diff --git a/docs/pydoc/config/embedders_api.yml b/docs/pydoc/config/embedders_api.yml
index c5b5b8a906..326d98c881 100644
--- a/docs/pydoc/config/embedders_api.yml
+++ b/docs/pydoc/config/embedders_api.yml
@@ -7,6 +7,7 @@ loaders:
         "azure_text_embedder",
         "hugging_face_tei_document_embedder",
         "hugging_face_tei_text_embedder",
+        "hugging_face_api_text_embedder",
         "openai_document_embedder",
         "openai_text_embedder",
         "sentence_transformers_document_embedder",
diff --git a/haystack/components/embedders/__init__.py b/haystack/components/embedders/__init__.py
index 6ff4e339a7..a2e3d15a4f 100644
--- a/haystack/components/embedders/__init__.py
+++ b/haystack/components/embedders/__init__.py
@@ -1,5 +1,6 @@
 from haystack.components.embedders.azure_document_embedder import AzureOpenAIDocumentEmbedder
 from haystack.components.embedders.azure_text_embedder import AzureOpenAITextEmbedder
+from haystack.components.embedders.hugging_face_api_text_embedder import HuggingFaceAPITextEmbedder
 from haystack.components.embedders.hugging_face_tei_document_embedder import HuggingFaceTEIDocumentEmbedder
 from haystack.components.embedders.hugging_face_tei_text_embedder import HuggingFaceTEITextEmbedder
 from haystack.components.embedders.openai_document_embedder import OpenAIDocumentEmbedder
@@ -10,6 +11,7 @@
 __all__ = [
     "HuggingFaceTEITextEmbedder",
     "HuggingFaceTEIDocumentEmbedder",
+    "HuggingFaceAPITextEmbedder",
     "SentenceTransformersTextEmbedder",
     "SentenceTransformersDocumentEmbedder",
     "OpenAITextEmbedder",
diff --git a/haystack/components/embedders/hugging_face_api_text_embedder.py b/haystack/components/embedders/hugging_face_api_text_embedder.py
new file mode 100644
index 0000000000..de7c3097b2
--- /dev/null
+++ b/haystack/components/embedders/hugging_face_api_text_embedder.py
@@ -0,0 +1,191 @@
+import json
+from typing import Any, Dict, List, Optional, Union
+
+from haystack import component, default_from_dict, default_to_dict, logging
+from haystack.lazy_imports import LazyImport
+from haystack.utils import Secret, deserialize_secrets_inplace
+from haystack.utils.hf import HFEmbeddingAPIType, HFModelType, check_valid_model
+from haystack.utils.url_validation import is_valid_http_url
+
+with LazyImport(message="Run 'pip install \"huggingface_hub>=0.22.0\"'") as huggingface_hub_import:
+    from huggingface_hub import InferenceClient
+
+logger = logging.getLogger(__name__)
+
+
+@component
+class HuggingFaceAPITextEmbedder:
+    """
+    This component can be used to embed strings using different Hugging Face APIs:
+    - [Free Serverless Inference API]((https://huggingface.co/inference-api)
+    - [Paid Inference Endpoints](https://huggingface.co/inference-endpoints)
+    - [Self-hosted Text Embeddings Inference](https://github.com/huggingface/text-embeddings-inference)
+
+
+    Example usage with the free Serverless Inference API:
+    ```python
+    from haystack.components.embedders import HuggingFaceAPITextEmbedder
+    from haystack.utils import Secret
+
+    text_embedder = HuggingFaceAPITextEmbedder(api_type="serverless_inference_api",
+                                               api_params={"model": "BAAI/bge-small-en-v1.5"},
+                                               token=Secret.from_token("<your-api-key>"))
+
+    print(text_embedder.run("I love pizza!"))
+
+    # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
+    ```
+
+    Example usage with paid Inference Endpoints:
+    ```python
+    from haystack.components.embedders import HuggingFaceAPITextEmbedder
+    from haystack.utils import Secret
+    text_embedder = HuggingFaceAPITextEmbedder(api_type="inference_endpoints",
+                                               api_params={"model": "BAAI/bge-small-en-v1.5"},
+                                               token=Secret.from_token("<your-api-key>"))
+
+    print(text_embedder.run("I love pizza!"))
+
+    # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
+    ```
+
+    Example usage with self-hosted Text Embeddings Inference:
+    ```python
+    from haystack.components.embedders import HuggingFaceAPITextEmbedder
+    from haystack.utils import Secret
+
+    text_embedder = HuggingFaceAPITextEmbedder(api_type="text_embeddings_inference",
+                                               api_params={"url": "http://localhost:8080"})
+
+    print(text_embedder.run("I love pizza!"))
+
+    # {'embedding': [0.017020374536514282, -0.023255806416273117, ...],
+    ```
+    """
+
+    def __init__(
+        self,
+        api_type: Union[HFEmbeddingAPIType, str],
+        api_params: Dict[str, str],
+        token: Optional[Secret] = Secret.from_env_var("HF_API_TOKEN", strict=False),
+        prefix: str = "",
+        suffix: str = "",
+        truncate: bool = True,
+        normalize: bool = False,
+    ):
+        """
+        Create an HuggingFaceAPITextEmbedder component.
+
+        :param api_type:
+            The type of Hugging Face API to use.
+        :param api_params:
+            A dictionary containing the following keys:
+            - `model`: model ID on the Hugging Face Hub. Required when `api_type` is `SERVERLESS_INFERENCE_API`.
+            - `url`: URL of the inference endpoint. Required when `api_type` is `INFERENCE_ENDPOINTS` or `TEXT_EMBEDDINGS_INFERENCE`.
+        :param token: The HuggingFace token to use as HTTP bearer authorization
+            You can find your HF token in your [account settings](https://huggingface.co/settings/tokens)
+        :param prefix:
+            A string to add at the beginning of each text.
+        :param suffix:
+            A string to add at the end of each text.
+        :param truncate:
+            Truncate input text from the end to the maximum length supported by the model.
+            This parameter takes effect when the `api_type` is `TEXT_EMBEDDINGS_INFERENCE`.
+            It also takes effect when the `api_type` is `INFERENCE_ENDPOINTS` and the backend is based on Text Embeddings Inference.
+            This parameter is ignored when the `api_type` is `SERVERLESS_INFERENCE_API` (it is always set to `True` and cannot be changed).
+        :param normalize:
+            Normalize the embeddings to unit length.
+            This parameter takes effect when the `api_type` is `TEXT_EMBEDDINGS_INFERENCE`.
+            It also takes effect when the `api_type` is `INFERENCE_ENDPOINTS` and the backend is based on Text Embeddings Inference.
+            This parameter is ignored when the `api_type` is `SERVERLESS_INFERENCE_API` (it is always set to `False` and cannot be changed).
+        """
+        huggingface_hub_import.check()
+
+        if isinstance(api_type, str):
+            api_type = HFEmbeddingAPIType.from_str(api_type)
+
+        if api_type == HFEmbeddingAPIType.SERVERLESS_INFERENCE_API:
+            model = api_params.get("model")
+            if model is None:
+                raise ValueError(
+                    "To use the Serverless Inference API, you need to specify the `model` parameter in `api_params`."
+                )
+            check_valid_model(model, HFModelType.EMBEDDING, token)
+            model_or_url = model
+        elif api_type in [HFEmbeddingAPIType.INFERENCE_ENDPOINTS, HFEmbeddingAPIType.TEXT_EMBEDDINGS_INFERENCE]:
+            url = api_params.get("url")
+            if url is None:
+                raise ValueError(
+                    "To use Text Embeddings Inference or Inference Endpoints, you need to specify the `url` parameter in `api_params`."
+                )
+            if not is_valid_http_url(url):
+                raise ValueError(f"Invalid URL: {url}")
+            model_or_url = url
+
+        self.api_type = api_type
+        self.api_params = api_params
+        self.token = token
+        self.prefix = prefix
+        self.suffix = suffix
+        self.truncate = truncate
+        self.normalize = normalize
+        self._client = InferenceClient(model_or_url, token=token.resolve_value() if token else None)
+
+    def to_dict(self) -> Dict[str, Any]:
+        """
+        Serializes the component to a dictionary.
+
+        :returns:
+            Dictionary with serialized data.
+        """
+        return default_to_dict(
+            self,
+            api_type=self.api_type,
+            api_params=self.api_params,
+            prefix=self.prefix,
+            suffix=self.suffix,
+            token=self.token.to_dict() if self.token else None,
+            truncate=self.truncate,
+            normalize=self.normalize,
+        )
+
+    @classmethod
+    def from_dict(cls, data: Dict[str, Any]) -> "HuggingFaceAPITextEmbedder":
+        """
+        Deserializes the component from a dictionary.
+
+        :param data:
+            Dictionary to deserialize from.
+        :returns:
+            Deserialized component.
+        """
+        deserialize_secrets_inplace(data["init_parameters"], keys=["token"])
+        return default_from_dict(cls, data)
+
+    @component.output_types(embedding=List[float])
+    def run(self, text: str):
+        """
+        Embed a single string.
+
+        :param text:
+            Text to embed.
+
+        :returns:
+            A dictionary with the following keys:
+            - `embedding`: The embedding of the input text.
+        """
+        if not isinstance(text, str):
+            raise TypeError(
+                "HuggingFaceAPITextEmbedder expects a string as an input."
+                "In case you want to embed a list of Documents, please use the HuggingFaceAPIDocumentEmbedder."
+            )
+
+        text_to_embed = self.prefix + text + self.suffix
+
+        response = self._client.post(
+            json={"inputs": [text_to_embed], "truncate": self.truncate, "normalize": self.normalize},
+            task="feature-extraction",
+        )
+        embedding = json.loads(response.decode())[0]
+
+        return {"embedding": embedding}
diff --git a/haystack/components/embedders/hugging_face_tei_text_embedder.py b/haystack/components/embedders/hugging_face_tei_text_embedder.py
index f618214e30..5956ee1d83 100644
--- a/haystack/components/embedders/hugging_face_tei_text_embedder.py
+++ b/haystack/components/embedders/hugging_face_tei_text_embedder.py
@@ -1,4 +1,5 @@
 import json
+import warnings
 from typing import Any, Dict, List, Optional
 from urllib.parse import urlparse
 
@@ -74,6 +75,12 @@ def __init__(
             Inference (TEI) endpoints and paid HF Inference Endpoints deployed with TEI. It will be ignored when used
             with free HF Inference endpoints or paid HF Inference endpoints deployed without TEI.
         """
+        warnings.warn(
+            "`HuggingFaceTEITextEmbedder` is deprecated and will be removed in Haystack 2.3.0."
+            "Use `HuggingFaceAPITextEmbedder` instead.",
+            DeprecationWarning,
+        )
+
         huggingface_hub_import.check()
 
         if url:
diff --git a/haystack/utils/hf.py b/haystack/utils/hf.py
index b941a742f0..1261d23dc7 100644
--- a/haystack/utils/hf.py
+++ b/haystack/utils/hf.py
@@ -48,6 +48,33 @@ def from_str(string: str) -> "HFGenerationAPIType":
         return mode
 
 
+class HFEmbeddingAPIType(Enum):
+    """
+    API type to use for Hugging Face API Embedders.
+    """
+
+    # HF [Text Embeddings Inference (TEI)](https://github.com/huggingface/text-embeddings-inference).
+    TEXT_EMBEDDINGS_INFERENCE = "text_embeddings_inference"
+
+    # HF [Inference Endpoints](https://huggingface.co/inference-endpoints).
+    INFERENCE_ENDPOINTS = "inference_endpoints"
+
+    # HF [Serverless Inference API](https://huggingface.co/inference-api).
+    SERVERLESS_INFERENCE_API = "serverless_inference_api"
+
+    def __str__(self):
+        return self.value
+
+    @staticmethod
+    def from_str(string: str) -> "HFEmbeddingAPIType":
+        enum_map = {e.value: e for e in HFEmbeddingAPIType}
+        mode = enum_map.get(string)
+        if mode is None:
+            msg = f"Unknown Hugging Face API type '{string}'. Supported types are: {list(enum_map.keys())}"
+            raise ValueError(msg)
+        return mode
+
+
 class HFModelType(Enum):
     EMBEDDING = 1
     GENERATION = 2
diff --git a/releasenotes/notes/hfapitextembedder-97bf5f739f413f3e.yaml b/releasenotes/notes/hfapitextembedder-97bf5f739f413f3e.yaml
new file mode 100644
index 0000000000..1f8ac29dee
--- /dev/null
+++ b/releasenotes/notes/hfapitextembedder-97bf5f739f413f3e.yaml
@@ -0,0 +1,13 @@
+---
+features:
+  - |
+    Introduce `HuggingFaceAPITextEmbedder`.
+    This component can be used to embed strings using different Hugging Face APIs:
+      - free Serverless Inference API
+      - paid Inference Endpoints
+      - self-hosted Text Embeddings Inference.
+    This embedder will replace the `HuggingFaceTEITextEmbedder` in the future.
+deprecations:
+  - |
+    Deprecate `HuggingFaceTEITextEmbedder`. This component will be removed in Haystack 2.3.0.
+    Use `HuggingFaceAPITextEmbedder` instead.
diff --git a/test/components/embedders/test_hugging_face_api_text_embedder.py b/test/components/embedders/test_hugging_face_api_text_embedder.py
new file mode 100644
index 0000000000..c5a14db481
--- /dev/null
+++ b/test/components/embedders/test_hugging_face_api_text_embedder.py
@@ -0,0 +1,172 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+from huggingface_hub.utils import RepositoryNotFoundError
+from numpy import array, random
+
+from haystack.components.embedders import HuggingFaceAPITextEmbedder
+from haystack.utils.auth import Secret
+from haystack.utils.hf import HFEmbeddingAPIType
+
+
+@pytest.fixture
+def mock_check_valid_model():
+    with patch(
+        "haystack.components.embedders.hugging_face_api_text_embedder.check_valid_model", MagicMock(return_value=None)
+    ) as mock:
+        yield mock
+
+
+def mock_embedding_generation(json, **kwargs):
+    response = str(array([random.rand(384) for i in range(len(json["inputs"]))]).tolist()).encode()
+    return response
+
+
+class TestHuggingFaceAPITextEmbedder:
+    def test_init_invalid_api_type(self):
+        with pytest.raises(ValueError):
+            HuggingFaceAPITextEmbedder(api_type="invalid_api_type", api_params={})
+
+    def test_init_serverless(self, mock_check_valid_model):
+        model = "BAAI/bge-small-en-v1.5"
+        embedder = HuggingFaceAPITextEmbedder(
+            api_type=HFEmbeddingAPIType.SERVERLESS_INFERENCE_API, api_params={"model": model}
+        )
+
+        assert embedder.api_type == HFEmbeddingAPIType.SERVERLESS_INFERENCE_API
+        assert embedder.api_params == {"model": model}
+        assert embedder.prefix == ""
+        assert embedder.suffix == ""
+        assert embedder.truncate
+        assert not embedder.normalize
+
+    def test_init_serverless_invalid_model(self, mock_check_valid_model):
+        mock_check_valid_model.side_effect = RepositoryNotFoundError("Invalid model id")
+        with pytest.raises(RepositoryNotFoundError):
+            HuggingFaceAPITextEmbedder(
+                api_type=HFEmbeddingAPIType.SERVERLESS_INFERENCE_API, api_params={"model": "invalid_model_id"}
+            )
+
+    def test_init_serverless_no_model(self):
+        with pytest.raises(ValueError):
+            HuggingFaceAPITextEmbedder(
+                api_type=HFEmbeddingAPIType.SERVERLESS_INFERENCE_API, api_params={"param": "irrelevant"}
+            )
+
+    def test_init_tei(self):
+        url = "https://some_model.com"
+
+        embedder = HuggingFaceAPITextEmbedder(
+            api_type=HFEmbeddingAPIType.TEXT_EMBEDDINGS_INFERENCE, api_params={"url": url}
+        )
+
+        assert embedder.api_type == HFEmbeddingAPIType.TEXT_EMBEDDINGS_INFERENCE
+        assert embedder.api_params == {"url": url}
+        assert embedder.prefix == ""
+        assert embedder.suffix == ""
+        assert embedder.truncate
+        assert not embedder.normalize
+
+    def test_init_tei_invalid_url(self):
+        with pytest.raises(ValueError):
+            HuggingFaceAPITextEmbedder(
+                api_type=HFEmbeddingAPIType.TEXT_EMBEDDINGS_INFERENCE, api_params={"url": "invalid_url"}
+            )
+
+    def test_init_tei_no_url(self):
+        with pytest.raises(ValueError):
+            HuggingFaceAPITextEmbedder(
+                api_type=HFEmbeddingAPIType.TEXT_EMBEDDINGS_INFERENCE, api_params={"param": "irrelevant"}
+            )
+
+    def test_to_dict(self, mock_check_valid_model):
+        embedder = HuggingFaceAPITextEmbedder(
+            api_type=HFEmbeddingAPIType.SERVERLESS_INFERENCE_API,
+            api_params={"model": "BAAI/bge-small-en-v1.5"},
+            prefix="prefix",
+            suffix="suffix",
+            truncate=False,
+            normalize=True,
+        )
+
+        data = embedder.to_dict()
+
+        assert data == {
+            "type": "haystack.components.embedders.hugging_face_api_text_embedder.HuggingFaceAPITextEmbedder",
+            "init_parameters": {
+                "api_type": HFEmbeddingAPIType.SERVERLESS_INFERENCE_API,
+                "api_params": {"model": "BAAI/bge-small-en-v1.5"},
+                "token": {"env_vars": ["HF_API_TOKEN"], "strict": False, "type": "env_var"},
+                "prefix": "prefix",
+                "suffix": "suffix",
+                "truncate": False,
+                "normalize": True,
+            },
+        }
+
+    def test_from_dict(self, mock_check_valid_model):
+        data = {
+            "type": "haystack.components.embedders.hugging_face_api_text_embedder.HuggingFaceAPITextEmbedder",
+            "init_parameters": {
+                "api_type": HFEmbeddingAPIType.SERVERLESS_INFERENCE_API,
+                "api_params": {"model": "BAAI/bge-small-en-v1.5"},
+                "token": {"env_vars": ["HF_API_TOKEN"], "strict": False, "type": "env_var"},
+                "prefix": "prefix",
+                "suffix": "suffix",
+                "truncate": False,
+                "normalize": True,
+            },
+        }
+
+        embedder = HuggingFaceAPITextEmbedder.from_dict(data)
+
+        assert embedder.api_type == HFEmbeddingAPIType.SERVERLESS_INFERENCE_API
+        assert embedder.api_params == {"model": "BAAI/bge-small-en-v1.5"}
+        assert embedder.prefix == "prefix"
+        assert embedder.suffix == "suffix"
+        assert not embedder.truncate
+        assert embedder.normalize
+
+    def test_run_wrong_input_format(self, mock_check_valid_model):
+        embedder = HuggingFaceAPITextEmbedder(
+            api_type=HFEmbeddingAPIType.SERVERLESS_INFERENCE_API, api_params={"model": "BAAI/bge-small-en-v1.5"}
+        )
+
+        list_integers_input = [1, 2, 3]
+
+        with pytest.raises(TypeError):
+            embedder.run(text=list_integers_input)
+
+    def test_run(self, mock_check_valid_model):
+        with patch("huggingface_hub.InferenceClient.post") as mock_embedding_patch:
+            mock_embedding_patch.side_effect = mock_embedding_generation
+
+            embedder = HuggingFaceAPITextEmbedder(
+                api_type=HFEmbeddingAPIType.SERVERLESS_INFERENCE_API,
+                api_params={"model": "BAAI/bge-small-en-v1.5"},
+                token=Secret.from_token("fake-api-token"),
+                prefix="prefix ",
+                suffix=" suffix",
+            )
+
+            result = embedder.run(text="The food was delicious")
+
+            mock_embedding_patch.assert_called_once_with(
+                json={"inputs": ["prefix The food was delicious suffix"], "truncate": True, "normalize": False},
+                task="feature-extraction",
+            )
+
+        assert len(result["embedding"]) == 384
+        assert all(isinstance(x, float) for x in result["embedding"])
+
+    @pytest.mark.flaky(reruns=5, reruns_delay=5)
+    @pytest.mark.integration
+    def test_live_run_serverless(self):
+        embedder = HuggingFaceAPITextEmbedder(
+            api_type=HFEmbeddingAPIType.SERVERLESS_INFERENCE_API,
+            api_params={"model": "sentence-transformers/all-MiniLM-L6-v2"},
+        )
+        result = embedder.run(text="The food was delicious")
+
+        assert len(result["embedding"]) == 384
+        assert all(isinstance(x, float) for x in result["embedding"])