diff --git a/projects/pgai/pgai/semantic_catalog/vectorizer/openai.py b/projects/pgai/pgai/semantic_catalog/vectorizer/openai.py
index f61869e8f..40cf85193 100644
--- a/projects/pgai/pgai/semantic_catalog/vectorizer/openai.py
+++ b/projects/pgai/pgai/semantic_catalog/vectorizer/openai.py
@@ -7,12 +7,24 @@
 import os
 from collections.abc import Sequence
 
-from openai import AsyncClient
+from openai import AsyncClient, AsyncAzureOpenAI
 
 from pgai.semantic_catalog.vectorizer import OpenAIConfig
 from pgai.semantic_catalog.vectorizer.models import EmbedRow
 
 
+def create_openai_client(config: OpenAIConfig):
+    api_key: str | None = None
+    if config.api_key_name is not None:
+        api_key = os.getenv(config.api_key_name)
+    if config.model.startswith("azure:"):
+        return AsyncAzureOpenAI(
+                azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+                api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
+                api_key=api_key), config.model.replace("azure:", "")
+    return AsyncClient(api_key=api_key, base_url=config.base_url), config.model
+
+
 async def embed_batch(config: OpenAIConfig, batch: list[EmbedRow]) -> None:
     """Generate embeddings for a batch of content using OpenAI.
 
@@ -26,13 +38,10 @@ async def embed_batch(config: OpenAIConfig, batch: list[EmbedRow]) -> None:
     Raises:
         RuntimeError: If the number of embeddings returned doesn't match the batch size.
     """
-    api_key: str | None = None
-    if config.api_key_name is not None:
-        api_key = os.getenv(config.api_key_name)
-    client = AsyncClient(api_key=api_key, base_url=config.base_url)  # TODO: cache this?
+    client, model = create_openai_client(config) # TODO: cache this?
     response = await client.embeddings.create(
         input=[x.content for x in batch],
-        model=config.model,
+        model=model,
         dimensions=config.dimensions,
     )
     if len(response.data) != len(batch):
@@ -58,13 +67,10 @@ async def embed_query(config: OpenAIConfig, query: str) -> Sequence[float]:
     Raises:
         RuntimeError: If the number of embeddings returned is not exactly 1.
     """
-    api_key: str | None = None
-    if config.api_key_name is not None:
-        api_key = os.getenv(config.api_key_name)
-    client = AsyncClient(api_key=api_key, base_url=config.base_url)  # TODO: cache this?
+    client, model = create_openai_client(config) # TODO: cache this?
     response = await client.embeddings.create(
         input=query,
-        model=config.model,
+        model=model,
         dimensions=config.dimensions,
     )
     if len(response.data) != 1: