diff --git a/rationai/resources/models.py b/rationai/resources/models.py
index 47be222..3a297a7 100644
--- a/rationai/resources/models.py
+++ b/rationai/resources/models.py
@@ -1,3 +1,5 @@
+from typing import cast
+
 import lz4.frame
 import numpy as np
 from httpx import USE_CLIENT_DEFAULT
@@ -27,8 +29,7 @@ def classify_image(
             (float | dict[str, float]): The classification result as a single float
                 (for binary classification) or probabilities for each class.
         """
-        data = image.tobytes()
-        compressed_data = lz4.frame.compress(data)
+        compressed_data = lz4.frame.compress(image.tobytes())
         response = self._post(model, data=compressed_data, timeout=timeout)
         response.raise_for_status()
         return response.json()
@@ -55,8 +56,7 @@ def segment_image(
         else:
             h, w = image.shape[:2]
 
-        data = image.tobytes()
-        compressed_data = lz4.frame.compress(data)
+        compressed_data = lz4.frame.compress(image.tobytes())
         response = self._post(model, data=compressed_data, timeout=timeout)
         response.raise_for_status()
 
@@ -64,6 +64,42 @@ def segment_image(
             lz4.frame.decompress(response.content), dtype=np.float16
         ).reshape(-1, h, w)
 
+    def embed_image[DType: np.generic](
+        self,
+        model: str,
+        image: Image | NDArray[np.uint8],
+        output_dtype: type[DType] = np.float32,  # type: ignore[assignment]
+        timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT,
+    ) -> NDArray[DType]:
+        """Compute an embedding vector for an image using the specified model.
+
+        Args:
+            model: The name of the model to use for embedding.
+            image: The image to embed. It must be uint8 RGB image.
+            output_dtype: Output numpy dtype for embeddings (e.g. np.float16, np.float32).
+            timeout: Optional timeout for the request.
+
+        Returns:
+            NDArray[DType]: The embedding array reshaped according to
+                the `x-output-shape` response header.
+        """
+        compressed_data = lz4.frame.compress(image.tobytes())
+        response = self._post(
+            model,
+            data=compressed_data,
+            headers={"x-output-dtype": np.dtype(output_dtype).name},
+            timeout=timeout,
+        )
+        response.raise_for_status()
+
+        payload = lz4.frame.decompress(response.content)
+        embedding = np.frombuffer(payload, dtype=output_dtype)
+
+        response_shape = response.headers["x-output-shape"]
+        embedding = embedding.reshape(eval(response_shape))
+
+        return cast("NDArray[DType]", embedding)
+
 
 class AsyncModels(AsyncAPIResource):
     async def classify_image(
@@ -83,8 +119,7 @@ async def classify_image(
             (float | dict[str, float]): The classification result as a single float
                 (for binary classification) or probabilities for each class.
         """
-        data = image.tobytes()
-        compressed_data = lz4.frame.compress(data)
+        compressed_data = lz4.frame.compress(image.tobytes())
         response = await self._post(model, data=compressed_data, timeout=timeout)
         response.raise_for_status()
         return response.json()
@@ -111,11 +146,46 @@ async def segment_image(
         else:
             h, w = image.shape[:2]
 
-        data = image.tobytes()
-        compressed_data = lz4.frame.compress(data)
+        compressed_data = lz4.frame.compress(image.tobytes())
         response = await self._post(model, data=compressed_data, timeout=timeout)
         response.raise_for_status()
 
         return np.frombuffer(
             lz4.frame.decompress(response.content), dtype=np.float16
         ).reshape(-1, h, w)
+
+    async def embed_image[DType: np.generic](
+        self,
+        model: str,
+        image: Image | NDArray[np.uint8],
+        output_dtype: type[DType] = np.float32,  # type: ignore[assignment]
+        timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT,
+    ) -> NDArray[DType]:
+        """Compute an embedding vector for an image using the specified model.
+
+        Args:
+            model: The name of the model to use for embedding.
+            image: The image to embed. It must be uint8 RGB image.
+            output_dtype: Output numpy dtype for embeddings (e.g. np.float16, np.float32).
+            timeout: Optional timeout for the request.
+
+        Returns:
+            NDArray[DType]: The embedding array reshaped according to
+                the `x-output-shape` response header.
+        """
+        compressed_data = lz4.frame.compress(image.tobytes())
+        response = await self._post(
+            model,
+            data=compressed_data,
+            headers={"x-output-dtype": np.dtype(output_dtype).name},
+            timeout=timeout,
+        )
+        response.raise_for_status()
+
+        payload = lz4.frame.decompress(response.content)
+        embedding = np.frombuffer(payload, dtype=output_dtype)
+
+        response_shape = response.headers["x-output-shape"]
+        embedding = embedding.reshape(eval(response_shape))
+
+        return cast("NDArray[DType]", embedding)