What does AI recommend for vector database platforms?

Weaviate maintains its position as the most cited vector database platform by AI assistants, frequently recommended for its native hybrid search capabilities. However, the landscape is shifting, with Qdrant rising to the #2 spot and platforms like PostgreSQL and Redis gaining significant ground for specialized use cases.

Which vector database platforms brands appear most in AI answers?

Across 2,995 AI responses, Weaviate is mentioned most, named in 67% of them, followed by Pinecone (65%) and Milvus (64%).

How does Parse rank these brands?

Parse measures each brand's mention rate — the share of answers naming it — across 2,995 AI responses to this market's buyer questions. Answers are collected daily and the ranking is published weekly.

How does a brand enter this ranking?

Brands enter the ranking when AI answers mention them. Parse collects answers daily and publishes the re-measured set weekly, so new brands appear as AI starts recommending them.

Best vector database platforms, according to AI

Data as of Aug 25, 2026 · Based on 2,995 AI responses · See how Parse measures this

Vector Database Platforms

292 brands

ParseChatGPT + Google AI

Parse

https://parse.gl

Measured across ChatGPT & Google AI

Best Vector Database Platforms, According to ChatGPT & Google AI | Parse

#	Brand	What AI says	Mention rate
1	WeaviateFrequently recommended for native hybrid search, combining semantic and keyword queries.	Frequently recommended for native hybrid search, combining semantic and keyword queries.	67%
2	PineconeConsistently positioned as the top choice for fully managed, low-maintenance production deployments.	Consistently positioned as the top choice for fully managed, low-maintenance production deployments.	65%
3	MilvusRecommended for its ability to handle massive collections, scaling to billions of vectors.	Recommended for its ability to handle massive collections, scaling to billions of vectors.	64%
4	QdrantA rising contender, often cited for its high performance and efficient metadata filtering.	A rising contender, often cited for its high performance and efficient metadata filtering.	62%
5	ChromaFrequently suggested as a developer-friendly choice for prototyping and smaller-scale RAG applications.	Frequently suggested as a developer-friendly choice for prototyping and smaller-scale RAG applications.	23%
6	Elastic		22%
7	Amazon		21%
8	Alphabet		19%
9	Microsoft		18%
10	MongodbCited as an option for existing users adding vector search to document stores.	Cited as an option for existing users adding vector search to document stores.	16%
11	OpenSearch		15%
12	RedisCited for ultra-low latency vector search due to its in-memory architecture.	Cited for ultra-low latency vector search due to its in-memory architecture.	14%
13	PostgreSQLGaining popularity as a pragmatic option using the pgvector extension for SQL-based search.	Gaining popularity as a pragmatic option using the pgvector extension for SQL-based search.	14%
14	pgvector		12%
15	ZillizThe commercial entity behind Milvus, offering a managed cloud version for enterprise scale.	The commercial entity behind Milvus, offering a managed cloud version for enterprise scale.	10%
16	Vespa (Big Data Platform)		10%
17	FAISS		10%
18	OpenAI		9%
19	LanceDB		7%
20	Supabase		6%
21	Oracle		6%
22	Cohere		5%
23	LangChain		5%
24	Hugging Face		4%
25	Marqo		4%
26	LlamaIndex		3%
27	Datastax		3%
28	Voyage AI		3%
29	Snowflake		2%
30	Convex		2%
31	Appwrite		2%
32	Datavid		2%
33	SingleStore		2%
34	Cockroach Labs		2%
35	ChromaFrequently suggested as a developer-friendly choice for prototyping and smaller-scale RAG applications.	Frequently suggested as a developer-friendly choice for prototyping and smaller-scale RAG applications.	2%
36	Databricks		2%
37	Cohere		2%
38	PyTorch		1%
39	Couchbase		1%
40	Google Gemini API		1%
41	RavenDB		1%
42	ApertureData		<1%
43	Singlebase		<1%
44	ScyllaDB		<1%
45	Yugabyte		<1%
46	Vdaas		<1%
47	DeepLake		<1%
48	Deepset		<1%
49	Xano		<1%
50	Instaclustr		<1%
51	CrateDB		<1%
52	NVIDIA		<1%
53	MindsDB		<1%
54	IBM		<1%
55	Vectara		<1%
56	Activeloop		<1%
57	Apache Software Foundation		<1%
58	Embedding Sync		<1%
59	Airbyte		<1%
60	InterSystems		<1%
61	OpenAI CLIP		<1%
62	pgvectorscale		<1%
63	Mistral		<1%
64	Anthropic		<1%
65	arXiv		<1%
66	Jina AI		<1%
67	Medium		<1%
68	ImageBind		<1%
69	Meilisearch		<1%
70	Turbopuffer		<1%
71	Aerospike		<1%
72	ZenML		<1%
73	hnswlib		<1%
74	Huawei Cloud		<1%
75	MyScale		<1%
76	lakeFS		<1%
77	Shakudo		<1%
78	Neo4j		<1%
79	ClickHouse		<1%
80	Firecrawl		<1%
81	Facebook Research		<1%
82	National University of Singapore		<1%
83	Relevance AI		<1%
84	LeewayHertz		<1%
85	Ollama		<1%
86	Gyde		<1%
87	MariaDB		<1%
88	Sentence-Transformers		<1%
89	Cloudflare		<1%
90	Zvec		<1%
91	Backendless		<1%
92	Microsoft Semantic Kernel		<1%
93	Tigergraph		<1%
94	TileDB		<1%
95	SigLIP		<1%
96	Glean		<1%
97	PocketBase		<1%
98	Tiger Data		<1%
99	Teradata		<1%
100	Typesense		<1%