diff --git a/components/seldon-request-logger/app/default_logger.py b/components/seldon-request-logger/app/default_logger.py
index 0ff3cc5dd9..74dad8a566 100644
--- a/components/seldon-request-logger/app/default_logger.py
+++ b/components/seldon-request-logger/app/default_logger.py
@@ -16,6 +16,7 @@
 import array
 import traceback
 from flask import jsonify
+from elasticsearch import Elasticsearch, helpers
 
 MAX_PAYLOAD_BYTES = 300000
 app = Flask(__name__)
@@ -146,29 +147,8 @@ def process_and_update_elastic_doc(
         if type(new_content_part["instance"]) == type([]) and not (new_content_part["dataType"] == "json"):
             # if we've a list then this is batch
             # we assume first dimension is always batch
-
-            no_items_in_batch = len(new_content_part["instance"])
-            index = 0
-            elements = None
-            if "elements" in new_content_part:
-                elements = new_content_part["elements"]
-
-            for num, item in enumerate(new_content_part["instance"],start=0):
-
-                item_body = doc_body.copy()
-
-                item_body[message_type]["instance"] = item
-
-                if type(elements) == type([]) and len(elements) > num:
-                    item_body[message_type]["elements"] = elements[num]
-
-                item_request_id = build_request_id_batched(
-                    request_id, no_items_in_batch, index
-                )
-                added_content.append(upsert_doc_to_elastic(
-                    elastic_object, message_type, item_body, item_request_id, index_name
-                ))
-                index = index + 1
+            bulk_upsert_doc_to_elastic(elastic_object, message_type, doc_body,
+                                       doc_body[message_type].copy(), request_id, index_name)
         else:
             #not batch so don't batch elements either
             if "elements" in new_content_part and type(new_content_part["elements"]) == type([]):
@@ -301,6 +281,51 @@ def upsert_doc_to_elastic(
     return new_content
 
 
+def bulk_upsert_doc_to_elastic(
+        elastic_object: Elasticsearch, message_type, doc_body, new_content_part, request_id, index_name
+    ):
+    log_mapping.get_log_metadata(elastic_object, message_type, doc_body, request_id, index_name)
+    no_items_in_batch = len(new_content_part["instance"])
+    elements = None
+    if "elements" in new_content_part:
+        elements = new_content_part["elements"]
+
+    def gen_data():
+        for num, item in enumerate(new_content_part["instance"], start=0):
+
+            item_body = doc_body.copy()
+            item_body[message_type]["instance"] = item
+            if type(elements) == type([]) and len(elements) > num:
+                item_body[message_type]["elements"] = elements[num]
+
+            item_request_id = build_request_id_batched(
+                    request_id, no_items_in_batch, num
+                )
+
+            print(
+                "bulk upserting to doc "
+                + index_name
+                + "/"
+                + (log_helper.DOC_TYPE_NAME if log_helper.DOC_TYPE_NAME != None else "_doc")
+                + "/"
+                + request_id
+                + " adding "
+                + message_type
+            )
+
+            yield {
+                "_index": index_name,
+                "_type": log_helper.DOC_TYPE_NAME,
+                "_op_type": "update",
+                "_id": item_request_id,
+                "_source": {"doc_as_upsert": True, "doc": item_body},
+            }
+
+    helpers.bulk(
+        elastic_object, gen_data(), refresh=True
+    )
+
+
 # take request or response part and process it by deriving metadata
 def process_content(message_type, content, headers):