diff --git a/backend/danswer/indexing/embedder.py b/backend/danswer/indexing/embedder.py
index 0aaeb3552..0b542067a 100644
--- a/backend/danswer/indexing/embedder.py
+++ b/backend/danswer/indexing/embedder.py
@@ -19,7 +19,7 @@ from danswer.search.search_nlp_models import EmbeddingModel
 from danswer.utils.batching import batch_list
 from danswer.utils.logger import setup_logger
 from shared_configs.configs import INDEXING_MODEL_SERVER_HOST
-from shared_configs.configs import MODEL_SERVER_PORT
+from shared_configs.configs import INDEXING_MODEL_SERVER_PORT
 
 
 logger = setup_logger()
@@ -61,7 +61,7 @@ class DefaultIndexingEmbedder(IndexingEmbedder):
             normalize=normalize,
             # The below are globally set, this flow always uses the indexing one
             server_host=INDEXING_MODEL_SERVER_HOST,
-            server_port=MODEL_SERVER_PORT,
+            server_port=INDEXING_MODEL_SERVER_PORT,
         )
 
     def embed_chunks(
diff --git a/backend/shared_configs/configs.py b/backend/shared_configs/configs.py
index 41b46723e..aeeb9cf2a 100644
--- a/backend/shared_configs/configs.py
+++ b/backend/shared_configs/configs.py
@@ -9,6 +9,9 @@ MODEL_SERVER_PORT = int(os.environ.get("MODEL_SERVER_PORT") or "9000")
 INDEXING_MODEL_SERVER_HOST = (
     os.environ.get("INDEXING_MODEL_SERVER_HOST") or MODEL_SERVER_HOST
 )
+INDEXING_MODEL_SERVER_PORT = int(
+    os.environ.get("INDEXING_MODEL_SERVER_PORT") or MODEL_SERVER_PORT
+)
 
 # Danswer custom Deep Learning Models
 INTENT_MODEL_VERSION = "danswer/intent-model"