diff --git a/tests/load_tests/test_vertex_embeddings_load_test.py b/tests/load_tests/test_vertex_embeddings_load_test.py
new file mode 100644
index 000000000..9ff961d73
--- /dev/null
+++ b/tests/load_tests/test_vertex_embeddings_load_test.py
@@ -0,0 +1,121 @@
+"""
+Load test on vertex AI embeddings to ensure vertex median response time is less than 300ms
+
+"""
+
+import sys
+import os
+
+sys.path.insert(0, os.path.abspath("../.."))
+
+import asyncio
+import litellm
+import pytest
+import time
+from statistics import mean, median
+import json
+import tempfile
+
+
+def load_vertex_ai_credentials():
+    # Define the path to the vertex_key.json file
+    print("loading vertex ai credentials")
+    filepath = os.path.dirname(os.path.abspath(__file__))
+    vertex_key_path = filepath + "/vertex_key.json"
+
+    # Read the existing content of the file or create an empty dictionary
+    try:
+        with open(vertex_key_path, "r") as file:
+            # Read the file content
+            print("Read vertexai file path")
+            content = file.read()
+
+            # If the file is empty or not valid JSON, create an empty dictionary
+            if not content or not content.strip():
+                service_account_key_data = {}
+            else:
+                # Attempt to load the existing JSON content
+                file.seek(0)
+                service_account_key_data = json.load(file)
+    except FileNotFoundError:
+        # If the file doesn't exist, create an empty dictionary
+        service_account_key_data = {}
+
+    # Update the service_account_key_data with environment variables
+    private_key_id = os.environ.get("VERTEX_AI_PRIVATE_KEY_ID", "")
+    private_key = os.environ.get("VERTEX_AI_PRIVATE_KEY", "")
+    private_key = private_key.replace("\\n", "\n")
+    service_account_key_data["private_key_id"] = private_key_id
+    service_account_key_data["private_key"] = private_key
+
+    # Create a temporary file
+    with tempfile.NamedTemporaryFile(mode="w+", delete=False) as temp_file:
+        # Write the updated content to the temporary files
+        json.dump(service_account_key_data, temp_file, indent=2)
+
+    # Export the temporary file as GOOGLE_APPLICATION_CREDENTIALS
+    os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = os.path.abspath(temp_file.name)
+
+
+async def create_async_vertex_embedding_task():
+    load_vertex_ai_credentials()
+    base_url = "https://exampleopenaiendpoint-production.up.railway.app/v1/projects/adroit-crow-413218/locations/us-central1/publishers/google/models/embedding-gecko-001:predict"
+    embedding_args = {
+        "model": "vertex_ai/textembedding-gecko",
+        "input": "This is a test sentence for embedding.",
+        "timeout": 10,
+        "api_base": base_url,
+    }
+    start_time = time.time()
+    response = await litellm.aembedding(**embedding_args)
+    end_time = time.time()
+    print(f"Vertex AI embedding time: {end_time - start_time:.2f} seconds")
+    return response, end_time - start_time
+
+
+async def run_load_test(duration_seconds, requests_per_second):
+    end_time = time.time() + duration_seconds
+    vertex_times = []
+
+    print(
+        f"Running Load Test for {duration_seconds} seconds at {requests_per_second} RPS..."
+    )
+    while time.time() < end_time:
+        vertex_tasks = [
+            create_async_vertex_embedding_task() for _ in range(requests_per_second)
+        ]
+
+        vertex_results = await asyncio.gather(*vertex_tasks)
+
+        vertex_times.extend([duration for _, duration in vertex_results])
+
+        # Sleep for 1 second to maintain the desired RPS
+        await asyncio.sleep(1)
+
+    return vertex_times
+
+
+def analyze_results(vertex_times):
+    median_vertex = median(vertex_times)
+    print(f"Vertex AI median response time: {median_vertex:.4f} seconds")
+
+    if median_vertex > 0.3:
+        pytest.fail(
+            f"Vertex AI median response time is greater than 300ms: {median_vertex:.4f} seconds"
+        )
+    else:
+        print("Performance is good")
+        return True
+
+
+@pytest.mark.asyncio
+async def test_embedding_performance():
+    """
+    Run load test on vertex AI embeddings to ensure vertex median response time is less than 300ms
+
+    20 RPS for 20 seconds
+    """
+    duration_seconds = 20
+    requests_per_second = 20
+    vertex_times = await run_load_test(duration_seconds, requests_per_second)
+    result = analyze_results(vertex_times)