diff --git a/backend/danswer/natural_language_processing/utils.py b/backend/danswer/natural_language_processing/utils.py
index a8250570e84..56631495a4e 100644
--- a/backend/danswer/natural_language_processing/utils.py
+++ b/backend/danswer/natural_language_processing/utils.py
@@ -89,67 +89,70 @@ def _check_tokenizer_cache(
     model_provider: EmbeddingProvider | None, model_name: str | None
 ) -> BaseTokenizer:
     global _TOKENIZER_CACHE
-
     id_tuple = (model_provider, model_name)
 
     if id_tuple not in _TOKENIZER_CACHE:
-        if model_provider in [EmbeddingProvider.OPENAI, EmbeddingProvider.AZURE]:
-            if model_name is None:
-                raise ValueError(
-                    "model_name is required for OPENAI and AZURE embeddings"
-                )
+        tokenizer = None
 
-            _TOKENIZER_CACHE[id_tuple] = TiktokenTokenizer(model_name)
-            return _TOKENIZER_CACHE[id_tuple]
+        if model_name:
+            tokenizer = _try_initialize_tokenizer(model_name, model_provider)
 
-        try:
-            if model_name is None:
-                model_name = DOCUMENT_ENCODER_MODEL
-
-            logger.debug(f"Initializing HuggingFaceTokenizer for: {model_name}")
-            _TOKENIZER_CACHE[id_tuple] = HuggingFaceTokenizer(model_name)
-        except Exception as primary_error:
-            logger.error(
-                f"Error initializing HuggingFaceTokenizer for {model_name}: {primary_error}"
-            )
-            logger.warning(
+        if not tokenizer:
+            logger.info(
                 f"Falling back to default embedding model: {DOCUMENT_ENCODER_MODEL}"
             )
+            tokenizer = HuggingFaceTokenizer(DOCUMENT_ENCODER_MODEL)
 
-            try:
-                # Cache this tokenizer name to the default so we don't have to try to load it again
-                # and fail again
-                _TOKENIZER_CACHE[id_tuple] = HuggingFaceTokenizer(
-                    DOCUMENT_ENCODER_MODEL
-                )
-            except Exception as fallback_error:
-                logger.error(
-                    f"Error initializing fallback HuggingFaceTokenizer: {fallback_error}"
-                )
-                raise ValueError(
-                    f"Failed to initialize tokenizer for {model_name} and fallback model"
-                ) from fallback_error
+        _TOKENIZER_CACHE[id_tuple] = tokenizer
 
     return _TOKENIZER_CACHE[id_tuple]
 
 
+def _try_initialize_tokenizer(
+    model_name: str, model_provider: EmbeddingProvider | None
+) -> BaseTokenizer | None:
+    tokenizer: BaseTokenizer | None = None
+
+    if model_provider is not None:
+        # Try using TiktokenTokenizer first if model_provider exists
+        try:
+            tokenizer = TiktokenTokenizer(model_name)
+            logger.info(f"Initialized TiktokenTokenizer for: {model_name}")
+            return tokenizer
+        except Exception as tiktoken_error:
+            logger.debug(
+                f"TiktokenTokenizer not available for model {model_name}: {tiktoken_error}"
+            )
+    else:
+        # If no provider specified, try HuggingFaceTokenizer
+        try:
+            tokenizer = HuggingFaceTokenizer(model_name)
+            logger.info(f"Initialized HuggingFaceTokenizer for: {model_name}")
+            return tokenizer
+        except Exception as hf_error:
+            logger.warning(
+                f"Error initializing HuggingFaceTokenizer for {model_name}: {hf_error}"
+            )
+
+    # If both initializations fail, return None
+    return None
+
+
 _DEFAULT_TOKENIZER: BaseTokenizer = HuggingFaceTokenizer(DOCUMENT_ENCODER_MODEL)
 
 
 def get_tokenizer(
     model_name: str | None, provider_type: EmbeddingProvider | str | None
 ) -> BaseTokenizer:
-    if provider_type is not None:
-        if isinstance(provider_type, str):
-            try:
-                provider_type = EmbeddingProvider(provider_type)
-            except ValueError:
-                logger.debug(
-                    f"Invalid provider_type '{provider_type}'. Falling back to default tokenizer."
-                )
-                return _DEFAULT_TOKENIZER
-        return _check_tokenizer_cache(provider_type, model_name)
-    return _DEFAULT_TOKENIZER
+    if isinstance(provider_type, str):
+        try:
+            provider_type = EmbeddingProvider(provider_type)
+        except ValueError:
+            logger.debug(
+                f"Invalid provider_type '{provider_type}'. Falling back to default tokenizer."
+            )
+            return _DEFAULT_TOKENIZER
+    return _check_tokenizer_cache(provider_type, model_name)
 
 
 def tokenizer_trim_content(