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Composable Vector Search Infrastructure

Composable Vector Search Infrastructure

1
posts
2026

Qdrant is being developed as fundamental infrastructure for AI, emphasizing composable vector search primitives that can be combined at query time. This approach aims to address architectural limitations encountered in production AI workloads, such as RAG and agents, by providing flexible and performant retrieval across diverse environments from edge devices to supercomputers. The focus is on building a robust, long-lasting engine that controls the stack down to assembly, written in Rust for predictability and performance. Key aspects include supporting dense vectors, sparse vectors, metadata filters, multi-vector representations, and custom scoring. The goal is to provide one retrieval architecture from the data center to the device, enabling agentic AI and on-device applications.

2026

We Raised $50M to Build Composable Vector Search as Core Infrastructure - Qdrant

3/12/2026

This post announces a $50 million Series B funding round to build composable vector search as core infrastructure. It highlights the critical role of retrieval in AI systems (RAG, agents, multimodal search) and the architectural limitations of fixed pipelines. Qdrant's design philosophy is to build fundamental, long-lasting infrastructure in Rust for predictable performance and memory safety. The composable approach allows users to combine primitives like dense vectors, sparse vectors, metadata filters, and custom scoring at query time, adapting to different workloads. The post also emphasizes the need for fast, flexible retrieval everywhere, from agents to edge devices, and mentions Qdrant Edge for on-device capabilities. It acknowledges community contributions to the engine, such as ASCII folding for multilingual full-text retrieval and field-level control over HNSW graph construction.