diff --git a/training/src/anemoi/training/diagnostics/callbacks/__init__.py b/training/src/anemoi/training/diagnostics/callbacks/__init__.py
index 65a19ce1..8a7eab0b 100644
--- a/training/src/anemoi/training/diagnostics/callbacks/__init__.py
+++ b/training/src/anemoi/training/diagnostics/callbacks/__init__.py
@@ -188,7 +188,6 @@ def get_callbacks(config: DictConfig) -> list[Callback]:
     )
 
     # Plotting callbacks
-
     trainer_callbacks.extend(
         instantiate(callback, config) for callback in config.diagnostics.plot.get("callbacks", None) or []
     )
diff --git a/training/src/anemoi/training/diagnostics/callbacks/stopping.py b/training/src/anemoi/training/diagnostics/callbacks/stopping.py
new file mode 100644
index 00000000..d5c674b5
--- /dev/null
+++ b/training/src/anemoi/training/diagnostics/callbacks/stopping.py
@@ -0,0 +1,103 @@
+# (C) Copyright 2025- Anemoi contributors.
+#
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# In applying this licence, ECMWF does not waive the privileges and immunities
+# granted to it by virtue of its status as an intergovernmental organisation
+# nor does it submit to any jurisdiction.
+
+from __future__ import annotations
+
+import logging
+import time
+from datetime import timedelta
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import pytorch_lightning as pl
+
+from anemoi.utils.dates import frequency_to_string
+from anemoi.utils.dates import frequency_to_timedelta
+
+if TYPE_CHECKING:
+    from omegaconf import OmegaConf
+
+LOGGER = logging.getLogger(__name__)
+
+
+class TimeLimit(pl.callbacks.Callback):
+    """Callback to stop the training process after a given time limit."""
+
+    def __init__(self, config: OmegaConf, limit: int | str, record_file: str | None = None) -> None:
+        """Initialise the TimeLimit callback.
+
+        Parameters
+        ----------
+        limit : int or str
+            The frequency to convert. If an integer, it is assumed to be in hours. If a string, it can be in the format:
+
+            - "1h" for 1 hour
+            - "1d" for 1 day
+            - "1m" for 1 minute
+            - "1s" for 1 second
+            - "1:30" for 1 hour and 30 minutes
+            - "1:30:10" for 1 hour, 30 minutes and 10 seconds
+            - "PT10M" for 10 minutes (ISO8601)
+
+        record_file : str or None
+            The file to record the last checkpoint to. If None, no file is written.
+
+        """
+        super().__init__()
+        self.config = config
+
+        self.limit = frequency_to_timedelta(limit)
+        self._record_file = Path(record_file) if record_file is not None else None
+
+        if self._record_file is not None and self._record_file.exists():
+            assert self._record_file.is_file(), "The record file must be a file."
+
+        self._start_time = time.time()
+
+    def on_train_epoch_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+        _ = pl_module
+        self._run_stopping_check(trainer)
+
+    def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule) -> None:
+        _ = pl_module
+        self._run_stopping_check(trainer)
+
+    def _run_stopping_check(self, trainer: pl.Trainer) -> None:
+        """
+        Check if the time limit has been reached and stop the training if so.
+
+        Parameters
+        ----------
+        trainer : pl.Trainer
+            Pytorch Lightning trainer
+        """
+        if timedelta(seconds=time.time() - self._start_time) < self.limit:
+            return
+
+        LOGGER.info("Time limit of %s seconds reached. Stopping training.", frequency_to_string(self.limit))
+        trainer.should_stop = True
+        self._log_to_file(trainer)
+
+    def _log_to_file(self, trainer: pl.Trainer) -> None:
+        """
+        Log the last checkpoint path to a file if given.
+
+        Parameters
+        ----------
+        trainer : pl.Trainer
+            Pytorch Lightning trainer
+        """
+        if self._record_file is not None:
+            last_checkpoint = trainer.checkpoint_callback.last_model_path
+            self._record_file.parent.mkdir(parents=True, exist_ok=True)
+
+            if self._record_file.exists():
+                self._record_file.unlink()
+
+            Path(self._record_file).write_text(str(last_checkpoint))
diff --git a/training/tests/diagnostics/callbacks/test_timelimit.py b/training/tests/diagnostics/callbacks/test_timelimit.py
new file mode 100644
index 00000000..4af00fd6
--- /dev/null
+++ b/training/tests/diagnostics/callbacks/test_timelimit.py
@@ -0,0 +1,91 @@
+# (C) Copyright 2025- Anemoi contributors.
+#
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
+#
+# In applying this licence, ECMWF does not waive the privileges and immunities
+# granted to it by virtue of its status as an intergovernmental organisation
+# nor does it submit to any jurisdiction.
+
+
+from typing import Any
+from unittest.mock import Mock
+from unittest.mock import patch
+
+import pytest
+from pytorch_lightning import Trainer
+
+from anemoi.training.diagnostics.callbacks.stopping import TimeLimit
+
+
+@pytest.fixture
+def mock_trainer() -> Mock:
+    training_process = Mock(Trainer)
+    training_process.should_stop = False
+    return training_process
+
+
+def test_time_limit_stops_training(mock_trainer: Mock) -> None:
+    # Setting a time limit of 1 second
+    time_limit_callback = TimeLimit(config=None, limit="24h")
+
+    # Mocking the time to simulate training for 2 seconds
+    with patch("time.time", return_value=time_limit_callback._start_time + 24 * 60 * 60 + 1):
+        time_limit_callback.on_train_epoch_end(mock_trainer, None)
+
+    # Check if the training process should stop
+    assert mock_trainer.should_stop
+
+
+def test_time_limit_stops_training_int(mock_trainer: Mock) -> None:
+    # Setting a time limit of 1 second
+    time_limit_callback = TimeLimit(config=None, limit=24)
+
+    # Mocking the time to simulate training for 2 seconds
+    with patch("time.time", return_value=time_limit_callback._start_time + 24 * 60 * 60 + 1):
+        time_limit_callback.on_train_epoch_end(mock_trainer, None)
+
+    # Check if the training process should stop
+    assert mock_trainer.should_stop
+
+
+def test_time_limit_does_not_stop_training(mock_trainer: Mock) -> None:
+    # Setting a time limit of 1 hour
+    time_limit_callback = TimeLimit(config=None, limit="1h")
+
+    # Mocking the time to simulate training for 2 seconds
+    with patch("time.time", return_value=time_limit_callback._start_time + 2):
+        time_limit_callback.on_train_epoch_end(mock_trainer, None)
+
+    # Check if the training process should not stop
+    assert not mock_trainer.should_stop
+
+
+def test_time_limit_creates_file_on_stop(mock_trainer: Mock, tmp_path: Any) -> None:
+    # Setting a time limit of 1 second
+    time_limit_callback = TimeLimit(config=None, limit="1s", record_file=tmp_path / "log")
+
+    # Mocking the time to simulate training for 2 seconds
+    with patch("time.time", return_value=time_limit_callback._start_time + 2):
+        time_limit_callback.on_train_epoch_end(mock_trainer, None)
+
+    # Check if the training process should stop
+    assert mock_trainer.should_stop
+
+    # Check if the file is created
+    assert (tmp_path / "log").exists()
+
+
+def test_time_limit_does_not_create_file_when_not_stopping(mock_trainer: Mock, tmp_path: Any) -> None:
+    # Setting a time limit of 1 second
+    time_limit_callback = TimeLimit(config=None, limit="24h", record_file=tmp_path / "log")
+
+    # Mocking the time to simulate training for 2 seconds
+    with patch("time.time", return_value=time_limit_callback._start_time + 2):
+        time_limit_callback.on_train_epoch_end(mock_trainer, None)
+
+    # Check if the training process should stop
+    assert not mock_trainer.should_stop
+
+    # Check if the file is created
+    assert not (tmp_path / "log").exists()