diff --git a/docarray/index/backends/elastic.py b/docarray/index/backends/elastic.py
index 7edb04ad83e..c4da3ad5e0d 100644
--- a/docarray/index/backends/elastic.py
+++ b/docarray/index/backends/elastic.py
@@ -1,5 +1,4 @@
 # mypy: ignore-errors
-import uuid
 import warnings
 from collections import defaultdict
 from dataclasses import dataclass, field
@@ -74,12 +73,6 @@ def __init__(self, db_config=None, **kwargs):
         self._logger.debug('Elastic Search index is being initialized')
 
         # ElasticSearch client creation
-        if self._db_config.index_name is None:
-            id = uuid.uuid4().hex
-            self._db_config.index_name = 'index__' + id
-
-        self._index_name = self._db_config.index_name
-
         self._client = Elasticsearch(
             hosts=self._db_config.hosts,
             **self._db_config.es_config,
@@ -108,7 +101,7 @@ def __init__(self, db_config=None, **kwargs):
             mappings['properties'][col_name] = self._create_index_mapping(col)
 
         # print(mappings['properties'])
-        if self._client.indices.exists(index=self._index_name):
+        if self._client.indices.exists(index=self.index_name):
             self._client_put_mapping(mappings)
         else:
             self._client_create(mappings)
@@ -116,7 +109,20 @@ def __init__(self, db_config=None, **kwargs):
         if len(self._db_config.index_settings):
             self._client_put_settings(self._db_config.index_settings)
 
-        self._refresh(self._index_name)
+        self._refresh(self.index_name)
+
+    @property
+    def index_name(self):
+        default_index_name = (
+            self._schema.__name__.lower() if self._schema is not None else None
+        )
+        if default_index_name is None:
+            raise ValueError(
+                'A ElasticDocIndex must be typed with a Document type.'
+                'To do so, use the syntax: ElasticDocIndex[DocumentType]'
+            )
+
+        return self._db_config.index_name or default_index_name
 
     ###############################################
     # Inner classes for query builder and configs #
@@ -333,7 +339,7 @@ def _index(
 
         for row in data:
             request = {
-                '_index': self._index_name,
+                '_index': self.index_name,
                 '_id': row['id'],
             }
             for col_name, col in self._column_infos.items():
@@ -349,13 +355,13 @@ def _index(
             warnings.warn(str(info))
 
         if refresh:
-            self._refresh(self._index_name)
+            self._refresh(self.index_name)
 
     def num_docs(self) -> int:
         """
         Get the number of documents.
         """
-        return self._client.count(index=self._index_name)['count']
+        return self._client.count(index=self.index_name)['count']
 
     def _del_items(
         self,
@@ -365,7 +371,7 @@ def _del_items(
         requests = []
         for _id in doc_ids:
             requests.append(
-                {'_op_type': 'delete', '_index': self._index_name, '_id': _id}
+                {'_op_type': 'delete', '_index': self.index_name, '_id': _id}
             )
 
         _, warning_info = self._send_requests(requests, chunk_size)
@@ -375,7 +381,7 @@ def _del_items(
             ids = [info['delete']['_id'] for info in warning_info]
             warnings.warn(f'No document with id {ids} found')
 
-        self._refresh(self._index_name)
+        self._refresh(self.index_name)
 
     def _get_items(self, doc_ids: Sequence[str]) -> Sequence[TSchema]:
         accumulated_docs = []
@@ -416,7 +422,7 @@ def execute_query(self, query: Dict[str, Any], *args, **kwargs) -> Any:
                 f'args and kwargs not supported for `execute_query` on {type(self)}'
             )
 
-        resp = self._client.search(index=self._index_name, **query)
+        resp = self._client.search(index=self.index_name, **query)
         docs, scores = self._format_response(resp)
 
         return _FindResult(documents=docs, scores=parse_obj_as(NdArray, scores))
@@ -440,7 +446,7 @@ def _find_batched(
     ) -> _FindResultBatched:
         request = []
         for query in queries:
-            head = {'index': self._index_name}
+            head = {'index': self.index_name}
             body = self._form_search_body(query, limit, search_field)
             request.extend([head, body])
 
@@ -469,7 +475,7 @@ def _filter_batched(
     ) -> List[List[Dict]]:
         request = []
         for query in filter_queries:
-            head = {'index': self._index_name}
+            head = {'index': self.index_name}
             body = {'query': query, 'size': limit}
             request.extend([head, body])
 
@@ -499,7 +505,7 @@ def _text_search_batched(
     ) -> _FindResultBatched:
         request = []
         for query in queries:
-            head = {'index': self._index_name}
+            head = {'index': self.index_name}
             body = self._form_text_search_body(query, limit, search_field)
             request.extend([head, body])
 
@@ -615,20 +621,20 @@ def _refresh(self, index_name: str):
 
     def _client_put_mapping(self, mappings: Dict[str, Any]):
         self._client.indices.put_mapping(
-            index=self._index_name, properties=mappings['properties']
+            index=self.index_name, properties=mappings['properties']
         )
 
     def _client_create(self, mappings: Dict[str, Any]):
-        self._client.indices.create(index=self._index_name, mappings=mappings)
+        self._client.indices.create(index=self.index_name, mappings=mappings)
 
     def _client_put_settings(self, settings: Dict[str, Any]):
-        self._client.indices.put_settings(index=self._index_name, settings=settings)
+        self._client.indices.put_settings(index=self.index_name, settings=settings)
 
     def _client_mget(self, ids: Sequence[str]):
-        return self._client.mget(index=self._index_name, ids=ids)
+        return self._client.mget(index=self.index_name, ids=ids)
 
     def _client_search(self, **kwargs):
-        return self._client.search(index=self._index_name, **kwargs)
+        return self._client.search(index=self.index_name, **kwargs)
 
     def _client_msearch(self, request: List[Dict[str, Any]]):
-        return self._client.msearch(index=self._index_name, searches=request)
+        return self._client.msearch(index=self.index_name, searches=request)
diff --git a/docarray/index/backends/elasticv7.py b/docarray/index/backends/elasticv7.py
index 623f11053bb..1782e921f62 100644
--- a/docarray/index/backends/elasticv7.py
+++ b/docarray/index/backends/elasticv7.py
@@ -119,7 +119,7 @@ def execute_query(self, query: Dict[str, Any], *args, **kwargs) -> Any:
                 f'args and kwargs not supported for `execute_query` on {type(self)}'
             )
 
-        resp = self._client.search(index=self._index_name, body=query)
+        resp = self._client.search(index=self.index_name, body=query)
         docs, scores = self._format_response(resp)
 
         return _FindResult(documents=docs, scores=parse_obj_as(NdArray, scores))
@@ -161,20 +161,20 @@ def _form_search_body(self, query: np.ndarray, limit: int, search_field: str = '
     ###############################################
 
     def _client_put_mapping(self, mappings: Dict[str, Any]):
-        self._client.indices.put_mapping(index=self._index_name, body=mappings)
+        self._client.indices.put_mapping(index=self.index_name, body=mappings)
 
     def _client_create(self, mappings: Dict[str, Any]):
         body = {'mappings': mappings}
-        self._client.indices.create(index=self._index_name, body=body)
+        self._client.indices.create(index=self.index_name, body=body)
 
     def _client_put_settings(self, settings: Dict[str, Any]):
-        self._client.indices.put_settings(index=self._index_name, body=settings)
+        self._client.indices.put_settings(index=self.index_name, body=settings)
 
     def _client_mget(self, ids: Sequence[str]):
-        return self._client.mget(index=self._index_name, body={'ids': ids})
+        return self._client.mget(index=self.index_name, body={'ids': ids})
 
     def _client_search(self, **kwargs):
-        return self._client.search(index=self._index_name, body=kwargs)
+        return self._client.search(index=self.index_name, body=kwargs)
 
     def _client_msearch(self, request: List[Dict[str, Any]]):
-        return self._client.msearch(index=self._index_name, body=request)
+        return self._client.msearch(index=self.index_name, body=request)
diff --git a/docarray/index/backends/qdrant.py b/docarray/index/backends/qdrant.py
index d60e9daf7fa..e2e503d593c 100644
--- a/docarray/index/backends/qdrant.py
+++ b/docarray/index/backends/qdrant.py
@@ -45,7 +45,6 @@
 
 TSchema = TypeVar('TSchema', bound=BaseDoc)
 
-
 QDRANT_PY_VECTOR_TYPES: List[Any] = [np.ndarray, AbstractTensor]
 if torch_imported:
     import torch
@@ -86,6 +85,19 @@ def __init__(self, db_config=None, **kwargs):
         self._initialize_collection()
         self._logger.info(f'{self.__class__.__name__} has been initialized')
 
+    @property
+    def collection_name(self):
+        default_collection_name = (
+            self._schema.__name__.lower() if self._schema is not None else None
+        )
+        if default_collection_name is None:
+            raise ValueError(
+                'A QdrantDocumentIndex must be typed with a Document type.'
+                'To do so, use the syntax: QdrantDocumentIndex[DocumentType]'
+            )
+
+        return self._db_config.collection_name or default_collection_name
+
     @dataclass
     class Query:
         """Dataclass describing a query."""
@@ -211,7 +223,7 @@ class DBConfig(BaseDocIndex.DBConfig):
         timeout: Optional[float] = None
         host: Optional[str] = None
         path: Optional[str] = None
-        collection_name: str = 'documents'
+        collection_name: Optional[str] = None
         shard_number: Optional[int] = None
         replication_factor: Optional[int] = None
         write_consistency_factor: Optional[int] = None
@@ -250,7 +262,7 @@ def python_type_to_db_type(self, python_type: Type) -> Any:
 
     def _initialize_collection(self):
         try:
-            self._client.get_collection(self._db_config.collection_name)
+            self._client.get_collection(self.collection_name)
         except (UnexpectedResponse, RpcError, ValueError):
             vectors_config = {
                 column_name: self._to_qdrant_vector_params(column_info)
@@ -258,7 +270,7 @@ def _initialize_collection(self):
                 if column_info.db_type == 'vector'
             }
             self._client.create_collection(
-                collection_name=self._db_config.collection_name,
+                collection_name=self.collection_name,
                 vectors_config=vectors_config,
                 shard_number=self._db_config.shard_number,
                 replication_factor=self._db_config.replication_factor,
@@ -270,7 +282,7 @@ def _initialize_collection(self):
                 quantization_config=self._db_config.quantization_config,
             )
             self._client.create_payload_index(
-                collection_name=self._db_config.collection_name,
+                collection_name=self.collection_name,
                 field_name='__generated_vectors',
                 field_schema=rest.PayloadSchemaType.KEYWORD,
             )
@@ -280,7 +292,7 @@ def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]):
         # TODO: add batching the documents to avoid timeouts
         points = [self._build_point_from_row(row) for row in rows]
         self._client.upsert(
-            collection_name=self._db_config.collection_name,
+            collection_name=self.collection_name,
             points=points,
         )
 
@@ -288,7 +300,7 @@ def num_docs(self) -> int:
         """
         Get the number of documents.
         """
-        return self._client.count(collection_name=self._db_config.collection_name).count
+        return self._client.count(collection_name=self.collection_name).count
 
     def _del_items(self, doc_ids: Sequence[str]):
         items = self._get_items(doc_ids)
@@ -298,7 +310,7 @@ def _del_items(self, doc_ids: Sequence[str]):
             raise KeyError('Document keys could not found: %s' % ','.join(missing_keys))
 
         self._client.delete(
-            collection_name=self._db_config.collection_name,
+            collection_name=self.collection_name,
             points_selector=rest.PointIdsList(
                 points=[self._to_qdrant_id(doc_id) for doc_id in doc_ids],
             ),
@@ -308,7 +320,7 @@ def _get_items(
         self, doc_ids: Sequence[str]
     ) -> Union[Sequence[TSchema], Sequence[Dict[str, Any]]]:
         response, _ = self._client.scroll(
-            collection_name=self._db_config.collection_name,
+            collection_name=self.collection_name,
             scroll_filter=rest.Filter(
                 must=[
                     rest.HasIdCondition(
@@ -343,7 +355,7 @@ def execute_query(self, query: Union[Query, RawQuery], *args, **kwargs) -> DocLi
             # We perform semantic search with some vectors with Qdrant's search method
             # should be called
             points = self._client.search(  # type: ignore[assignment]
-                collection_name=self._db_config.collection_name,
+                collection_name=self.collection_name,
                 query_vector=(query.vector_field, query.vector_query),  # type: ignore[arg-type]
                 query_filter=rest.Filter(
                     must=[query.filter],
@@ -364,7 +376,7 @@ def execute_query(self, query: Union[Query, RawQuery], *args, **kwargs) -> DocLi
         else:
             # Just filtering, so Qdrant's scroll has to be used instead
             points, _ = self._client.scroll(  # type: ignore[assignment]
-                collection_name=self._db_config.collection_name,
+                collection_name=self.collection_name,
                 scroll_filter=query.filter,
                 limit=query.limit,
                 with_payload=True,
@@ -388,7 +400,7 @@ def _execute_raw_query(
             if search_params:
                 search_params = rest.SearchParams.parse_obj(search_params)  # type: ignore[assignment]
             points = self._client.search(  # type: ignore[assignment]
-                collection_name=self._db_config.collection_name,
+                collection_name=self.collection_name,
                 query_vector=query.pop('vector'),
                 query_filter=payload_filter,
                 search_params=search_params,
@@ -397,7 +409,7 @@ def _execute_raw_query(
         else:
             # Just filtering, so Qdrant's scroll has to be used instead
             points, _ = self._client.scroll(  # type: ignore[assignment]
-                collection_name=self._db_config.collection_name,
+                collection_name=self.collection_name,
                 scroll_filter=payload_filter,
                 **query,
             )
@@ -417,7 +429,7 @@ def _find_batched(
         self, queries: np.ndarray, limit: int, search_field: str = ''
     ) -> _FindResultBatched:
         responses = self._client.search_batch(
-            collection_name=self._db_config.collection_name,
+            collection_name=self.collection_name,
             requests=[
                 rest.SearchRequest(
                     vector=rest.NamedVector(
@@ -470,7 +482,7 @@ def _filter_batched(
             # There is no batch scroll available in Qdrant client yet, so we need to
             # perform the queries one by one. It will be changed in the future versions.
             response, _ = self._client.scroll(
-                collection_name=self._db_config.collection_name,
+                collection_name=self.collection_name,
                 scroll_filter=filter_query,
                 limit=limit,
                 with_payload=True,
diff --git a/docarray/index/backends/weaviate.py b/docarray/index/backends/weaviate.py
index 368992645e2..5179f8cb588 100644
--- a/docarray/index/backends/weaviate.py
+++ b/docarray/index/backends/weaviate.py
@@ -111,6 +111,17 @@ def __init__(self, db_config=None, **kwargs) -> None:
         self._set_properties()
         self._create_schema()
 
+    @property
+    def index_name(self):
+        default_index_name = self._schema.__name__ if self._schema is not None else None
+        if default_index_name is None:
+            raise ValueError(
+                'A WeaviateDocumentIndex must be typed with a Document type.'
+                'To do so, use the syntax: WeaviateDocumentIndex[DocumentType]'
+            )
+
+        return self._db_config.index_name or default_index_name
+
     def _set_properties(self) -> None:
         field_overwrites = {"id": DOCUMENTID}
 
@@ -207,13 +218,13 @@ def _create_schema(self) -> None:
         #       and configure replication
         # we will update base on user feedback
         schema["properties"] = properties
-        schema["class"] = self._db_config.index_name
+        schema["class"] = self.index_name
 
         # TODO: Use exists() instead of contains() when available
         #       see https://github.com/weaviate/weaviate-python-client/issues/232
         if self._client.schema.contains(schema):
             logging.warning(
-                f"Found index {self._db_config.index_name} with schema {schema}. Will reuse existing schema."
+                f"Found index {self.index_name} with schema {schema}. Will reuse existing schema."
             )
         else:
             self._client.schema.create_class(schema)
@@ -223,7 +234,7 @@ class DBConfig(BaseDocIndex.DBConfig):
         """Dataclass that contains all "static" configurations of WeaviateDocumentIndex."""
 
         host: str = 'http://localhost:8080'
-        index_name: str = 'Document'
+        index_name: Optional[str] = None
         username: Optional[str] = None
         password: Optional[str] = None
         scopes: List[str] = field(default_factory=lambda: ["offline_access"])
@@ -269,7 +280,7 @@ def _del_items(self, doc_ids: Sequence[str]):
         # see: https://weaviate.io/developers/weaviate/api/rest/batch#maximum-number-of-deletes-per-query
         while has_matches:
             results = self._client.batch.delete_objects(
-                class_name=self._db_config.index_name,
+                class_name=self.index_name,
                 where=where_filter,
             )
 
@@ -279,14 +290,14 @@ def _filter(self, filter_query: Any, limit: int) -> Union[DocList, List[Dict]]:
         self._overwrite_id(filter_query)
 
         results = (
-            self._client.query.get(self._db_config.index_name, self.properties)
+            self._client.query.get(self.index_name, self.properties)
             .with_additional("vector")
             .with_where(filter_query)
             .with_limit(limit)
             .do()
         )
 
-        docs = results["data"]["Get"][self._db_config.index_name]
+        docs = results["data"]["Get"][self.index_name]
 
         return [self._parse_weaviate_result(doc) for doc in docs]
 
@@ -297,7 +308,7 @@ def _filter_batched(
             self._overwrite_id(filter_query)
 
         qs = [
-            self._client.query.get(self._db_config.index_name, self.properties)
+            self._client.query.get(self.index_name, self.properties)
             .with_additional("vector")
             .with_where(filter_query)
             .with_limit(limit)
@@ -370,7 +381,7 @@ def _find(
         score_name: Literal["certainty", "distance"] = "certainty",
         score_threshold: Optional[float] = None,
     ) -> _FindResult:
-        index_name = self._db_config.index_name
+        index_name = self.index_name
         if search_field:
             logging.warning(
                 'Argument search_field is not supported for WeaviateDocumentIndex. Ignoring.'
@@ -474,7 +485,7 @@ def _find_batched(
                 near_vector[score_name] = score_threshold
 
             q = (
-                self._client.query.get(self._db_config.index_name, self.properties)
+                self._client.query.get(self.index_name, self.properties)
                 .with_near_vector(near_vector)
                 .with_limit(limit)
                 .with_additional([score_name, "vector"])
@@ -507,7 +518,7 @@ def _get_items(self, doc_ids: Sequence[str]) -> List[Dict]:
         }
 
         results = (
-            self._client.query.get(self._db_config.index_name, self.properties)
+            self._client.query.get(self.index_name, self.properties)
             .with_where(where_filter)
             .with_additional("vector")
             .do()
@@ -515,7 +526,7 @@ def _get_items(self, doc_ids: Sequence[str]) -> List[Dict]:
 
         docs = [
             self._parse_weaviate_result(doc)
-            for doc in results["data"]["Get"][self._db_config.index_name]
+            for doc in results["data"]["Get"][self.index_name]
         ]
 
         return docs
@@ -554,7 +565,7 @@ def _parse_weaviate_result(self, result: Dict) -> Dict:
 
     def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]):
         docs = self._transpose_col_value_dict(column_to_data)
-        index_name = self._db_config.index_name
+        index_name = self.index_name
 
         with self._client.batch as batch:
             for doc in docs:
@@ -577,7 +588,7 @@ def _index(self, column_to_data: Dict[str, Generator[Any, None, None]]):
     def _text_search(
         self, query: str, limit: int, search_field: str = ''
     ) -> _FindResult:
-        index_name = self._db_config.index_name
+        index_name = self.index_name
         bm25 = {"query": query, "properties": [search_field]}
 
         results = (
@@ -602,7 +613,7 @@ def _text_search_batched(
             bm25 = {"query": query, "properties": [search_field]}
 
             q = (
-                self._client.query.get(self._db_config.index_name, self.properties)
+                self._client.query.get(self.index_name, self.properties)
                 .with_bm25(bm25)
                 .with_limit(limit)
                 .with_additional(["score", "vector"])
@@ -663,7 +674,7 @@ def num_docs(self) -> int:
         """
         Get the number of documents.
         """
-        index_name = self._db_config.index_name
+        index_name = self.index_name
         result = self._client.query.aggregate(index_name).with_meta_count().do()
         # TODO: decorator to check for errors
         total_docs = result["data"]["Aggregate"][index_name][0]["meta"]["count"]
@@ -734,7 +745,7 @@ class QueryBuilder(BaseDocIndex.QueryBuilder):
         def __init__(self, document_index):
             self._queries = [
                 document_index._client.query.get(
-                    document_index._db_config.index_name, document_index.properties
+                    document_index.index_name, document_index.properties
                 )
             ]
 
diff --git a/docs/user_guide/storing/docindex.md b/docs/user_guide/storing/docindex.md
index 2240b06cc86..ad2ee2b96d2 100644
--- a/docs/user_guide/storing/docindex.md
+++ b/docs/user_guide/storing/docindex.md
@@ -161,7 +161,12 @@ db.index(data)
 For `HnswDocumentIndex` you need to specify a `work_dir` where the data will be stored; for other backends you
 usually specify a `host` and a `port` instead.
 
-Either way, if the location does not yet contain any data, we start from a blank slate.
+In addition to a host and a port, most backends can also take an `index_name`, `table_name`, `collection_name` or similar.
+This specifies the name of the index/table/collection that will be created in the database.
+You don't have to specify this though: By default, this name will be taken from the name of the Document type that you use as schema.
+For example, for `WeaviateDocumentIndex[MyDoc](...)` the data will be stored in a Weaviate Class of name `MyDoc`.
+
+In any case, if the location does not yet contain any data, we start from a blank slate.
 If the location already contains data from a previous session, it will be accessible through the Document Index.
 
 ## Index data
diff --git a/docs/user_guide/storing/index_elastic.md b/docs/user_guide/storing/index_elastic.md
index a21528c46b8..eb1186f6d12 100644
--- a/docs/user_guide/storing/index_elastic.md
+++ b/docs/user_guide/storing/index_elastic.md
@@ -420,7 +420,7 @@ The following configs can be set in `DBConfig`:
 |-------------------|----------------------------------------------------------------------------------------------------------------------------------------|-------------------------|
 | `hosts`           | Hostname of the Elasticsearch server                                                                                                   | `http://localhost:9200` |
 | `es_config`       | Other ES [configuration options](https://www.elastic.co/guide/en/elasticsearch/client/python-api/8.6/config.html) in a Dict and pass to `Elasticsearch` client constructor, e.g. `cloud_id`, `api_key` | None |
-| `index_name`      | Elasticsearch index name, the name of Elasticsearch index object                                       | None |
+| `index_name`      | Elasticsearch index name, the name of Elasticsearch index object                                       | None. Data will be stored in an index named after the Document type used as schema. |
 | `index_settings`  | Other [index settings](https://www.elastic.co/guide/en/elasticsearch/reference/8.6/index-modules.html#index-modules-settings) in a Dict for creating the index    | dict  |
 | `index_mappings`  | Other [index mappings](https://www.elastic.co/guide/en/elasticsearch/reference/8.6/mapping.html) in a Dict for creating the index | dict  |
 
diff --git a/docs/user_guide/storing/index_qdrant.md b/docs/user_guide/storing/index_qdrant.md
index 7d832f1dd67..01249d01b7a 100644
--- a/docs/user_guide/storing/index_qdrant.md
+++ b/docs/user_guide/storing/index_qdrant.md
@@ -27,6 +27,10 @@ For general usage of a Document Index, see the [general user guide](./docindex.m
     runtime_config = QdrantDocumentIndex.RuntimeConfig()
     print(runtime_config)  # shows default values
     ```
+    
+    Note that the collection_name from the DBConfig is an Optional[str] with None as default value. This is because
+    the QdrantDocumentIndex will take the name the Document type that you use as schema. For example, for QdrantDocumentIndex[MyDoc](...) 
+    the data will be stored in a collection name MyDoc if no specific collection_name is passed in the DBConfig.
 
 ```python
 import numpy as np
diff --git a/docs/user_guide/storing/index_weaviate.md b/docs/user_guide/storing/index_weaviate.md
index 83d84dd8fa9..09aaccc69b7 100644
--- a/docs/user_guide/storing/index_weaviate.md
+++ b/docs/user_guide/storing/index_weaviate.md
@@ -203,18 +203,18 @@ And a list of environment variables is [available on this page](https://weaviate
 
 Additionally, you can specify the below settings when you instantiate a configuration object in DocArray.
 
-| name | type | explanation | default | example |
-| ---- | ---- | ----------- | ------- | ------- |
+| name | type | explanation | default                                                                | example |
+| ---- | ---- | ----------- |------------------------------------------------------------------------| ------- |
 | **Category: General** |
-| host | str | Weaviate instance url | http://localhost:8080 |
+| host | str | Weaviate instance url | http://localhost:8080                                                  |
 | **Category: Authentication** |
-| username | str | Username known to the specified authentication provider (e.g. WCS) | None | `jp@weaviate.io` |
-| password | str | Corresponding password | None | `p@ssw0rd` |
-| auth_api_key | str | API key known to the Weaviate instance | None | `mys3cretk3y` | 
+| username | str | Username known to the specified authentication provider (e.g. WCS) | None                                                                   | `jp@weaviate.io` |
+| password | str | Corresponding password | None                                                                   | `p@ssw0rd` |
+| auth_api_key | str | API key known to the Weaviate instance | None                                                                   | `mys3cretk3y` | 
 | **Category: Data schema** |
-| index_name | str | Class name to use to store the document | `Document` |
+| index_name | str | Class name to use to store the document| The document class name, e.g. `MyDoc` for `WeaviateDocumentIndex[MyDoc]` | `Document` |
 | **Category: Embedded Weaviate** |
-| embedded_options| EmbeddedOptions | Options for embedded weaviate | None |
+| embedded_options| EmbeddedOptions | Options for embedded weaviate | None                                                                   |
 
 The type `EmbeddedOptions` can be specified as described [here](https://weaviate.io/developers/weaviate/installation/embedded#embedded-options)
 
diff --git a/tests/index/elastic/fixture.py b/tests/index/elastic/fixture.py
index 812f0f09d51..ef7766acd0c 100644
--- a/tests/index/elastic/fixture.py
+++ b/tests/index/elastic/fixture.py
@@ -1,5 +1,6 @@
 import os
 import time
+import uuid
 
 import numpy as np
 import pytest
@@ -87,3 +88,8 @@ def ten_deep_nested_docs():
         DeepNestedDoc(d=NestedDoc(d=SimpleDoc(tens=np.random.randn(10))))
         for _ in range(10)
     ]
+
+
+@pytest.fixture(scope='function')
+def tmp_index_name():
+    return uuid.uuid4().hex
diff --git a/tests/index/elastic/v7/test_column_config.py b/tests/index/elastic/v7/test_column_config.py
index f1fa93d7748..812734aef26 100644
--- a/tests/index/elastic/v7/test_column_config.py
+++ b/tests/index/elastic/v7/test_column_config.py
@@ -129,3 +129,17 @@ class MyDoc(BaseDoc):
     }
     docs, _ = index.execute_query(query)
     assert [doc['id'] for doc in docs] == [doc[0].id, doc[1].id]
+
+
+def test_index_name():
+    class TextDoc(BaseDoc):
+        text: str = Field()
+
+    class StringDoc(BaseDoc):
+        text: str = Field(col_type='text')
+
+    index = ElasticV7DocIndex[TextDoc]()
+    assert index.index_name == TextDoc.__name__.lower()
+
+    index = ElasticV7DocIndex[StringDoc]()
+    assert index.index_name == StringDoc.__name__.lower()
diff --git a/tests/index/elastic/v7/test_find.py b/tests/index/elastic/v7/test_find.py
index d54b3b0480d..1fe7893e91e 100644
--- a/tests/index/elastic/v7/test_find.py
+++ b/tests/index/elastic/v7/test_find.py
@@ -6,8 +6,12 @@
 from docarray import BaseDoc
 from docarray.index import ElasticV7DocIndex
 from docarray.typing import NdArray, TorchTensor
-from tests.index.elastic.fixture import start_storage_v7  # noqa: F401
-from tests.index.elastic.fixture import FlatDoc, SimpleDoc
+from tests.index.elastic.fixture import (  # noqa: F401
+    FlatDoc,
+    SimpleDoc,
+    start_storage_v7,
+    tmp_index_name,
+)
 
 pytestmark = [pytest.mark.slow, pytest.mark.index]
 
@@ -167,8 +171,8 @@ class TfDoc(BaseDoc):
     )
 
 
-def test_find_batched():
-    index = ElasticV7DocIndex[SimpleDoc]()
+def test_find_batched(tmp_index_name):  # noqa: F811
+    index = ElasticV7DocIndex[SimpleDoc](index_name=tmp_index_name)
 
     index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)]
     index.index(index_docs)
@@ -243,13 +247,13 @@ class MyDoc(BaseDoc):
             assert doc.text.index(query) >= 0
 
 
-def test_query_builder():
+def test_query_builder(tmp_index_name):  # noqa: F811
     class MyDoc(BaseDoc):
         tens: NdArray[10]
         num: int
         text: str
 
-    index = ElasticV7DocIndex[MyDoc]()
+    index = ElasticV7DocIndex[MyDoc](index_name=tmp_index_name)
     index_docs = [
         MyDoc(
             id=f'{i}', tens=np.random.rand(10), num=int(i / 2), text=f'text {int(i/2)}'
diff --git a/tests/index/elastic/v7/test_index_get_del.py b/tests/index/elastic/v7/test_index_get_del.py
index e6e6baf3e60..050bcb03f54 100644
--- a/tests/index/elastic/v7/test_index_get_del.py
+++ b/tests/index/elastic/v7/test_index_get_del.py
@@ -18,14 +18,17 @@
     ten_flat_docs,
     ten_nested_docs,
     ten_simple_docs,
+    tmp_index_name,
 )
 
 pytestmark = [pytest.mark.slow, pytest.mark.index]
 
 
 @pytest.mark.parametrize('use_docarray', [True, False])
-def test_index_simple_schema(ten_simple_docs, use_docarray):  # noqa: F811
-    index = ElasticV7DocIndex[SimpleDoc]()
+def test_index_simple_schema(
+    ten_simple_docs, use_docarray, tmp_index_name  # noqa: F811
+):
+    index = ElasticV7DocIndex[SimpleDoc](index_name=tmp_index_name)
     if use_docarray:
         ten_simple_docs = DocList[SimpleDoc](ten_simple_docs)
 
@@ -34,8 +37,8 @@ def test_index_simple_schema(ten_simple_docs, use_docarray):  # noqa: F811
 
 
 @pytest.mark.parametrize('use_docarray', [True, False])
-def test_index_flat_schema(ten_flat_docs, use_docarray):  # noqa: F811
-    index = ElasticV7DocIndex[FlatDoc]()
+def test_index_flat_schema(ten_flat_docs, use_docarray, tmp_index_name):  # noqa: F811
+    index = ElasticV7DocIndex[FlatDoc](index_name=tmp_index_name)
     if use_docarray:
         ten_flat_docs = DocList[FlatDoc](ten_flat_docs)
 
@@ -44,8 +47,10 @@ def test_index_flat_schema(ten_flat_docs, use_docarray):  # noqa: F811
 
 
 @pytest.mark.parametrize('use_docarray', [True, False])
-def test_index_nested_schema(ten_nested_docs, use_docarray):  # noqa: F811
-    index = ElasticV7DocIndex[NestedDoc]()
+def test_index_nested_schema(
+    ten_nested_docs, use_docarray, tmp_index_name  # noqa: F811
+):
+    index = ElasticV7DocIndex[NestedDoc](index_name=tmp_index_name)
     if use_docarray:
         ten_nested_docs = DocList[NestedDoc](ten_nested_docs)
 
@@ -54,8 +59,10 @@ def test_index_nested_schema(ten_nested_docs, use_docarray):  # noqa: F811
 
 
 @pytest.mark.parametrize('use_docarray', [True, False])
-def test_index_deep_nested_schema(ten_deep_nested_docs, use_docarray):  # noqa: F811
-    index = ElasticV7DocIndex[DeepNestedDoc]()
+def test_index_deep_nested_schema(
+    ten_deep_nested_docs, use_docarray, tmp_index_name  # noqa: F811
+):
+    index = ElasticV7DocIndex[DeepNestedDoc](index_name=tmp_index_name)
     if use_docarray:
         ten_deep_nested_docs = DocList[DeepNestedDoc](ten_deep_nested_docs)
 
@@ -73,6 +80,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs):  # noqa: F
         id_ = d.id
         assert index[id_].id == id_
         assert np.all(index[id_].tens == d.tens)
+    index._client.indices.delete(index='simpledoc')
 
     # flat
     index = ElasticV7DocIndex[FlatDoc]()
@@ -84,6 +92,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs):  # noqa: F
         assert index[id_].id == id_
         assert np.all(index[id_].tens_one == d.tens_one)
         assert np.all(index[id_].tens_two == d.tens_two)
+    index._client.indices.delete(index='flatdoc')
 
     # nested
     index = ElasticV7DocIndex[NestedDoc]()
@@ -95,6 +104,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs):  # noqa: F
         assert index[id_].id == id_
         assert index[id_].d.id == d.d.id
         assert np.all(index[id_].d.tens == d.d.tens)
+    index._client.indices.delete(index='nesteddoc')
 
 
 def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs):  # noqa: F811
@@ -147,16 +157,16 @@ def test_get_key_error(ten_simple_docs):  # noqa: F811
         index['not_a_real_id']
 
 
-def test_persisting(ten_simple_docs):  # noqa: F811
-    index = ElasticV7DocIndex[SimpleDoc](index_name='test_persisting')
+def test_persisting(ten_simple_docs, tmp_index_name):  # noqa: F811
+    index = ElasticV7DocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
 
-    index2 = ElasticV7DocIndex[SimpleDoc](index_name='test_persisting')
+    index2 = ElasticV7DocIndex[SimpleDoc](index_name=tmp_index_name)
     assert index2.num_docs() == 10
 
 
-def test_del_single(ten_simple_docs):  # noqa: F811
-    index = ElasticV7DocIndex[SimpleDoc]()
+def test_del_single(ten_simple_docs, tmp_index_name):  # noqa: F811
+    index = ElasticV7DocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
     # delete once
     assert index.num_docs() == 10
@@ -183,10 +193,10 @@ def test_del_single(ten_simple_docs):  # noqa: F811
             assert np.all(index[id_].tens == d.tens)
 
 
-def test_del_multiple(ten_simple_docs):  # noqa: F811
+def test_del_multiple(ten_simple_docs, tmp_index_name):  # noqa: F811
     docs_to_del_idx = [0, 2, 4, 6, 8]
 
-    index = ElasticV7DocIndex[SimpleDoc]()
+    index = ElasticV7DocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
 
     assert index.num_docs() == 10
@@ -210,8 +220,8 @@ def test_del_key_error(ten_simple_docs):  # noqa: F811
         del index['not_a_real_id']
 
 
-def test_num_docs(ten_simple_docs):  # noqa: F811
-    index = ElasticV7DocIndex[SimpleDoc]()
+def test_num_docs(ten_simple_docs, tmp_index_name):  # noqa: F811
+    index = ElasticV7DocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
 
     assert index.num_docs() == 10
diff --git a/tests/index/elastic/v8/test_column_config.py b/tests/index/elastic/v8/test_column_config.py
index 0edd105697d..c2831dfb59e 100644
--- a/tests/index/elastic/v8/test_column_config.py
+++ b/tests/index/elastic/v8/test_column_config.py
@@ -3,17 +3,17 @@
 
 from docarray import BaseDoc
 from docarray.index import ElasticDocIndex
-from tests.index.elastic.fixture import start_storage_v8  # noqa: F401
+from tests.index.elastic.fixture import start_storage_v8, tmp_index_name  # noqa: F401
 
 pytestmark = [pytest.mark.slow, pytest.mark.index, pytest.mark.elasticv8]
 
 
-def test_column_config():
+def test_column_config(tmp_index_name):  # noqa: F811
     class MyDoc(BaseDoc):
         text: str
         color: str = Field(col_type='keyword')
 
-    index = ElasticDocIndex[MyDoc]()
+    index = ElasticDocIndex[MyDoc](index_name=tmp_index_name)
     index_docs = [
         MyDoc(id='0', text='hello world', color='red'),
         MyDoc(id='1', text='never gonna give you up', color='blue'),
@@ -30,7 +30,7 @@ class MyDoc(BaseDoc):
     assert [doc.id for doc in docs] == ['0', '1']
 
 
-def test_field_object():
+def test_field_object(tmp_index_name):  # noqa: F811
     class MyDoc(BaseDoc):
         manager: dict = Field(
             properties={
@@ -44,7 +44,7 @@ class MyDoc(BaseDoc):
             }
         )
 
-    index = ElasticDocIndex[MyDoc]()
+    index = ElasticDocIndex[MyDoc](index_name=tmp_index_name)
     doc = [
         MyDoc(manager={'age': 25, 'name': {'first': 'Rachel', 'last': 'Green'}}),
         MyDoc(manager={'age': 30, 'name': {'first': 'Monica', 'last': 'Geller'}}),
@@ -60,11 +60,11 @@ class MyDoc(BaseDoc):
     assert [doc.id for doc in docs] == [doc[1].id, doc[2].id]
 
 
-def test_field_geo_point():
+def test_field_geo_point(tmp_index_name):  # noqa: F811
     class MyDoc(BaseDoc):
         location: dict = Field(col_type='geo_point')
 
-    index = ElasticDocIndex[MyDoc]()
+    index = ElasticDocIndex[MyDoc](index_name=tmp_index_name)
     doc = [
         MyDoc(location={'lat': 40.12, 'lon': -72.34}),
         MyDoc(location={'lat': 41.12, 'lon': -73.34}),
@@ -87,12 +87,12 @@ class MyDoc(BaseDoc):
     assert [doc['id'] for doc in docs] == [doc[0].id, doc[1].id]
 
 
-def test_field_range():
+def test_field_range(tmp_index_name):  # noqa: F811
     class MyDoc(BaseDoc):
         expected_attendees: dict = Field(col_type='integer_range')
         time_frame: dict = Field(col_type='date_range', format='yyyy-MM-dd')
 
-    index = ElasticDocIndex[MyDoc]()
+    index = ElasticDocIndex[MyDoc](index_name=tmp_index_name)
     doc = [
         MyDoc(
             expected_attendees={'gte': 10, 'lt': 20},
@@ -129,3 +129,17 @@ class MyDoc(BaseDoc):
     }
     docs, _ = index.execute_query(query)
     assert [doc['id'] for doc in docs] == [doc[0].id, doc[1].id]
+
+
+def test_index_name():
+    class TextDoc(BaseDoc):
+        text: str = Field()
+
+    class StringDoc(BaseDoc):
+        text: str = Field(col_type='text')
+
+    index = ElasticDocIndex[TextDoc]()
+    assert index.index_name == TextDoc.__name__.lower()
+
+    index = ElasticDocIndex[StringDoc]()
+    assert index.index_name == StringDoc.__name__.lower()
diff --git a/tests/index/elastic/v8/test_find.py b/tests/index/elastic/v8/test_find.py
index bb87755254c..cfd27ed0912 100644
--- a/tests/index/elastic/v8/test_find.py
+++ b/tests/index/elastic/v8/test_find.py
@@ -6,18 +6,22 @@
 from docarray import BaseDoc
 from docarray.index import ElasticDocIndex
 from docarray.typing import NdArray, TorchTensor
-from tests.index.elastic.fixture import start_storage_v8  # noqa: F401
-from tests.index.elastic.fixture import FlatDoc, SimpleDoc
+from tests.index.elastic.fixture import (  # noqa: F401
+    FlatDoc,
+    SimpleDoc,
+    start_storage_v8,
+    tmp_index_name,
+)
 
 pytestmark = [pytest.mark.slow, pytest.mark.index, pytest.mark.elasticv8]
 
 
 @pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product'])
-def test_find_simple_schema(similarity):
+def test_find_simple_schema(similarity, tmp_index_name):  # noqa: F811
     class SimpleSchema(BaseDoc):
         tens: NdArray[10] = Field(similarity=similarity)
 
-    index = ElasticDocIndex[SimpleSchema]()
+    index = ElasticDocIndex[SimpleSchema](index_name=tmp_index_name)
 
     index_docs = []
     for _ in range(10):
@@ -37,12 +41,12 @@ class SimpleSchema(BaseDoc):
 
 
 @pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product'])
-def test_find_flat_schema(similarity):
+def test_find_flat_schema(similarity, tmp_index_name):  # noqa: F811
     class FlatSchema(BaseDoc):
         tens_one: NdArray = Field(dims=10, similarity=similarity)
         tens_two: NdArray = Field(dims=50, similarity=similarity)
 
-    index = ElasticDocIndex[FlatSchema]()
+    index = ElasticDocIndex[FlatSchema](index_name=tmp_index_name)
 
     index_docs = []
     for _ in range(10):
@@ -75,7 +79,7 @@ class FlatSchema(BaseDoc):
 
 
 @pytest.mark.parametrize('similarity', ['cosine', 'l2_norm', 'dot_product'])
-def test_find_nested_schema(similarity):
+def test_find_nested_schema(similarity, tmp_index_name):  # noqa: F811
     class SimpleDoc(BaseDoc):
         tens: NdArray[10] = Field(similarity=similarity)
 
@@ -87,7 +91,7 @@ class DeepNestedDoc(BaseDoc):
         d: NestedDoc
         tens: NdArray = Field(similarity=similarity, dims=10)
 
-    index = ElasticDocIndex[DeepNestedDoc]()
+    index = ElasticDocIndex[DeepNestedDoc](index_name=tmp_index_name)
 
     index_docs = []
     for _ in range(10):
@@ -188,8 +192,8 @@ class TfDoc(BaseDoc):
     )
 
 
-def test_find_batched():
-    index = ElasticDocIndex[SimpleDoc]()
+def test_find_batched(tmp_index_name):  # noqa: F811
+    index = ElasticDocIndex[SimpleDoc](index_name=tmp_index_name)
 
     index_docs = [SimpleDoc(tens=np.random.rand(10)) for _ in range(10)]
     index.index(index_docs)
@@ -272,7 +276,9 @@ class MyDoc(BaseDoc):
 
     index = ElasticDocIndex[MyDoc]()
     index_docs = [
-        MyDoc(id=f'{i}', tens=np.ones(10) * i, num=int(i / 2), text=f'text {int(i/2)}')
+        MyDoc(
+            id=f'{i}', tens=np.ones(10) * i, num=int(i / 2), text=f'text {int(i / 2)}'
+        )
         for i in range(10)
     ]
     index.index(index_docs)
@@ -327,3 +333,12 @@ class MyDoc(BaseDoc):
 
     docs, _ = index.execute_query(query)
     assert [doc['id'] for doc in docs] == ['7', '6', '5', '4']
+
+
+def test_index_name():
+    class MyDoc(BaseDoc):
+        expected_attendees: dict = Field(col_type='integer_range')
+        time_frame: dict = Field(col_type='date_range', format='yyyy-MM-dd')
+
+    index = ElasticDocIndex[MyDoc]()
+    assert index.index_name == MyDoc.__name__.lower()
diff --git a/tests/index/elastic/v8/test_index_get_del.py b/tests/index/elastic/v8/test_index_get_del.py
index 8efd66429b0..8d182dfd19a 100644
--- a/tests/index/elastic/v8/test_index_get_del.py
+++ b/tests/index/elastic/v8/test_index_get_del.py
@@ -18,14 +18,17 @@
     ten_flat_docs,
     ten_nested_docs,
     ten_simple_docs,
+    tmp_index_name,
 )
 
 pytestmark = [pytest.mark.slow, pytest.mark.index, pytest.mark.elasticv8]
 
 
 @pytest.mark.parametrize('use_docarray', [True, False])
-def test_index_simple_schema(ten_simple_docs, use_docarray):  # noqa: F811
-    index = ElasticDocIndex[SimpleDoc]()
+def test_index_simple_schema(
+    ten_simple_docs, use_docarray, tmp_index_name  # noqa: F811
+):
+    index = ElasticDocIndex[SimpleDoc](index_name=tmp_index_name)
     if use_docarray:
         ten_simple_docs = DocList[SimpleDoc](ten_simple_docs)
 
@@ -34,8 +37,8 @@ def test_index_simple_schema(ten_simple_docs, use_docarray):  # noqa: F811
 
 
 @pytest.mark.parametrize('use_docarray', [True, False])
-def test_index_flat_schema(ten_flat_docs, use_docarray):  # noqa: F811
-    index = ElasticDocIndex[FlatDoc]()
+def test_index_flat_schema(ten_flat_docs, use_docarray, tmp_index_name):  # noqa: F811
+    index = ElasticDocIndex[FlatDoc](index_name=tmp_index_name)
     if use_docarray:
         ten_flat_docs = DocList[FlatDoc](ten_flat_docs)
 
@@ -44,8 +47,10 @@ def test_index_flat_schema(ten_flat_docs, use_docarray):  # noqa: F811
 
 
 @pytest.mark.parametrize('use_docarray', [True, False])
-def test_index_nested_schema(ten_nested_docs, use_docarray):  # noqa: F811
-    index = ElasticDocIndex[NestedDoc]()
+def test_index_nested_schema(
+    ten_nested_docs, use_docarray, tmp_index_name  # noqa: F811
+):
+    index = ElasticDocIndex[NestedDoc](index_name=tmp_index_name)
     if use_docarray:
         ten_nested_docs = DocList[NestedDoc](ten_nested_docs)
 
@@ -54,8 +59,10 @@ def test_index_nested_schema(ten_nested_docs, use_docarray):  # noqa: F811
 
 
 @pytest.mark.parametrize('use_docarray', [True, False])
-def test_index_deep_nested_schema(ten_deep_nested_docs, use_docarray):  # noqa: F811
-    index = ElasticDocIndex[DeepNestedDoc]()
+def test_index_deep_nested_schema(
+    ten_deep_nested_docs, use_docarray, tmp_index_name  # noqa: F811
+):
+    index = ElasticDocIndex[DeepNestedDoc](index_name=tmp_index_name)
     if use_docarray:
         ten_deep_nested_docs = DocList[DeepNestedDoc](ten_deep_nested_docs)
 
@@ -73,6 +80,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs):  # noqa: F
         id_ = d.id
         assert index[id_].id == id_
         assert np.all(index[id_].tens == d.tens)
+    index._client.indices.delete(index='simpledoc', ignore_unavailable=True)
 
     # flat
     index = ElasticDocIndex[FlatDoc]()
@@ -84,6 +92,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs):  # noqa: F
         assert index[id_].id == id_
         assert np.all(index[id_].tens_one == d.tens_one)
         assert np.all(index[id_].tens_two == d.tens_two)
+    index._client.indices.delete(index='flatdoc', ignore_unavailable=True)
 
     # nested
     index = ElasticDocIndex[NestedDoc]()
@@ -95,6 +104,7 @@ def test_get_single(ten_simple_docs, ten_flat_docs, ten_nested_docs):  # noqa: F
         assert index[id_].id == id_
         assert index[id_].d.id == d.d.id
         assert np.all(index[id_].d.tens == d.d.tens)
+    index._client.indices.delete(index='nesteddoc', ignore_unavailable=True)
 
 
 def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs):  # noqa: F811
@@ -139,24 +149,24 @@ def test_get_multiple(ten_simple_docs, ten_flat_docs, ten_nested_docs):  # noqa:
         assert np.all(d_out.d.tens == d_in.d.tens)
 
 
-def test_get_key_error(ten_simple_docs):  # noqa: F811
-    index = ElasticDocIndex[SimpleDoc]()
+def test_get_key_error(ten_simple_docs, tmp_index_name):  # noqa: F811
+    index = ElasticDocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
 
     with pytest.raises(KeyError):
         index['not_a_real_id']
 
 
-def test_persisting(ten_simple_docs):  # noqa: F811
-    index = ElasticDocIndex[SimpleDoc](index_name='test_persisting')
+def test_persisting(ten_simple_docs, tmp_index_name):  # noqa: F811
+    index = ElasticDocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
 
-    index2 = ElasticDocIndex[SimpleDoc](index_name='test_persisting')
+    index2 = ElasticDocIndex[SimpleDoc](index_name=tmp_index_name)
     assert index2.num_docs() == 10
 
 
-def test_del_single(ten_simple_docs):  # noqa: F811
-    index = ElasticDocIndex[SimpleDoc]()
+def test_del_single(ten_simple_docs, tmp_index_name):  # noqa: F811
+    index = ElasticDocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
     # delete once
     assert index.num_docs() == 10
@@ -183,10 +193,10 @@ def test_del_single(ten_simple_docs):  # noqa: F811
             assert np.all(index[id_].tens == d.tens)
 
 
-def test_del_multiple(ten_simple_docs):  # noqa: F811
+def test_del_multiple(ten_simple_docs, tmp_index_name):  # noqa: F811
     docs_to_del_idx = [0, 2, 4, 6, 8]
 
-    index = ElasticDocIndex[SimpleDoc]()
+    index = ElasticDocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
 
     assert index.num_docs() == 10
@@ -202,16 +212,16 @@ def test_del_multiple(ten_simple_docs):  # noqa: F811
             assert np.all(index[doc.id].tens == doc.tens)
 
 
-def test_del_key_error(ten_simple_docs):  # noqa: F811
-    index = ElasticDocIndex[SimpleDoc]()
+def test_del_key_error(ten_simple_docs, tmp_index_name):  # noqa: F811
+    index = ElasticDocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
 
     with pytest.warns(UserWarning):
         del index['not_a_real_id']
 
 
-def test_num_docs(ten_simple_docs):  # noqa: F811
-    index = ElasticDocIndex[SimpleDoc]()
+def test_num_docs(ten_simple_docs, tmp_index_name):  # noqa: F811
+    index = ElasticDocIndex[SimpleDoc](index_name=tmp_index_name)
     index.index(ten_simple_docs)
 
     assert index.num_docs() == 10
diff --git a/tests/index/qdrant/docker-compose.yml b/tests/index/qdrant/docker-compose.yml
new file mode 100644
index 00000000000..a3a57b7c87f
--- /dev/null
+++ b/tests/index/qdrant/docker-compose.yml
@@ -0,0 +1,12 @@
+version: '3.8'
+
+services:
+  qdrant:
+    image: qdrant/qdrant:v1.1.2
+    ports:
+      - "6333:6333"
+      - "6334:6334"
+    ulimits: # Only required for tests, as there are a lot of collections created
+      nofile:
+        soft: 65535
+        hard: 65535
\ No newline at end of file
diff --git a/tests/index/qdrant/fixtures.py b/tests/index/qdrant/fixtures.py
index d44a0950d35..d48372ad0f0 100644
--- a/tests/index/qdrant/fixtures.py
+++ b/tests/index/qdrant/fixtures.py
@@ -1,8 +1,29 @@
+import os
+import time
+import uuid
+
 import pytest
 import qdrant_client
 
 from docarray.index import QdrantDocumentIndex
 
+cur_dir = os.path.dirname(os.path.abspath(__file__))
+qdrant_yml = os.path.abspath(os.path.join(cur_dir, 'docker-compose.yml'))
+
+
+@pytest.fixture(scope='session', autouse=True)
+def start_storage():
+    os.system(f"docker-compose -f {qdrant_yml} up -d --remove-orphans")
+    time.sleep(1)
+
+    yield
+    os.system(f"docker-compose -f {qdrant_yml} down --remove-orphans")
+
+
+@pytest.fixture(scope='function')
+def tmp_collection_name():
+    return uuid.uuid4().hex
+
 
 @pytest.fixture
 def qdrant() -> qdrant_client.QdrantClient:
diff --git a/tests/index/qdrant/test_find.py b/tests/index/qdrant/test_find.py
index 610695e5c81..4dd2c27ba25 100644
--- a/tests/index/qdrant/test_find.py
+++ b/tests/index/qdrant/test_find.py
@@ -5,7 +5,7 @@
 from docarray import BaseDoc, DocList
 from docarray.index import QdrantDocumentIndex
 from docarray.typing import NdArray, TorchTensor
-from tests.index.qdrant.fixtures import qdrant, qdrant_config  # noqa: F401
+from tests.index.qdrant.fixtures import start_storage, tmp_collection_name  # noqa: F401
 
 pytestmark = [pytest.mark.slow, pytest.mark.index]
 
@@ -32,11 +32,11 @@ class TorchDoc(BaseDoc):
 
 
 @pytest.mark.parametrize('space', ['cosine', 'l2', 'ip'])
-def test_find_simple_schema(qdrant_config, space):  # noqa: F811
+def test_find_simple_schema(space):
     class SimpleSchema(BaseDoc):
         tens: NdArray[10] = Field(space=space)  # type: ignore[valid-type]
 
-    index = QdrantDocumentIndex[SimpleSchema](db_config=qdrant_config)
+    index = QdrantDocumentIndex[SimpleSchema](host='localhost')
 
     index_docs = [SimpleDoc(tens=np.zeros(10)) for _ in range(10)]
     index_docs.append(SimpleDoc(tens=np.ones(10)))
@@ -50,9 +50,8 @@ class SimpleSchema(BaseDoc):
     assert len(scores) == 5
 
 
-@pytest.mark.parametrize('space', ['cosine', 'l2', 'ip'])
-def test_find_torch(qdrant_config, space):  # noqa: F811
-    index = QdrantDocumentIndex[TorchDoc](db_config=qdrant_config)
+def test_find_torch():
+    index = QdrantDocumentIndex[TorchDoc](host='localhost')
 
     index_docs = [TorchDoc(tens=np.zeros(10)) for _ in range(10)]
     index_docs.append(TorchDoc(tens=np.ones(10)))
@@ -72,14 +71,13 @@ def test_find_torch(qdrant_config, space):  # noqa: F811
 
 
 @pytest.mark.tensorflow
-@pytest.mark.parametrize('space', ['cosine', 'l2', 'ip'])
-def test_find_tensorflow(qdrant_config, space):  # noqa: F811
+def test_find_tensorflow():
     from docarray.typing import TensorFlowTensor
 
     class TfDoc(BaseDoc):
         tens: TensorFlowTensor[10]  # type: ignore[valid-type]
 
-    index = QdrantDocumentIndex[TfDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[TfDoc](host='localhost')
 
     index_docs = [
         TfDoc(tens=np.random.rand(10).astype(dtype=np.float32)) for _ in range(10)
@@ -101,12 +99,12 @@ class TfDoc(BaseDoc):
 
 
 @pytest.mark.parametrize('space', ['cosine', 'l2', 'ip'])
-def test_find_flat_schema(qdrant_config, space):  # noqa: F811
+def test_find_flat_schema(space):
     class FlatSchema(BaseDoc):
         tens_one: NdArray = Field(dim=10, space=space)
         tens_two: NdArray = Field(dim=50, space=space)
 
-    index = QdrantDocumentIndex[FlatSchema](db_config=qdrant_config)
+    index = QdrantDocumentIndex[FlatSchema](host='localhost')
 
     index_docs = [
         FlatDoc(tens_one=np.zeros(10), tens_two=np.zeros(50)) for _ in range(10)
@@ -129,7 +127,7 @@ class FlatSchema(BaseDoc):
 
 
 @pytest.mark.parametrize('space', ['cosine', 'l2', 'ip'])
-def test_find_nested_schema(qdrant_config, space):  # noqa: F811
+def test_find_nested_schema(space):
     class SimpleDoc(BaseDoc):
         tens: NdArray[10] = Field(space=space)  # type: ignore[valid-type]
 
@@ -141,7 +139,7 @@ class DeepNestedDoc(BaseDoc):
         d: NestedDoc
         tens: NdArray = Field(space=space, dim=10)
 
-    index = QdrantDocumentIndex[DeepNestedDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[DeepNestedDoc](host='localhost')
 
     index_docs = [
         DeepNestedDoc(
@@ -191,11 +189,13 @@ class DeepNestedDoc(BaseDoc):
 
 
 @pytest.mark.parametrize('space', ['cosine', 'l2', 'ip'])
-def test_find_batched(qdrant_config, space):  # noqa: F811
+def test_find_batched(space, tmp_collection_name):  # noqa: F811
     class SimpleSchema(BaseDoc):
         tens: NdArray[10] = Field(space=space)  # type: ignore[valid-type]
 
-    index = QdrantDocumentIndex[SimpleSchema](db_config=qdrant_config)
+    index = QdrantDocumentIndex[SimpleSchema](
+        host='localhost', collection_name=tmp_collection_name
+    )
 
     index_docs = [SimpleDoc(tens=vector) for vector in np.identity(10)]
     index.index(index_docs)
diff --git a/tests/index/qdrant/test_index_get_del.py b/tests/index/qdrant/test_index_get_del.py
index a1db816e58c..27cd3ee171b 100644
--- a/tests/index/qdrant/test_index_get_del.py
+++ b/tests/index/qdrant/test_index_get_del.py
@@ -10,7 +10,7 @@
 from docarray.documents import ImageDoc, TextDoc
 from docarray.index import QdrantDocumentIndex
 from docarray.typing import NdArray, NdArrayEmbedding, TorchTensor
-from tests.index.qdrant.fixtures import qdrant, qdrant_config  # noqa: F401
+from tests.index.qdrant.fixtures import start_storage, tmp_collection_name  # noqa: F401
 
 pytestmark = [pytest.mark.slow, pytest.mark.index]
 
@@ -56,9 +56,11 @@ def ten_nested_docs():
 
 @pytest.mark.parametrize('use_docarray', [True, False])
 def test_index_simple_schema(
-    ten_simple_docs, qdrant_config, use_docarray  # noqa: F811
+    ten_simple_docs, use_docarray, tmp_collection_name  # noqa: F811
 ):
-    index = QdrantDocumentIndex[SimpleDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[SimpleDoc](
+        host='localhost', collection_name=tmp_collection_name
+    )
     if use_docarray:
         ten_simple_docs = DocList[SimpleDoc](ten_simple_docs)
 
@@ -67,8 +69,12 @@ def test_index_simple_schema(
 
 
 @pytest.mark.parametrize('use_docarray', [True, False])
-def test_index_flat_schema(ten_flat_docs, qdrant_config, use_docarray):  # noqa: F811
-    index = QdrantDocumentIndex[FlatDoc](db_config=qdrant_config)
+def test_index_flat_schema(
+    ten_flat_docs, use_docarray, tmp_collection_name  # noqa: F811
+):
+    index = QdrantDocumentIndex[FlatDoc](
+        host='localhost', collection_name=tmp_collection_name
+    )
     if use_docarray:
         ten_flat_docs = DocList[FlatDoc](ten_flat_docs)
 
@@ -78,9 +84,11 @@ def test_index_flat_schema(ten_flat_docs, qdrant_config, use_docarray):  # noqa:
 
 @pytest.mark.parametrize('use_docarray', [True, False])
 def test_index_nested_schema(
-    ten_nested_docs, qdrant_config, use_docarray  # noqa: F811
+    ten_nested_docs, use_docarray, tmp_collection_name  # noqa: F811
 ):
-    index = QdrantDocumentIndex[NestedDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[NestedDoc](
+        host='localhost', collection_name=tmp_collection_name
+    )
     if use_docarray:
         ten_nested_docs = DocList[NestedDoc](ten_nested_docs)
 
@@ -88,24 +96,26 @@ def test_index_nested_schema(
     assert index.num_docs() == 10
 
 
-def test_index_torch(qdrant_config):  # noqa: F811
+def test_index_torch(tmp_collection_name):  # noqa: F811
     docs = [TorchDoc(tens=np.random.randn(10)) for _ in range(10)]
     assert isinstance(docs[0].tens, torch.Tensor)
     assert isinstance(docs[0].tens, TorchTensor)
 
-    index = QdrantDocumentIndex[TorchDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[TorchDoc](
+        host='localhost', collection_name=tmp_collection_name
+    )
 
     index.index(docs)
     assert index.num_docs() == 10
 
 
 @pytest.mark.skip('Qdrant does not support storing image tensors yet')
-def test_index_builtin_docs(qdrant_config):  # noqa: F811
+def test_index_builtin_docs():
     # TextDoc
     class TextSchema(TextDoc):
         embedding: Optional[NdArrayEmbedding] = Field(dim=10)
 
-    index = QdrantDocumentIndex[TextSchema](db_config=qdrant_config)
+    index = QdrantDocumentIndex[TextSchema](host='localhost')
 
     index.index(
         DocList[TextDoc](
@@ -133,16 +143,20 @@ class ImageSchema(ImageDoc):
     assert index.num_docs() == 10
 
 
-def test_get_key_error(ten_simple_docs, qdrant_config):  # noqa: F811
-    index = QdrantDocumentIndex[SimpleDoc](db_config=qdrant_config)
+def test_get_key_error(ten_simple_docs, tmp_collection_name):  # noqa: F811
+    index = QdrantDocumentIndex[SimpleDoc](
+        host='localhost', collection_name=tmp_collection_name
+    )
     index.index(ten_simple_docs)
 
     with pytest.raises(KeyError):
         index['not_a_real_id']
 
 
-def test_del_single(ten_simple_docs, qdrant_config):  # noqa: F811
-    index = QdrantDocumentIndex[SimpleDoc](db_config=qdrant_config)
+def test_del_single(ten_simple_docs, tmp_collection_name):  # noqa: F811
+    index = QdrantDocumentIndex[SimpleDoc](
+        host='localhost', collection_name=tmp_collection_name
+    )
     index.index(ten_simple_docs)
     # delete once
     assert index.num_docs() == 10
@@ -167,10 +181,12 @@ def test_del_single(ten_simple_docs, qdrant_config):  # noqa: F811
             assert index[id_].id == id_
 
 
-def test_del_multiple(ten_simple_docs, qdrant_config):  # noqa: F811
+def test_del_multiple(ten_simple_docs, tmp_collection_name):  # noqa: F811
     docs_to_del_idx = [0, 2, 4, 6, 8]
 
-    index = QdrantDocumentIndex[SimpleDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[SimpleDoc](
+        host='localhost', collection_name=tmp_collection_name
+    )
     index.index(ten_simple_docs)
 
     assert index.num_docs() == 10
@@ -185,16 +201,20 @@ def test_del_multiple(ten_simple_docs, qdrant_config):  # noqa: F811
             assert index[doc.id].id == doc.id
 
 
-def test_del_key_error(ten_simple_docs, qdrant_config):  # noqa: F811
-    index = QdrantDocumentIndex[SimpleDoc](db_config=qdrant_config)
+def test_del_key_error(ten_simple_docs, tmp_collection_name):  # noqa: F811
+    index = QdrantDocumentIndex[SimpleDoc](
+        host='localhost', collection_name=tmp_collection_name
+    )
     index.index(ten_simple_docs)
 
     with pytest.raises(KeyError):
         del index['not_a_real_id']
 
 
-def test_num_docs(ten_simple_docs, qdrant_config):  # noqa: F811
-    index = QdrantDocumentIndex[SimpleDoc](db_config=qdrant_config)
+def test_num_docs(ten_simple_docs, tmp_collection_name):  # noqa: F811
+    index = QdrantDocumentIndex[SimpleDoc](
+        host='localhost', collection_name=tmp_collection_name
+    )
     index.index(ten_simple_docs)
 
     assert index.num_docs() == 10
@@ -213,12 +233,12 @@ def test_num_docs(ten_simple_docs, qdrant_config):  # noqa: F811
     assert index.num_docs() == 10
 
 
-def test_multimodal_doc(qdrant_config):  # noqa: F811
+def test_multimodal_doc():
     class MyMultiModalDoc(BaseDoc):
         image: ImageDoc
         text: TextDoc
 
-    index = QdrantDocumentIndex[MyMultiModalDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[MyMultiModalDoc](host='localhost')
 
     doc = [
         MyMultiModalDoc(
@@ -233,3 +253,17 @@ class MyMultiModalDoc(BaseDoc):
         0.0
     )
     assert index[id_].text.text == doc[0].text.text
+
+
+def test_collection_name():
+    class TextDoc(BaseDoc):
+        text: str = Field()
+
+    class StringDoc(BaseDoc):
+        text: str = Field(col_type='payload')
+
+    index = QdrantDocumentIndex[TextDoc]()
+    assert index.collection_name == TextDoc.__name__.lower()
+
+    index = QdrantDocumentIndex[StringDoc]()
+    assert index.collection_name == StringDoc.__name__.lower()
diff --git a/tests/index/qdrant/test_persist_data.py b/tests/index/qdrant/test_persist_data.py
index 9fd54715a30..cea1fa70295 100644
--- a/tests/index/qdrant/test_persist_data.py
+++ b/tests/index/qdrant/test_persist_data.py
@@ -5,7 +5,7 @@
 from docarray import BaseDoc
 from docarray.index import QdrantDocumentIndex
 from docarray.typing import NdArray
-from tests.index.qdrant.fixtures import qdrant, qdrant_config  # noqa: F401
+from tests.index.qdrant.fixtures import start_storage  # noqa: F401
 
 pytestmark = [pytest.mark.slow, pytest.mark.index]
 
@@ -19,18 +19,18 @@ class NestedDoc(BaseDoc):
     tens: NdArray[50]  # type: ignore[valid-type]
 
 
-def test_persist_and_restore(qdrant_config):  # noqa: F811
+def test_persist_and_restore():
     query = SimpleDoc(tens=np.random.random((10,)))
 
     # create index
-    index = QdrantDocumentIndex[SimpleDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[SimpleDoc](host='localhost')
     index.index([SimpleDoc(tens=np.random.random((10,))) for _ in range(10)])
     assert index.num_docs() == 10
     find_results_before = index.find(query, search_field='tens', limit=5)
 
     # delete and restore
     del index
-    index = QdrantDocumentIndex[SimpleDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[SimpleDoc](host='localhost')
     assert index.num_docs() == 10
     find_results_after = index.find(query, search_field='tens', limit=5)
     for doc_before, doc_after in zip(find_results_before[0], find_results_after[0]):
@@ -42,13 +42,13 @@ def test_persist_and_restore(qdrant_config):  # noqa: F811
     assert index.num_docs() == 15
 
 
-def test_persist_and_restore_nested(qdrant_config):  # noqa: F811
+def test_persist_and_restore_nested():
     query = NestedDoc(
         tens=np.random.random((50,)), d=SimpleDoc(tens=np.random.random((10,)))
     )
 
     # create index
-    index = QdrantDocumentIndex[NestedDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[NestedDoc](host='localhost')
     index.index(
         [
             NestedDoc(
@@ -62,7 +62,7 @@ def test_persist_and_restore_nested(qdrant_config):  # noqa: F811
 
     # delete and restore
     del index
-    index = QdrantDocumentIndex[NestedDoc](db_config=qdrant_config)
+    index = QdrantDocumentIndex[NestedDoc](host='localhost')
     assert index.num_docs() == 10
     find_results_after = index.find(query, search_field='d__tens', limit=5)
     for doc_before, doc_after in zip(find_results_before[0], find_results_after[0]):
diff --git a/tests/index/weaviate/test_column_config_weaviate.py b/tests/index/weaviate/test_column_config_weaviate.py
index 4789a6d707f..fd5a18d7560 100644
--- a/tests/index/weaviate/test_column_config_weaviate.py
+++ b/tests/index/weaviate/test_column_config_weaviate.py
@@ -34,3 +34,17 @@ class StringDoc(BaseDoc):
     dbconfig = WeaviateDocumentIndex.DBConfig(index_name="StringDoc")
     index = WeaviateDocumentIndex[StringDoc](db_config=dbconfig)
     assert get_text_field_data_type(index, "StringDoc") == "string"
+
+
+def test_index_name():
+    class TextDoc(BaseDoc):
+        text: str = Field()
+
+    class StringDoc(BaseDoc):
+        text: str = Field(col_type="string")
+
+    index = WeaviateDocumentIndex[TextDoc]()
+    assert index.index_name == TextDoc.__name__
+
+    index = WeaviateDocumentIndex[StringDoc]()
+    assert index.index_name == StringDoc.__name__
diff --git a/tests/index/weaviate/test_index_get_del_weaviate.py b/tests/index/weaviate/test_index_get_del_weaviate.py
index ba4d6a27f5e..71b1609f03f 100644
--- a/tests/index/weaviate/test_index_get_del_weaviate.py
+++ b/tests/index/weaviate/test_index_get_del_weaviate.py
@@ -65,7 +65,7 @@ def test_index(weaviate_client, documents):
 
 
 def test_index_simple_schema(weaviate_client, ten_simple_docs):
-    index = WeaviateDocumentIndex[SimpleDoc]()
+    index = WeaviateDocumentIndex[SimpleDoc](index_name="Document")
     index.index(ten_simple_docs)
     assert index.num_docs() == 10