diff --git a/deployer/commands/transform/cost_table.py b/deployer/commands/transform/cost_table.py
index 36fe96929..12aa265b2 100644
--- a/deployer/commands/transform/cost_table.py
+++ b/deployer/commands/transform/cost_table.py
@@ -130,8 +130,10 @@ def gcp(
     We assume the input CSV file has the following columns (and are subject to
     changes by GCP):
     - Project name
-    - Month
     - Subtotal ($)
+    - Month *
+
+    (*) The Month column will be missing if the report is generated for a single month.
 
     We aim to have an output CSV file with columns:
     - Project name
@@ -142,17 +144,41 @@ def gcp(
 
     where:
     - start-month...end-month are unique values from the Month column in the
-      input file
+      input file or from the name of the input file if the Month column is missing.
     - Project name are unique entries from the input file
     - The Total column is the sum across all month columns for each project
     """
-    # Read the CSV file into a pandas dataframe. Only select relevant columns from
-    # the input file: [Project Name, Month, Subtotal ($)]. Rename the columns so
-    # that they are all lower case and any whitespace in column names is replaced
-    # with an underscore.
     df = pd.read_csv(
-        input_path, usecols=["Month", "Project name", "Subtotal ($)"]
-    ).rename(columns=lambda col: col.strip().lower().replace(" ", "_"))
+        input_path,
+    )
+
+    # We only need the following columns from the input file
+    required_columns = ["Month", "Project name", "Subtotal ($)"]
+
+    # Filter the columns that actually exist in the dataframe
+    existing_columns = [col for col in required_columns if col in df.columns]
+
+    # Select only the existing columns and rename them so that they
+    # are all lower case and replace any whitespace in column names
+    # with an underscore.
+    df = df[existing_columns].rename(
+        columns=lambda col: col.strip().lower().replace(" ", "_")
+    )
+
+    if "Month" not in existing_columns:
+        # If group by month wasn't possible, it means we have a single month
+        # and we extract it from the file name
+        match = re.search(r"(\d{4}-\d{2})-\d{2} — (\d{4}-\d{2})-\d{2}", input_path.name)
+        if not match:
+            raise ValueError(
+                "Month period not found in file name. Try downloading the report again from GCP without renaming it."
+            )
+        start_month_year = match.group(1)
+        end_month_year = match.group(2)
+        assert start_month_year == end_month_year
+
+        # Assign the extracted month period to the dataframe
+        df = df.assign(month=start_month_year)
 
     # Aggregate and pivot the dataframe into desired format
     transformed_df = (
diff --git a/docs/howto/bill.md b/docs/howto/bill.md
index 886f5fa87..b7addeb76 100644
--- a/docs/howto/bill.md
+++ b/docs/howto/bill.md
@@ -89,11 +89,16 @@ Currently this is the recommended way of retrieving the costs from GCP.
 1. Select 'Reports' on the left sidebar
 1. Under time range on the right sidebar, select 'Invoice Month'
 1. Select the time range you are interested in. Note that this has to be at least two months right now, or the next step does not work
-1. Under 'Group by', select 'Month -> Project'.
+1. Under 'Group by', select 'Month -> Project' or just 'Project' if the time range is only one month
 1. Under the chart, click the 'Download CSV' button.
+    ```{important}
+   It is important to not change the name of the file downloaded from GCP as it is used by the deployer to incur the month we are billing for.
+   ```
+
    ```{figure} ../images/gcp-billing-ui.png
    GCP billing UI
    ```
+
 1. Run the following deployer command to convert the generated CSV file into the format required for the invoicing process.
    
    ```bash