QDRANT

Andrey Vasnetsov was building machine learning systems in 2020 and kept hitting the same wall: existing vector search tools were either too slow, too memory-hungry, or lacked the filtering capabilities real production applications require. You could do approximate nearest-neighbor search, or you could filter by metadata — but doing both at the same time at scale was genuinely hard.

He started building Qdrant as a personal solution, writing it in Rust for maximum performance from day one. He open-sourced the project in 2021.

The Rust implementation attracted developers who cared deeply about production performance. When LLM applications exploded in 2022 and 2023, demand for production-grade vector search skyrocketed.

Qdrant was already there — already battle-tested, already in Rust — and the company formalized around the open-source project.

Open-source core, cloud revenue model. The Qdrant vector search engine is fully open source under Apache 2.0 — users can run it anywhere, including in production.

Qdrant Cloud is the managed service, charging for storage, memory, and compute so teams avoid managing clusters themselves. Enterprise contracts add SLAs, dedicated support, and private deployments.

The open source version is production-ready, so Qdrant must earn cloud adoption by being genuinely easier to operate.

Qdrant Engine (open source) — the core vector search engine supporting dense vectors, sparse vectors, and hybrid search. Installable via Docker or binary in minutes.

Qdrant Cloud — fully managed deployment across AWS, GCP, and Azure with one-click setup. Payload Filtering — Qdrant's advanced metadata filtering system, enabling complex multi-condition queries alongside vector similarity in a single pass.

GitHub-first. Qdrant accumulated tens of thousands of GitHub stars by shipping fast, maintaining clean documentation, and responding to the developer community with unusual speed.

They built first-class integrations into every major LLM framework — LangChain, LlamaIndex, Haystack — and appeared consistently in AI engineering tutorials and benchmarks. The Rust performance story travels naturally in benchmark comparisons, and developers benchmark before they deploy in production.

Competing in a market where established databases — PostgreSQL, Redis, MongoDB, Elasticsearch — are all adding native vector support. The objection is simple: you already have a database, so why add another one?

Qdrant's answer is performance and filtering precision at scale. Proving that case to enterprise IT teams, where procurement runs on existing vendor relationships and risk aversion, is harder than winning individual developers.

Unusual Ventures, 42CAP