# Weaviate: what AI models say (September 2026)

Source: Memetik Index, https://www.memetik.ai/vendors/weaviate
Website: https://weaviate.io

Weaviate was named in 45 of 50 AI answers across 1 category. Best position: Vector databases, 90% answer share, ranked #4, named first in 0%.

## What Weaviate is

Weaviate is an open-source vector database for developers creating and scaling AI applications. It is available self-hosted, as a managed service, or as a Kubernetes package in a VPC.

It stores, indexes and searches high-dimensional vectors. It combines vector search with BM25 keyword search for hybrid search, and can generate vector embeddings or accept embeddings supplied by the developer. It supports retrieval-augmented generation by using proprietary data with machine-learning models.

(From Weaviate's own website, read 2026-09-02.)

## By category

| Category | Rank | Answer share | Named first | Models |
|---|---|---|---|---|
| Vector databases (https://www.memetik.ai/index/vector-databases) | #4 of 16 | 90% | 0% | GPT-5.6 Sol, ChatGPT, GPT-5.6 Luna, Claude Opus 5, Claude, Claude Fable 5, Gemini, Gemini 3.5 Flash, Perplexity, Sonar Reasoning Pro |

## What the models said

> "Choose a database like Qdrant, Weaviate, or Pinecone that supports dense + sparse hybrid search natively."
> — Gemini, Vector databases

> "Choose Weaviate if hybrid search and multi-tenant isolation are core requirements for your product."
> — Perplexity, Vector databases

> "Choose Weaviate if built-in hybrid search and higher-level AI retrieval features matter."
> — GPT-5.6 Luna, Vector databases

> "Weaviate: strongest built-in search feature set Weaviate is worth choosing when hybrid search is central and you want keyword retrieval integrated directly with vector search."
> — GPT-5.6 Sol, Vector databases