fix: change cost calculation logs from INFO to DEBUG level

litellm/cost_calculator.py

@@ -657,7 +657,7 @@ def completion_cost(  # noqa: PLR0915
             potential_model_names.append(model)
         for idx, model in enumerate(potential_model_names):
             try:
-                verbose_logger.info(
+                verbose_logger.debug(
                     f"selected model name for cost calculation: {model}"
                 )
 
@@ -1176,7 +1176,7 @@ def batch_cost_calculator(
         model=model, custom_llm_provider=custom_llm_provider
     )
 
-    verbose_logger.info(
+    verbose_logger.debug(
         "Calculating batch cost per token. model=%s, custom_llm_provider=%s",
         model,
         custom_llm_provider,

tests/test_litellm/test_cost_calculation_log_level.py

@@ -0,0 +1,93 @@
+"""Test that cost calculation uses appropriate log levels"""
+import logging
+import os
+import sys
+
+import pytest
+
+sys.path.insert(0, os.path.abspath("../../.."))
+
+import litellm
+from litellm import completion_cost
+
+
+def test_cost_calculation_uses_debug_level(caplog):
+    """
+    Test that cost calculation logs use DEBUG level instead of INFO.
+    This ensures cost calculation details don't appear in production logs.
+    Part of fix for issue #9815.
+    """
+    # Create a mock completion response
+    mock_response = {
+        "id": "test",
+        "object": "chat.completion",
+        "created": 1234567890,
+        "model": "gpt-3.5-turbo",
+        "choices": [{
+            "index": 0,
+            "message": {"role": "assistant", "content": "Test response"},
+            "finish_reason": "stop"
+        }],
+        "usage": {
+            "prompt_tokens": 10,
+            "completion_tokens": 20,
+            "total_tokens": 30
+        }
+    }
+
+    # Test that cost calculation logs are at DEBUG level
+    with caplog.at_level(logging.DEBUG):
+        try:
+            cost = completion_cost(
+                completion_response=mock_response,
+                model="gpt-3.5-turbo"
+            )
+        except Exception:
+            pass  # Cost calculation may fail, but we're checking log levels
+
+    # Find the cost calculation log records
+    cost_calc_records = [
+        record for record in caplog.records 
+        if "selected model name for cost calculation" in record.message
+    ]
+
+    # Verify that cost calculation logs are at DEBUG level
+    assert len(cost_calc_records) > 0, "No cost calculation logs found"
+
+    for record in cost_calc_records:
+        assert record.levelno == logging.DEBUG, \
+            f"Cost calculation log should be DEBUG level, but was {record.levelname}"
+
+
+def test_batch_cost_calculation_uses_debug_level(caplog):
+    """
+    Test that batch cost calculation logs also use DEBUG level.
+    """
+    from litellm.cost_calculator import batch_cost_calculator
+    from litellm.types.utils import Usage
+
+    # Create a mock usage object
+    usage = Usage(prompt_tokens=100, completion_tokens=200, total_tokens=300)
+
+    # Test that batch cost calculation logs are at DEBUG level
+    with caplog.at_level(logging.DEBUG):
+        try:
+            batch_cost_calculator(
+                usage=usage,
+                model="gpt-3.5-turbo",
+                custom_llm_provider="openai"
+            )
+        except Exception:
+            pass  # May fail, but we're checking log levels
+
+    # Find batch cost calculation log records
+    batch_cost_records = [
+        record for record in caplog.records 
+        if "Calculating batch cost per token" in record.message
+    ]
+
+    # Verify logs exist and are at DEBUG level
+    if batch_cost_records:  # May not always log depending on the code path
+        for record in batch_cost_records:
+            assert record.levelno == logging.DEBUG, \
+                f"Batch cost calculation log should be DEBUG level, but was {record.levelname}"