diff --git a/backend/danswer/datastores/qdrant/indexing.py b/backend/danswer/datastores/qdrant/indexing.py
index 66a5c6c8f337..67299acaa6df 100644
--- a/backend/danswer/datastores/qdrant/indexing.py
+++ b/backend/danswer/datastores/qdrant/indexing.py
@@ -27,6 +27,18 @@ logger = setup_logger()
 DEFAULT_BATCH_SIZE = 30
 
 
+def create_collection(
+    collection_name: str, embedding_dim: int = DOC_EMBEDDING_DIM
+) -> None:
+    logger.info(f"Attempting to create collection {collection_name}")
+    result = get_qdrant_client().create_collection(
+        collection_name=collection_name,
+        vectors_config=VectorParams(size=embedding_dim, distance=Distance.COSINE),
+    )
+    if not result:
+        raise RuntimeError("Could not create Qdrant collection")
+
+
 def recreate_collection(
     collection_name: str, embedding_dim: int = DOC_EMBEDDING_DIM
 ) -> None:
diff --git a/backend/danswer/main.py b/backend/danswer/main.py
index c578a3e760be..714a6a932b55 100644
--- a/backend/danswer/main.py
+++ b/backend/danswer/main.py
@@ -93,9 +93,15 @@ def get_application() -> FastAPI:
 
     @application.on_event("startup")
     def startup_event() -> None:
+        # To avoid circular imports
         from danswer.semantic_search.semantic_search import (
             warm_up_models,
-        )  # To avoid circular imports
+        )
+        from danswer.datastores.qdrant.indexing import create_collection
+        from danswer.configs.app_configs import QDRANT_DEFAULT_COLLECTION
+
+        create_collection(collection_name=QDRANT_DEFAULT_COLLECTION)
+        logger.info("Collection ready")
 
         warm_up_models()
         logger.info("Semantic Search models are ready.")