "An open-source interactive data visualization library for Python"
 optional = true
 python-versions = ">=3.8"
 groups = ["main"]
 markers = "extra == \"plot\""
 files = [
-    {file = "plotly-6.2.0-py3-none-any.whl", hash = "sha256:32c444d4c940887219cb80738317040363deefdfee4f354498cc0b6dab8978bd"},
-    {file = "plotly-6.2.0.tar.gz", hash = "sha256:9dfa23c328000f16c928beb68927444c1ab9eae837d1fe648dbcda5360c7953d"},
+    {file = "plotly-6.3.0-py3-none-any.whl", hash = "sha256:7ad806edce9d3cdd882eaebaf97c0c9e252043ed1ed3d382c3e3520ec07806d4"},
+    {file = "plotly-6.3.0.tar.gz", hash = "sha256:8840a184d18ccae0f9189c2b9a2943923fd5cae7717b723f36eef78f444e5a73"},
 ]
 
 [package.dependencies]
@@ -2131,14 +2313,14 @@ files = [
 
 [[package]]
 name = "types-requests"
-version = "2.32.4.20250611"
+version = "2.32.4.20250809"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "types_requests-2.32.4.20250611-py3-none-any.whl", hash = "sha256:ad2fe5d3b0cb3c2c902c8815a70e7fb2302c4b8c1f77bdcd738192cdb3878072"},
-    {file = "types_requests-2.32.4.20250611.tar.gz", hash = "sha256:741c8777ed6425830bf51e54d6abe245f79b4dcb9019f1622b773463946bf826"},
+    {file = "types_requests-2.32.4.20250809-py3-none-any.whl", hash = "sha256:f73d1832fb519ece02c85b1f09d5f0dd3108938e7d47e7f94bbfa18a6782b163"},
+    {file = "types_requests-2.32.4.20250809.tar.gz", hash = "sha256:d8060de1c8ee599311f56ff58010fb4902f462a1470802cf9f6ed27bc46c4df3"},
 ]
 
 [package.dependencies]
diff --git a/simvue/api/objects/__init__.py b/simvue/api/objects/__init__.py
index 36950de4..78a99131 100644
--- a/simvue/api/objects/__init__.py
+++ b/simvue/api/objects/__init__.py
@@ -8,28 +8,54 @@
 
 """
 
-from .administrator import Tenant as Tenant, User as User
+from .administrator import Tenant, User
 from .alert import (
-    Alert as Alert,
-    EventsAlert as EventsAlert,
-    MetricsThresholdAlert as MetricsThresholdAlert,
-    MetricsRangeAlert as MetricsRangeAlert,
-    UserAlert as UserAlert,
+    Alert,
+    EventsAlert,
+    MetricsThresholdAlert,
+    MetricsRangeAlert,
+    UserAlert,
 )
 from .storage import (
-    S3Storage as S3Storage,
-    FileStorage as FileStorage,
-    Storage as Storage,
+    S3Storage,
+    FileStorage,
+    Storage,
 )
 from .artifact import (
-    FileArtifact as FileArtifact,
-    ObjectArtifact as ObjectArtifact,
-    Artifact as Artifact,
+    FileArtifact,
+    ObjectArtifact,
+    Artifact,
 )
 
-from .stats import Stats as Stats
-from .run import Run as Run
-from .tag import Tag as Tag
-from .folder import Folder as Folder, get_folder_from_path as get_folder_from_path
+from .stats import Stats
+from .run import Run
+from .tag import Tag
+from .folder import Folder, get_folder_from_path
 from .events import Events as Events
 from .metrics import Metrics as Metrics
+from .grids import Grid, GridMetrics
+
+__all__ = [
+    "Grid",
+    "GridMetrics",
+    "Metrics",
+    "Events",
+    "get_folder_from_path",
+    "Folder",
+    "Stats",
+    "Run",
+    "Tag",
+    "Artifact",
+    "FileArtifact",
+    "ObjectArtifact",
+    "S3Storage",
+    "FileStorage",
+    "Storage",
+    "MetricsRangeAlert",
+    "MetricsThresholdAlert",
+    "UserAlert",
+    "EventsAlert",
+    "Alert",
+    "Tenant",
+    "User",
+]
diff --git a/simvue/api/objects/base.py b/simvue/api/objects/base.py
index cb178e44..827555bf 100644
--- a/simvue/api/objects/base.py
+++ b/simvue/api/objects/base.py
@@ -443,10 +443,21 @@ def count(cls, **kwargs) -> int:
 
     @classmethod
     def _get_all_objects(
-        cls, offset: int | None, count: int | None, **kwargs
+        cls,
+        offset: int | None,
+        count: int | None,
+        endpoint: str | None = None,
+        **kwargs,
     ) -> typing.Generator[dict, None, None]:
         _class_instance = cls(_read_only=True)
-        _url = f"{_class_instance._base_url}"
+
+        # Allow the possibility of paginating a URL that is not the
+        # main class endpoint
+        _url = (
+            f"{_class_instance._user_config.server.url}/{endpoint}"
+            if endpoint
+            else f"{_class_instance._base_url}"
+        )
 
         _label = _class_instance.__class__.__name__.lower()
         if _label.endswith("s"):
diff --git a/simvue/api/objects/grids.py b/simvue/api/objects/grids.py
new file mode 100644
index 00000000..149db403
--- /dev/null
+++ b/simvue/api/objects/grids.py
@@ -0,0 +1,302 @@
+"""
+Simvue Server Grid
+==================
+
+Contains a class for remotely connecting to a Simvue grid, or defining
+a new grid given relevant arguments.
+
+"""
+
+import http
+import numpy
+import json
+import typing
+
+import pydantic
+
+from simvue.api.url import URL
+from simvue.models import GridMetricSet
+
+
+from .base import SimvueObject, write_only
+from simvue.api.request import (
+    get as sv_get,
+    put as sv_put,
+    post as sv_post,
+    get_json_from_response,
+)
+
+try:
+    from typing import Self
+except ImportError:
+    from typing_extensions import Self
+
+__all__ = ["Grid"]
+
+
+def check_ordered_array(axis_ticks: list[float]) -> bool:
+    """Returns if array is ordered or reverse ordered."""
+    if not isinstance(axis_ticks[0], float):
+        raise ValueError("Ordering can only be checked on a 1D array")
+    _array = numpy.array(axis_ticks)
+    return numpy.all(numpy.sort(_array) == _array) or numpy.all(
+        numpy.reversed(numpy.sort(_array)) == _array
+    )
+
+
+class Grid(SimvueObject):
+    """Class for retrieving grids stored on the server."""
+
+    @pydantic.validate_call
+    @write_only
+    def attach_to_run(self, run_id: str) -> None:
+        """Attach this grid to a given run."""
+        _response = sv_put(
+            url=f"{self.run_data_url(run_id)}",
+            headers=self._headers,
+        )
+
+        return get_json_from_response(
+            expected_status=[http.HTTPStatus.OK],
+            scenario=f"adding grid '{self._identifier}' to run '{run_id}'",
+            response=_response,
+        )
+
+    @property
+    def grid(self) -> list[list[float]]:
+        return self._get_attribute("grid")
+
+    @property
+    def name(self) -> str:
+        return self._get_attribute("name")
+
+    @classmethod
+    @pydantic.validate_call(config={"arbitrary_types_allowed": True})
+    def new(
+        cls,
+        *,
+        name: str,
+        grid: list[list[float]],
+        labels: list[str],
+        offline: bool = False,
+        **kwargs,
+    ) -> Self:
+        """Create a new Grid on the Simvue server.
+
+        Parameters
+        ----------
+        name : str
+            name for this grid.
+        grid : list[list[float]]
+            define a grid as a list of axes containing tick values.
+        labels : list[str]
+            label each of the axes defined.
+        offline: bool, optional
+            whether to create in offline mode, default is False.
+
+        Returns
+        -------
+        Metrics
+            metrics object
+        """
+        if len(grid) < 1:
+            raise ValueError("Invalid argument for 'grid'")
+
+        if len(labels) != len(set(labels)):
+            raise ValueError("Labels must be unique.")
+
+        if len(labels) != len(grid):
+            raise AssertionError(
+                "Length of argument 'labels' must match first "
+                f"grid dimension {len(grid)}."
+            )
+
+        for i, axis in enumerate(grid):
+            if not check_ordered_array(axis):
+                raise ValueError(f"Axis {i} has unordered values.")
+
+        return Grid(
+            grid=grid,
+            labels=labels,
+            name=name,
+            _read_only=False,
+            _offline=offline,
+        )
+
+    @property
+    def dimensions(self) -> tuple[int, int]:
+        """Returns the grid dimensions."""
+        return len(self.grid)
+
+    def run_data_url(self, run_id: str) -> URL:
+        """Returns the URL for grid data for a specific run."""
+        return URL(
+            f"{self._user_config.server.url}/runs/{run_id}/grids/{self._identifier}"
+        )
+
+    @pydantic.validate_call
+    def get_run_values(self, step: int) -> dict:
+        """Retrieve values for this grid from the server for a given run at a given step.
+
+        Parameters
+        ----------
+        run_id : str
+            run to return grid metrics for.
+        step : int
+            time step to retrieve values for.
+
+        Returns
+        ------
+        dict[str, list[dict[str, float]]
+            dictionary containing values from this for the run at specified step.
+        """
+        _response = sv_get(
+            url=f"{self.run_data_url / 'values'}",
+            headers=self._headers,
+            params={"step": step},
+        )
+
+        return get_json_from_response(
+            response=_response,
+            expected_status=[http.HTTPStatus.OK],
+            expected_type=dict,
+            scenario=f"Retrieving grid values for run '{self._run_id}' at step {step}",
+        )
+
+    @pydantic.validate_call
+    def get_run_span(self, run_id: str) -> dict:
+        """Retrieve span for this grid from the server for a given run.
+
+        Parameters
+        ----------
+        run_id : str
+            run to return grid metrics for.
+
+        Returns
+        ------
+        dict[str, list[dict[str, float]]
+            dictionary containing span from this for the run at specified step.
+        """
+        _response = sv_get(
+            url=f"{self.run_data_url / 'span'}",
+            headers=self._headers,
+        )
+
+        return get_json_from_response(
+            response=_response,
+            expected_status=[http.HTTPStatus.OK],
+            expected_type=dict,
+            scenario=f"Retrieving grid span for run '{run_id}'",
+        )
+
+    @classmethod
+    def get(
+        cls,
+        *_,
+        **__,
+    ) -> typing.Generator[tuple[str, Self | None], None, None]:
+        raise NotImplementedError
+
+
+class GridMetrics(SimvueObject):
+    def __init__(
+        self,
+        _read_only: bool = True,
+        _local: bool = False,
+        **kwargs,
+    ) -> None:
+        """Initialise a GridMetrics object instance."""
+        self._label = "grid"
+        super().__init__(_read_only=_read_only, _local=_local, **kwargs)
+        self._run_id = self._staging.get("run")
+        self._is_set = True
+
+    @staticmethod
+    def run_grids_endpoint(run: str | None = None) -> URL:
+        """Returns the URL for grids for a specific run."""
+        return URL(f"runs/{run}/grids/")
+
+    def _get_attribute(self, attribute: str, *default) -> typing.Any:
+        return super()._get_attribute(
+            attribute,
+            *default,
+            url=f"{self._user_config.server.url}/{self.run_grids_endpoint(self._run_id)}",
+        )
+
+    @classmethod
+    @pydantic.validate_call
+    def new(
+        cls, *, run: str, metrics: list[GridMetricSet], offline: bool = False, **kwargs
+    ) -> Self:
+        """Create a new GridMetrics object for n-dimensional metric submission.
+
+        Parameters
+        ----------
+        run: str
+            identifier for the run to attach metrics to.
+        metrics: list[GridMetricSet]
+            set of tensor-based metrics to attach to run.
+        offline: bool, optional
+            whether to create in offline mode, default is False.
+
+        Returns
+        -------
+        Metrics
+            metrics object
+        """
+        return GridMetrics(
+            run=run,
+            metrics=[metric.model_dump() for metric in metrics],
+            _read_only=False,
+            _offline=offline,
+        )
+
+    @classmethod
+    @pydantic.validate_call
+    def get(
+        cls,
+        runs: list[str],
+        *,
+        spans: bool = False,
+        **kwargs,
+    ) -> typing.Generator[dict[str, dict[str, list[dict[str, float]]]], None, None]:
+        """Retrieve tensor-metrics from the server for a given set of runs.
+
+        Parameters
+        ----------
+        runs : list[str]
+            list of runs to return metrics for.
+        spans : bool, optional
+            return spans information
+
+        Yields
+        ------
+        dict[str,  dict[str, list[dict[str, float]]]
+            metric set object containing metrics for run.
+        """
+        for run in runs:
+            yield from cls._get_all_objects(
+                runs=json.dumps(runs),
+                endpoint=cls.run_grids_endpoint(run),
+                **kwargs,
+            )
+
+    def commit(self) -> dict | None:
+        _run_staging = self._staging.pop("values", None)
+        self._log_values(self._staging["metrics"])
+
+    @pydantic.validate_call
+    @write_only
+    def _log_values(self, metrics: list[GridMetricSet]) -> None:
+        _response = sv_post(
+            url=f"{self._user_config.server.url}/{self.run_grids_endpoint(self._run_id)}",
+            headers=self._headers,
+            data=metrics,
+            params={},
+        )
+
+        get_json_from_response(
+            expected_status=[http.HTTPStatus.OK],
+            scenario=f"adding tensor values to run '{self._run_id}'",
+            response=_response,
+        )
diff --git a/simvue/models.py b/simvue/models.py
index 79aa2751..887be567 100644
--- a/simvue/models.py
+++ b/simvue/models.py
@@ -1,5 +1,6 @@
 import datetime
 import typing
+import numpy
 import pydantic
 
 
@@ -46,6 +47,30 @@ def timestamp_str(cls, value: str) -> str:
         return value
 
 
+class GridMetricSet(pydantic.BaseModel):
+    model_config = pydantic.ConfigDict(arbitrary_types_allowed=True)
+    time: pydantic.NonNegativeFloat | pydantic.NonNegativeInt
+    timestamp: str
+    step: pydantic.NonNegativeInt
+    array: numpy.ndarray
+    grid: str
+
+    @pydantic.field_validator("timestamp", mode="after")
+    @classmethod
+    def timestamp_str(cls, value: str) -> str:
+        try:
+            datetime.datetime.strptime(value, DATETIME_FORMAT)
+        except ValueError as e:
+            raise AssertionError(
+                f"Invalid timestamp, expected form '{DATETIME_FORMAT}'"
+            ) from e
+        return value
+
+    @pydantic.field_serializer("array", when_used="always")
+    def serialize_array(self, value: numpy.ndarray, *_) -> list:
+        return value.tolist()
+
+
 class EventSet(pydantic.BaseModel):
     message: str
     timestamp: str
diff --git a/simvue/run.py b/simvue/run.py
index 6854da9a..2a07fa0f 100644
--- a/simvue/run.py
+++ b/simvue/run.py
@@ -25,13 +25,15 @@
 import typing
 import warnings
 import uuid
+import numpy
 import randomname
 import click
 import psutil
 
 from simvue.api.objects.alert.fetch import Alert
 from simvue.api.objects.folder import Folder
-from simvue.exception import SimvueRunError
+from simvue.api.objects.grids import GridMetrics
+from simvue.exception import ObjectNotFoundError, SimvueRunError
 from simvue.utilities import prettify_pydantic
 
 
@@ -59,6 +61,7 @@
     EventsAlert,
     Events,
     Metrics,
+    Grid,
 )
 
 try:
@@ -165,6 +168,7 @@ def __init__(
 
         self._folder: Folder | None = None
         self._term_color: bool = True
+        self._grids: dict[str, str] = {}
         self._suppress_errors: bool = False
         self._queue_blocking: bool = False
         self._status: (
@@ -475,7 +479,7 @@ def _create_dispatch_callback(
 
         def _dispatch_callback(
             buffer: list[typing.Any],
-            category: typing.Literal["events", "metrics"],
+            category: typing.Literal["events", "metrics_tensor", "metrics_regular"],
         ) -> None:
             if category == "events":
                 _events = Events.new(
@@ -484,6 +488,13 @@ def _dispatch_callback(
                     events=buffer,
                 )
                 return _events.commit()
+            elif category == "metrics_tensor":
+                _grid_metrics = GridMetrics.new(
+                    run=self.id,
+                    metrics=buffer,
+                    offline=self._user_config.run.mode == "offline",
+                )
+                return _grid_metrics.commit()
             else:
                 _metrics = Metrics.new(
                     run=self.id,
@@ -539,7 +550,7 @@ def _start(self) -> bool:
             self._dispatcher = Dispatcher(
                 mode=self._dispatch_mode,
                 termination_trigger=self._shutdown_event,
-                object_types=["events", "metrics"],
+                object_types=["events", "metrics_regular", "metrics_tensor"],
                 callback=self._create_dispatch_callback(),
             )
 
@@ -1286,6 +1297,7 @@ def log_event(self, message: str, timestamp: str | None = None) -> bool:
     def _add_metrics_to_dispatch(
         self,
         metrics: dict[str, int | float],
+        *,
         step: int | None = None,
         time: float | None = None,
         timestamp: str | None = None,
@@ -1322,25 +1334,149 @@ def _add_metrics_to_dispatch(
             "timestamp": timestamp if timestamp is not None else self.time_stamp,
             "step": step if step is not None else self._step,
         }
-        self._dispatcher.add_item(_data, "metrics", self._queue_blocking)
+        self._dispatcher.add_item(_data, "metrics_regular", self._queue_blocking)
 
         return True
 
+    def _add_tensors_to_dispatch(
+        self,
+        tensors: dict[str, int | float],
+        *,
+        step: int | None = None,
+        time: float | None = None,
+        timestamp: str | None = None,
+        join_on_fail: bool = True,
+    ) -> bool:
+        for tensor, array in tensors.items():
+            _data: dict[str, typing.Any] = {
+                "array": array,
+                "time": time if time is not None else self.duration,
+                "timestamp": timestamp if timestamp is not None else self.time_stamp,
+                "step": step if step is not None else self._step,
+                "grid": self._grids[tensor]["id"],
+            }
+
+            self._dispatcher.add_item(_data, "metrics_tensor", self._queue_blocking)
+
+        return True
+
+    def _add_values_to_dispatch(
+        self,
+        values: dict[str, int | float | numpy.ndarray],
+        *,
+        step: int | None = None,
+        time: float | None = None,
+        timestamp: str | None = None,
+        join_on_fail: bool = True,
+    ) -> bool:
+        # If there are no metrics to log just ignore
+        if self._user_config.run.mode == "disabled":
+            return True
+
+        if not values:
+            return True
+
+        if not self._active:
+            self._error("Run is not active", join_on_fail)
+            return False
+
+        if self._status != "running":
+            self._error(
+                "Cannot log metrics when not in the running state", join_on_fail
+            )
+            return False
+
+        if timestamp and not validate_timestamp(timestamp):
+            self._error("Invalid timestamp format", join_on_fail)
+            return False
+
     @skip_if_failed("_aborted", "_suppress_errors", False)
     @check_run_initialised
-    @pydantic.validate_call
+    @pydantic.validate_call(config={"arbitrary_types_allowed": True})
+    def assign_metric_to_grid(
+        self,
+        *,
+        metric_name: str,
+        grid_name: str | None = None,
+        axes_ticks: numpy.ndarray | list[list[float]] | None = None,
+        axes_labels: list[str] | None = None,
+    ) -> bool:
+        """Assign a metric to a new/existing tensor-based metric grid.
+
+        Method assigns metric to a grid, if the grid does not
+        exist the additional arguments specify the grid definitions,
+        and that grid is created. If the arguments are specified when
+        the grid does exist, they are ignored and the original is used.
+
+        Parameters
+        ----------
+        metric_name : str
+            name of the metric to assign.
+        grid_name : str | None, optional
+            a unique name for this grid, if not specified the metric name is used.
+        axes_ticks : list[list[float]] | NDArray, optional
+            the tick positions for each axis.
+        axes_labels : list[str], optional
+            the name for each axis
+
+        Returns
+        -------
+        bool
+            if the assignment was successful.
+        """
+        if isinstance(axes_ticks, numpy.ndarray):
+            axes_ticks = axes_ticks.tolist()
+
+        grid_name = grid_name or metric_name
+
+        if grid_name not in self._grids and (axes_labels is None or axes_ticks is None):
+            self._error(f"Grid '{grid_name}' is not defined.")
+            return False
+
+        if axes_labels is not None and axes_ticks is not None:
+            _new_grid = Grid.new(name=grid_name, grid=axes_ticks, labels=axes_labels)
+            _new_grid.commit()
+
+            # Add entry for both the grid itself and the metric
+            self._grids[grid_name] = {
+                "id": _new_grid.id,
+                "dimensionality": len(axes_labels),
+            }
+            self._grids[metric_name] = {
+                "id": _new_grid.id,
+                "dimensionality": len(axes_labels),
+            }
+
+        if grid_name not in self._grids:
+            self._error(f"Expected grid for '{grid_name}' but none defined.")
+            return False
+
+        try:
+            _grid_attach = Grid(identifier=self._grids[grid_name]["id"])
+            _grid_attach.read_only(False)
+            _grid_attach.attach_to_run(self.id)
+            self._grids[metric_name] = self._grids[grid_name]
+        except (RuntimeError, ObjectNotFoundError) as e:
+            self._error(
+                f"Failed to attach run '{self.id}' to grid '{grid_name}': {e.args[0]}"
+            )
+        return True
+
+    @skip_if_failed("_aborted", "_suppress_errors", False)
+    @check_run_initialised
+    @pydantic.validate_call(config={"arbitrary_types_allowed": True})
     def log_metrics(
         self,
-        metrics: dict[MetricKeyString, int | float],
+        metrics: dict[MetricKeyString, int | float | numpy.ndarray],
         step: int | None = None,
         time: float | None = None,
         timestamp: str | None = None,
     ) -> bool:
-        """Log metrics to Simvue server
+        """Log metrics to Simvue server.
 
         Parameters
         ----------
-        metrics : dict[str, int | float]
+        metrics : dict[str, int | float | numpy.ndarray]
             set of metrics to upload to server for this run
         step : int, optional
             manually specify the step index for this log, by default None
@@ -1352,13 +1488,71 @@ def log_metrics(
         Returns
         -------
         bool
-            if the metric log was succcessful
+            if the metric log was successful
         """
-        add_dispatch = self._add_metrics_to_dispatch(
-            metrics, step=step, time=time, timestamp=timestamp
+        # TODO: When metrics and grids are combined into a single entity
+        # this can be removed. For now need to separate tensor based metrics
+        # from regular
+        _tensor_metrics: list[numpy.ndarray] = {}
+        _regular_metrics: list[numpy.ndarray] = {}
+
+        # Classify metrics into regular and tensor based
+        for label, metric in metrics.items():
+            if isinstance(metric, numpy.ndarray):
+                if label not in self._grids:
+                    logger.warning(
+                        f"Metric '{label}' is not assigned to a grid, "
+                        "using default axis range [0, 1] for all axes "
+                        "and assuming constant interval."
+                    )
+                    _axes_ticks = [numpy.linspace(0, 1, n) for n in metric.shape]
+                    self.assign_metric_to_grid(
+                        metric_name=label,
+                        grid_name=label,
+                        axes_ticks=_axes_ticks,
+                        axes_labels=[f"axis_{i}" for i in range(metric.ndim)],
+                    )
+                if metric.ndim != (_ndims := self._grids[label]["dimensionality"]):
+                    self._error(
+                        f"Cannot log tensor '{label}', "
+                        f"dimensionality incompatibility: {metric.ndim} != {_ndims}"
+                    )
+                _tensor_metrics[label] = metric
+            else:
+                _regular_metrics[label] = metric
+
+        # If there are no metrics to log just ignore
+        if self._user_config.run.mode == "disabled":
+            return True
+
+        if not (_tensor_metrics or _regular_metrics):
+            return True
+
+        if not self._active:
+            self._error("Run is not active", join_threads=True)
+            return False
+
+        if self._status != "running":
+            self._error(
+                "Cannot log metrics when not in the running state", join_threads=True
+            )
+            return False
+
+        if timestamp and not validate_timestamp(timestamp):
+            self._error("Invalid timestamp format", timestamp)
+            return False
+
+        _tensor_add_dispatch = self._add_tensors_to_dispatch(
+            tensors=_tensor_metrics,
+            step=step,
+            time=time,
+            timestamp=timestamp,
+        )
+        _regular_dispatch = self._add_metrics_to_dispatch(
+            metrics=_regular_metrics, step=step, time=time, timestamp=timestamp
         )
         self._step += 1
-        return add_dispatch
+        return _tensor_add_dispatch and _regular_dispatch
 
     @skip_if_failed("_aborted", "_suppress_errors", False)
     @check_run_initialised
diff --git a/tests/functional/test_run_class.py b/tests/functional/test_run_class.py
index 804f9bb6..fa54b084 100644
--- a/tests/functional/test_run_class.py
+++ b/tests/functional/test_run_class.py
@@ -2,6 +2,7 @@
 import logging
 import platform
 import os
+import numpy
 import pytest
 import requests
 import pytest_mock
@@ -19,6 +20,7 @@
 import datetime
 import simvue
 from simvue.api.objects import Alert, Metrics
+from simvue.api.objects.grids import GridMetrics
 from simvue.eco.api_client import CO2SignalData, CO2SignalResponse
 from simvue.exception import ObjectNotFoundError, SimvueRunError
 from simvue.eco.emissions_monitor import TIME_FORMAT, CO2Monitor
@@ -156,12 +158,14 @@ def test_run_with_emissions_offline(speedy_heartbeat, mock_co2_signal, create_pl
 @pytest.mark.parametrize(
     "visibility", ("bad_option", "tenant", "public", ["user01"], None)
 )
+@pytest.mark.parametrize("metric_type", ("regular", "tensor"))
 def test_log_metrics(
     overload_buffer: bool,
     timestamp: str | None,
     mocker: pytest_mock.MockerFixture,
     request: pytest.FixtureRequest,
     visibility: typing.Literal["public", "tenant"] | list[str] | None,
+    metric_type: typing.Literal["regular", "tensor"],
 ) -> None:
     METRICS = {"a": 10, "b": 1.2}
 
@@ -170,7 +174,11 @@ def test_log_metrics(
     run = sv_run.Run()
     run.config(suppress_errors=False)
 
-    metrics_spy = mocker.spy(Metrics, "new")
+
+    if metric_type == "tensor":
+        metrics_spy = mocker.spy(GridMetrics, "new")
+    else:
+        metrics_spy = mocker.spy(Metrics, "new")
     system_metrics_spy = mocker.spy(sv_run.Run, "_get_internal_metrics")
     unique_id = f"{uuid.uuid4()}".split("-")[0]
 
@@ -201,6 +209,17 @@ def test_log_metrics(
         visibility=visibility,
         retention_period=os.environ.get("SIMVUE_TESTING_RETENTION_PERIOD", "2 mins"),
     )
+    if metric_type == "tensor":
+        METRICS = {"c": numpy.identity(10), "g": numpy.ones((10, 10)) + 3 * numpy.identity(10)}
+        run.assign_metric_to_grid(
+            metric_name="c",
+            grid_name="test_log_metrics",
+            axes_ticks=numpy.vstack([
+                numpy.linspace(0, 10, 10),
+                numpy.linspace(0, 20, 10),
+            ]),
+            axes_labels=["x", "y"]
+        )
     # Will log system metrics on startup, and then not again within timeframe of test
     # So should have exactly one measurement of this
     run.config(system_metrics_interval=100)
@@ -210,37 +229,43 @@ def test_log_metrics(
 
     if overload_buffer:
         for i in range(run._dispatcher._max_buffer_size * 3):
-            run.log_metrics({key: i for key in METRICS}, timestamp=timestamp)
+            _value = i * numpy.identity(10) if metric_type == "tensor" else i
+            run.log_metrics({key: _value for key in METRICS}, timestamp=timestamp)
     else:
         run.log_metrics(METRICS, timestamp=timestamp)
     run.close()
-    time.sleep(2.0 if overload_buffer else 1.0)
-    client = sv_cl.Client()
-    _data = client.get_metric_values(
-        run_ids=[run.id],
-        metric_names=list(METRICS.keys()),
-        xaxis="step",
-        aggregate=False,
-    )
 
-    with contextlib.suppress(ObjectNotFoundError):
-        client.delete_folder(
-            f"/simvue_unit_testing/{unique_id}",
-            recursive=True,
-            remove_runs=True
+    #TODO: No client functions defined for grids yet
+    if metric_type != "tensor":
+        return
+
+        time.sleep(2.0 if overload_buffer else 1.0)
+        client = sv_cl.Client()
+        _data = client.get_metric_values(
+            run_ids=[run.id],
+            metric_names=list(METRICS.keys()),
+            xaxis="step",
+            aggregate=False,
         )
 
-    assert _data
+        #with contextlib.suppress(ObjectNotFoundError):
+        #    client.delete_folder(
+        #        f"/simvue_unit_testing/{unique_id}",
+        #        recursive=True,
+        #        remove_runs=True
+        #    )
 
-    assert sorted(set(METRICS.keys())) == sorted(set(_data.keys()))
-    _steps = []
-    for entry in _data.values():
-        _steps += [i[0] for i in entry.keys()]
-    _steps = set(_steps)
+        assert _data
 
-    assert len(_steps) == (
-        run._dispatcher._max_buffer_size * 3 if overload_buffer else 1
-    )
+        assert sorted(set(METRICS.keys())) == sorted(set(_data.keys()))
+        _steps = []
+        for entry in _data.values():
+            _steps += [i[0] for i in entry.keys()]
+        _steps = set(_steps)
+
+        assert len(_steps) == (
+            run._dispatcher._max_buffer_size * 3 if overload_buffer else 1
+        )
 
     if overload_buffer:
         assert metrics_spy.call_count > 2
diff --git a/tests/unit/test_grids.py b/tests/unit/test_grids.py
new file mode 100644
index 00000000..da7db33b
--- /dev/null
+++ b/tests/unit/test_grids.py
@@ -0,0 +1,82 @@
+import typing
+import datetime
+import numpy
+import pytest
+import uuid
+import contextlib
+import json
+import time
+import os
+
+from simvue.api.objects import Grid, GridMetrics, Folder, Run
+from simvue.models import GridMetricSet
+from simvue.run import Run as sv_Run
+from simvue.sender import sender
+from simvue.client import Client
+
+@pytest.mark.api
+@pytest.mark.online
+def test_grid_creation_online(create_test_run: tuple[sv_Run, dict]) -> None:
+    _uuid: str = f"{uuid.uuid4()}".split("-")[0]
+    _run, _ = create_test_run
+    _grid = Grid.new(
+        name=f"test_grid_creation_online_{_uuid}",
+        grid=numpy.vstack([
+            numpy.linspace(0, 10, 10),
+            numpy.linspace(0, 20, 10),
+            numpy.linspace(50, 60, 10),
+        ]),
+        labels=["x", "y", "z"]
+    )
+    _grid.commit()
+    _grid.attach_to_run(_run.id)
+
+
+@pytest.mark.api
+@pytest.mark.online
+def test_grid_metrics_creation_online(create_test_run: tuple[Run, dict]) -> None:
+    _uuid: str = f"{uuid.uuid4()}".split("-")[0]
+    _folder_name = f"/simvue_unit_testing/{_uuid}"
+    _folder = Folder.new(path=_folder_name)
+    _run = Run.new(folder=_folder_name)
+    _run.status = "running"
+    _values = {
+        "x": 1,
+        "y": 2.0,
+        "z": True
+    }
+    _time: int = 1
+    _step: int = 1
+    _folder.commit()
+    _run.commit()
+    _grid = Grid.new(
+        name=f"test_grid_metrics_creation_online_{_uuid}",
+        grid=numpy.vstack([
+            numpy.linspace(0, 10, 10),
+            numpy.linspace(0, 20, 10),
+        ]),
+        labels=["x", "y"]
+    )
+    _grid.commit()
+    _grid.attach_to_run(_run.id)
+
+    _metrics = GridMetrics.new(
+        run=_run.id,
+        metrics=[
+            {
+                "timestamp": datetime.datetime.now(datetime.timezone.utc).strftime(
+                    "%Y-%m-%dT%H:%M:%S.%f"
+                ),
+                "time": _time,
+                "step": _step,
+                "array": numpy.ones((10, 10)) + numpy.identity(10),
+                "grid": _grid.id
+            }
+        ],
+    )
+    _metrics.commit()
+    _run.status = "completed"
+    _run.commit()
+    _run.delete()
+    _folder.delete(recursive=True, delete_runs=True, runs_only=False)
+