209 AI developers voted across a competitive category with multiple architectural approaches vying for developer adoption. Here is what the results reveal.
The March 2026 IT Brand Pulse AI Brand Leader survey asked 209 members of the AI developer community to vote for Market Leader and Intelligence & Innovation Leader in Vector Databases, one of 26 products in the AI Engineering stack. Weaviate took the top position in both categories, receiving 28.2% of votes for Market Leader and 30.1% for Intelligence & Innovation Leader. Pinecone (20.1% market, 18.2% innovation) and Milvus (18.2% market, 20.1% innovation) formed a competitive second tier.
The competitive top tier and the presence of a strong “Others” category suggest the market is still fragmented, with multiple architectural approaches competing for developer adoption. The IT Brand Pulse analyst team expects vector databases to become increasingly embedded in broader AI platform offerings as RAG and semantic search become standard components of production AI systems.
What Are Vector Databases?
IT Brand Pulse defines Vector Databases as systems designed to store, index, and retrieve high-dimensional vector embeddings that represent unstructured data such as text, images, audio, and video. These platforms enable semantic search, similarity matching, and retrieval-augmented generation (RAG) by allowing AI applications to find relevant context based on meaning rather than exact matches. Core capabilities include vector indexing, approximate nearest neighbor (ANN) search, hybrid search (vector + keyword), metadata filtering, and integration with ML pipelines. Vector Databases sits within the Context & Memory sub-layer of the broader AI Engineering stack, alongside AI Context Engineering Platforms, AI Memory Platforms, Multimodal Memory Platforms, and Knowledge Graph Platforms.
Download the Brand Leader Report for Vector Databases.
