diff --git a/src/brad/planner/triggers/variable_costs.py b/src/brad/planner/triggers/variable_costs.py
index 9ac518be..c583535a 100644
--- a/src/brad/planner/triggers/variable_costs.py
+++ b/src/brad/planner/triggers/variable_costs.py
@@ -3,7 +3,7 @@
 import pytz
 import pandas as pd
 from datetime import datetime, timedelta
-from typing import List
+from typing import List, Tuple
 
 from .trigger import Trigger
 from brad.config.engine import Engine
@@ -60,7 +60,13 @@ async def should_replan(self) -> bool:
             )
             return False
 
-        current_hourly_cost = await self._estimate_current_scan_hourly_cost()
+        aurora_cost, athena_cost = await self._estimate_current_scan_hourly_cost()
+
+        if self._planner_config.use_io_optimized_aurora():
+            current_hourly_cost = athena_cost
+        else:
+            current_hourly_cost = athena_cost + aurora_cost
+
         if current_hourly_cost <= 1e-5:
             # Treated as 0.
             logger.debug(
@@ -85,9 +91,9 @@ async def should_replan(self) -> bool:
 
         return False
 
-    async def _estimate_current_scan_hourly_cost(self) -> float:
+    async def _estimate_current_scan_hourly_cost(self) -> Tuple[float, float]:
         if self._current_blueprint is None:
-            return 0.0
+            return 0.0, 0.0
 
         # Extract the queries seen in the last window.
         window_end = datetime.now()
@@ -107,7 +113,7 @@ async def _estimate_current_scan_hourly_cost(self) -> float:
             )
         )
         if len(workload.analytical_queries()) == 0:
-            return 0.0
+            return 0.0, 0.0
         self._data_access_provider.apply_access_statistics(workload)
 
         # Compute the scan cost of this last window of queries.
@@ -120,7 +126,12 @@ async def _estimate_current_scan_hourly_cost(self) -> float:
         )
 
         for idx, q in enumerate(workload.analytical_queries()):
-            engine = await router.engine_for(q)
+            maybe_engine = q.primary_execution_location()
+            if maybe_engine is None:
+                engine = await router.engine_for(q)
+            else:
+                engine = maybe_engine
+
             if engine == Engine.Aurora:
                 aurora_query_indices.append(idx)
                 aurora_queries.append(q)
@@ -128,6 +139,7 @@ async def _estimate_current_scan_hourly_cost(self) -> float:
                 athena_query_indices.append(idx)
                 athena_queries.append(q)
 
+        # NOTE: Ideally we use the actual values.
         aurora_accessed_pages = compute_aurora_accessed_pages(
             aurora_queries,
             workload.get_predicted_aurora_pages_accessed_batch(aurora_query_indices),
@@ -139,10 +151,11 @@ async def _estimate_current_scan_hourly_cost(self) -> float:
         )
 
         # We use the hit rate to estimate Aurora scan costs.
+        # Note that if we are using I/O optimized Aurora, there is no
+        # incremental scan cost.
         lookback_epochs = math.ceil(
             self._planner_config.planning_window() / self._config.epoch_length
         )
-        # TODO: If there are read replicas, we should use the hit rate from them instead.
         aurora_reader_metrics = self._monitor.aurora_reader_metrics()
         if len(aurora_reader_metrics) > 0:
             reader_hit_rates = []
@@ -168,7 +181,7 @@ async def _estimate_current_scan_hourly_cost(self) -> float:
             athena_scanned_bytes, self._planner_config
         )
 
-        return aurora_scan_cost + athena_scan_cost
+        return aurora_scan_cost, athena_scan_cost
 
 
 _HIT_RATE_METRIC = "BufferCacheHitRatio_Average"