diff --git a/sde_collections/utils/benchmark_patterns_execution.py b/sde_collections/utils/benchmark_patterns_execution.py
new file mode 100644
index 00000000..1616d8c8
--- /dev/null
+++ b/sde_collections/utils/benchmark_patterns_execution.py
@@ -0,0 +1,255 @@
+# Run the following command for execution
+# docker compose -f local.yml run --rm django python3 manage.py test sde_collections.utils.benchmark_patterns_execution
+
+
+import cProfile
+import io
+import pstats
+import random
+from functools import wraps
+from time import time
+from typing import Any
+
+from django.db import transaction
+from django.test import TestCase
+
+from sde_collections.models.collection import Collection
+from sde_collections.models.collection_choice_fields import Divisions, DocumentTypes
+from sde_collections.models.delta_patterns import (
+    DeltaDivisionPattern,
+    DeltaDocumentTypePattern,
+    DeltaExcludePattern,
+    DeltaIncludePattern,
+    DeltaTitlePattern,
+)
+from sde_collections.models.delta_url import CuratedUrl, DeltaUrl
+
+
+def profile_function(func):
+    """Decorator to profile a function and all its called functions."""
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        pr = cProfile.Profile()
+        pr.enable()
+        result = func(*args, **kwargs)
+        pr.disable()
+
+        s = io.StringIO()
+        ps = pstats.Stats(pr, stream=s).sort_stats("cumulative")
+        ps.print_stats()
+
+        return result, s.getvalue()
+
+    return wrapper
+
+
+class DeltaPatternsDetailedBenchmark(TestCase):
+    """Detailed benchmark suite for Delta Patterns including all called functions."""
+
+    def setUp(self):
+        """Set up test data with varying sizes using bulk operations."""
+        start_time = time()
+        print("\nSetting up test data...")
+
+        with transaction.atomic():
+            # Create collection
+            self.collection = Collection.objects.create(
+                name="Test Collection",
+                division=Divisions.ASTROPHYSICS,
+                document_type=DocumentTypes.DATA,
+            )
+
+            self.url_sizes = [10, 100, 1000, 10000, 50000, 100000, 150000, 200000]
+            max_size = max(self.url_sizes)
+
+            # Bulk create CuratedUrls in batches
+            batch_size = 1000
+            for i in range(0, max_size, batch_size):
+                batch_end = min(i + batch_size, max_size)
+                print(f"Creating CuratedUrls batch {i} to {batch_end}")
+
+                curated_urls = [
+                    CuratedUrl(
+                        collection=self.collection,
+                        url=f"https://example.com/doc/{j}",
+                        scraped_title=f"Document {j}",
+                        generated_title=f"Generated Document {j}",
+                        document_type=random.choice(list(DocumentTypes.values)),
+                        division=random.choice(list(Divisions.values)),
+                    )
+                    for j in range(i, batch_end)
+                ]
+                CuratedUrl.objects.bulk_create(curated_urls)
+
+        setup_time = time() - start_time
+        print(f"Setup completed in {setup_time:.2f} seconds")
+
+    def tearDown(self):
+        """Clean up test data."""
+        print("\nCleaning up test data...")
+        start_time = time()
+
+        with transaction.atomic():
+            DeltaUrl.objects.all().delete()
+            CuratedUrl.objects.all().delete()
+            Collection.objects.all().delete()
+
+        cleanup_time = time() - start_time
+        print(f"Cleanup completed in {cleanup_time:.2f} seconds")
+
+    def _get_pattern_metrics(self, profile_output: str) -> dict[str, list[dict[str, Any]]]:
+        """Parse cProfile output and categorize metrics by operation type."""
+        metrics = []
+        lines = [line for line in profile_output.split("\n") if line.strip() and "function calls" not in line]
+
+        for line in lines:
+            if line.strip() and len(line.split()) >= 6:
+                parts = line.split()
+                try:
+                    metrics.append(
+                        {
+                            "ncalls": parts[0],
+                            "tottime": float(parts[1]),
+                            "percall": float(parts[2]),
+                            "cumtime": float(parts[3]),
+                            "percall_cum": float(parts[4]),
+                            "function_name": " ".join(parts[5:]),
+                        }
+                    )
+                except (ValueError, IndexError):
+                    continue
+
+        # Categorize metrics
+        categorized_metrics = {
+            "curated_url_ops": [m for m in metrics if "CuratedUrl" in m["function_name"]],
+            "delta_url_ops": [m for m in metrics if "DeltaUrl" in m["function_name"]],
+            "pattern_ops": [m for m in metrics if "Pattern" in m["function_name"]],
+            "db_ops": [m for m in metrics if "django.db" in m["function_name"]],
+            "other_ops": [
+                m
+                for m in metrics
+                if not any(x in m["function_name"] for x in ["CuratedUrl", "DeltaUrl", "Pattern", "django.db"])
+            ],
+        }
+
+        return categorized_metrics
+
+    def _benchmark_pattern_with_profiling(self, pattern_class, pattern_data):
+        """Run detailed benchmarks with categorized metrics."""
+        results = []
+
+        for size in self.url_sizes:
+            print(f"\nTesting with dataset size: {size}")
+            pattern_instance = pattern_class.objects.create(
+                collection=self.collection, match_pattern="https://example.com/doc/*", **pattern_data
+            )
+
+            # Profile operations
+            apply_func = profile_function(pattern_instance.apply)
+            unapply_func = profile_function(pattern_instance.unapply)
+
+            _, apply_profile = apply_func()
+            _, unapply_profile = unapply_func()
+
+            # Get categorized metrics
+            apply_metrics = self._get_pattern_metrics(apply_profile)
+            unapply_metrics = self._get_pattern_metrics(unapply_profile)
+
+            results.append(
+                {
+                    "pattern_type": pattern_class.__name__,
+                    "dataset_size": size,
+                    "apply_metrics": apply_metrics,
+                    "unapply_metrics": unapply_metrics,
+                }
+            )
+
+            pattern_instance.delete()
+
+        return results
+
+    def _print_detailed_results(self, pattern_name: str, results: list[dict[str, Any]]):
+        """Print detailed profiling results with categorized metrics."""
+        print(f"\n{'='*100}")
+        print(f"Detailed Profile for {pattern_name}")
+        print(f"{'='*100}")
+
+        categories = ["curated_url_ops", "delta_url_ops", "pattern_ops", "db_ops", "other_ops"]
+
+        for result in results:
+            size = result["dataset_size"]
+            print(f"\nDataset Size: {size}")
+
+            for operation in ["apply_metrics", "unapply_metrics"]:
+                op_name = operation.upper().replace("_METRICS", "")
+                print(f"\n{op_name} OPERATION")
+
+                for category in categories:
+                    metrics = result[operation][category]
+                    if metrics:
+                        print(f"\n{category.replace('_', ' ').title()}:")
+                        print(f"{'Function Name':<50} | {'Calls':<8} | {'Total Time':<12} | {'Cumulative':<12}")
+                        print("-" * 86)
+
+                        sorted_metrics = sorted(metrics, key=lambda x: float(x["cumtime"]), reverse=True)
+                        for metric in sorted_metrics[:5]:  # Show top 5 for each category
+                            print(
+                                f"{metric['function_name']:<50} | "
+                                f"{metric['ncalls']:<8} | "
+                                f"{metric['tottime']:<12.4f} | "
+                                f"{metric['cumtime']:<12.4f}"
+                            )
+
+    def test_all_patterns(self):
+        """Run benchmarks for all pattern types."""
+        pattern_configs = [
+            (DeltaExcludePattern, {"reason": "Test exclusion"}, "Exclude Pattern"),
+            (DeltaIncludePattern, {}, "Include Pattern"),
+            (DeltaDivisionPattern, {"division": Divisions.ASTROPHYSICS}, "Division Pattern"),
+            (DeltaDocumentTypePattern, {"document_type": DocumentTypes.DATA}, "Document Type Pattern"),
+            (DeltaTitlePattern, {"title_pattern": "{title} - {collection}"}, "Title Pattern"),
+        ]
+
+        all_results = []
+        for pattern_class, pattern_data, pattern_name in pattern_configs:
+            print(f"\nBenchmarking {pattern_name}...")
+            results = self._benchmark_pattern_with_profiling(pattern_class, pattern_data)
+            self._print_detailed_results(pattern_name, results)
+            all_results.extend(results)
+
+        self._print_comparative_summary(all_results)
+
+    def _print_comparative_summary(self, all_results):
+        """Print a comparative summary with operation categories."""
+        print("\n" + "=" * 100)
+        print("COMPARATIVE SUMMARY")
+        print("=" * 100)
+
+        for size in self.url_sizes:
+            print(f"\nDataset Size: {size}")
+            size_results = [r for r in all_results if r["dataset_size"] == size]
+
+            for result in size_results:
+                pattern_type = result["pattern_type"]
+                print(f"\n{pattern_type}")
+
+                for operation in ["apply_metrics", "unapply_metrics"]:
+                    op_name = operation.upper().replace("_METRICS", "")
+                    metrics = result[operation]
+
+                    total_time = sum(sum(float(m["tottime"]) for m in category) for category in metrics.values())
+
+                    curated_time = sum(float(m["tottime"]) for m in metrics["curated_url_ops"])
+                    delta_time = sum(float(m["tottime"]) for m in metrics["delta_url_ops"])
+                    pattern_time = sum(float(m["tottime"]) for m in metrics["pattern_ops"])
+                    db_time = sum(float(m["tottime"]) for m in metrics["db_ops"])
+
+                    print(f"\n{op_name}:")
+                    print(f"{'Operation Type':<20} | {'Time (s)':<10} | {'% of Total':<10}")
+                    print("-" * 50)
+                    print(f"{'CuratedUrl Ops':<20} | {curated_time:<10.4f} | {(curated_time/total_time)*100:<10.1f}")
+                    print(f"{'DeltaUrl Ops':<20} | {delta_time:<10.4f} | {(delta_time/total_time)*100:<10.1f}")
+                    print(f"{'Pattern Ops':<20} | {pattern_time:<10.4f} | {(pattern_time/total_time)*100:<10.1f}")
+                    print(f"{'Database Ops':<20} | {db_time:<10.4f} | {(db_time/total_time)*100:<10.1f}")
+                    print(f"{'Total':<20} | {total_time:<10.4f} | 100.0")