diff --git a/src/litserve/specs/openai_embedding.py b/src/litserve/specs/openai_embedding.py
index a6640ab4d..b5f8a2e86 100644
--- a/src/litserve/specs/openai_embedding.py
+++ b/src/litserve/specs/openai_embedding.py
@@ -12,8 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import asyncio
+import base64
 import inspect
 import logging
+import struct
 import sys
 import time
 import uuid
@@ -59,7 +61,7 @@ def ensure_list(self):
 
 class Embedding(BaseModel):
     index: int
-    embedding: list[float]
+    embedding: Union[list[float], str]
     object: Literal["embedding"] = "embedding"
 
 
@@ -287,6 +289,11 @@ async def embeddings_endpoint(self, request: EmbeddingRequest) -> EmbeddingRespo
 
         self._validate_response(response)
         data: list[Embedding] = self._handle_embedding_response(response["embeddings"], num_items)
+        if request.encoding_format == "base64":
+            for item in data:
+                # OpenAI clients decode base64 embeddings as little-endian float32 vectors.
+                packed = struct.pack(f"<{len(item.embedding)}f", *item.embedding)
+                item.embedding = base64.b64encode(packed).decode("ascii")
 
         usage = UsageInfo(**response)
 
diff --git a/tests/e2e/test_e2e.py b/tests/e2e/test_e2e.py
index cd47b7432..7215bce5a 100644
--- a/tests/e2e/test_e2e.py
+++ b/tests/e2e/test_e2e.py
@@ -11,6 +11,7 @@
 # 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 base64
 import json
 import os
 import subprocess
@@ -392,6 +393,13 @@ def test_openai_embedding_parity():
     for data in response.data:
         assert len(data.embedding) == 768, f"Expected 768 dimensions but got {len(data.embedding)}"
 
+    # the SDK returns the raw payload when the caller explicitly asks for base64
+    response = client.embeddings.create(model="lit", input=input_text, encoding_format="base64")
+    embedding = response.data[0].embedding
+    assert isinstance(embedding, str), "Expected a base64 string but got something else"
+    # decoding gives raw bytes, so the length pins both the dimensions and the 4-byte float32 width
+    assert len(base64.b64decode(embedding)) == 768 * 4, "Expected 768 little-endian float32 values"
+
 
 @e2e_from_file("tests/e2e/default_async_streaming.py")
 def test_e2e_default_async_streaming():
diff --git a/tests/unit/test_openai_embedding.py b/tests/unit/test_openai_embedding.py
index 6a45fa633..17f2a052f 100644
--- a/tests/unit/test_openai_embedding.py
+++ b/tests/unit/test_openai_embedding.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import asyncio
+import base64
 import copy
 import time
 
@@ -209,3 +210,23 @@ async def test_batching_with_client_side_batching(openai_embedding_request_data_
                 == "The OpenAIEmbedding spec does not support dynamic batching when client-side batching is used. "
                 "To resolve this, either set `max_batch_size=1` or send a single input from the client."
             )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize("request_data", ["openai_embedding_request_data", "openai_embedding_request_data_array"])
+async def test_openai_embedding_spec_with_base64_encoding(request_data, request):
+    request_data = {**request.getfixturevalue(request_data), "encoding_format": "base64"}
+    server = ls.LitServer(TestEmbedAPI(spec=OpenAIEmbeddingSpec()))
+
+    with wrap_litserve_start(server) as server:
+        async with (
+            LifespanManager(server.app) as manager,
+            AsyncClient(transport=ASGITransport(app=manager.app), base_url="http://test") as ac,
+        ):
+            resp = await ac.post("/v1/embeddings", json=request_data, timeout=10)
+            assert resp.status_code == 200, "Status code should be 200"
+            for item in resp.json()["data"]:
+                embedding = item["embedding"]
+                assert isinstance(embedding, str), "Embedding should be a base64 string"
+                # OpenAI clients decode base64 embeddings as little-endian float32 vectors
+                assert len(np.frombuffer(base64.b64decode(embedding), dtype="<f4")) == 768