Introducing Qdrant: Open Source Vector Database Startup

Many have seen the rise of ChatGPT and the generative AI hype train as a sign that artificial intelligence is entering mainstream culture. In just six months, there has been an enormous increase in public interest in AI, but not all of the necessary infrastructure is available to meet this growing demand. German startup Quadrant aims to fill this gap and provide the needed support for these new use cases.

Quadrant, a Berlin-based company established in 2021, offers AI software developers an open-source vector search engine and database for unstructured data. This is especially advantageous during the development of AI applications because it allows the usage of real-time data that isn’t classified or labeled.

Quadrant has today secured $7.5 million in seed financing, led by Unusual Ventures, with additional backing from 42cap, IBB Ventures, and several angel investors such as Cloudera co-founder Amr Awadallah. This follows the €2 million ($2.2 million) pre-seed funding round Qdrant closed last year to help push their technology into the commercial realm.


For those unfamiliar with it, vector databases are designed to store unstructured data like images, videos, and text. This makes it possible for people (and systems) to search through unlabeled content. This is especially beneficial for broadening the applications of large language models (LLMs), including GPT-4, which powers ChatGPT.

Gartner reports that 90% of newly created data in the business world is unstructured, growing three times faster than its structured counterpart. Unfortunately, many AI R&D projects do not complete due to a lack of necessary tools. Connecting an LLM to real-time, unstructured data can provide numerous opportunities for those looking to construct more useful AI applications, as stated by Andre Zayarni, CEO and co-founder of Qdrant.

Zayarni says:

“Vector databases are the natural extension of their (LLMs) capabilities,”

“The biggest limitation of GPT is that it ‘knows’ only about events that happened before the time the model was trained, but if it’s connected to a vector database, the virtual ‘memory’ of an LLM can be extended with real-time and real-world data.”

Investors have been keeping tabs on the situation; for instance, Pinecone, a proposition close to Qdrant, procured $28 million in revenue last year. Zayarni thinks that Qdrant’s open-source basis is a strong attraction for possible customers.

Zayarni continues to say:

“Engineers trust open source, and it will be hard for proprietary software to compete in this market if there is an OSS product with a similar — or even better — offering,”

Besides Zilliz and Chroma, other open-source players are in the market. Last year, Zilliz raised $60 million for their Milvus vector database. Additionally, just this past month, Chroma obtained $18 million of seed money to expand its AI-oriented open-source vector database.

The fact that Qdrant has garnered $7.5 million in seed funding indicates the current investor mindset – they are drawn to any technology that could potentially expand all developers’ artificial intelligence and machine learning opportunities.

Zayarni commented that Qdrant had devoted a considerable amount of time to perfecting its pitch deck before launching its seed funding round, with the result of receiving an initial term sheet only two days after sending out the deck. This was then followed by another term sheet two days later.

Zayarni went on to say:

“We had more than 20 VCs interested — almost all of them wanted to join as co-investors later — and we most probably would have received more offers,”

“But Unusual Ventures’ deep experience with OSS (open source software) and its model of being an active operational partner instead of just an investor were incredibly attractive to us, so we decided to go with them.”

After introducing its managed cloud offering a few months ago, Qdrant is now celebrating the news of funding. This service offers developers convenience with one-click deployments, automated version upgrades, backups, and an upcoming database admin interface.

Quadrant has recently obtained new financial backing, enabling them to progress on developing an enterprise product that can be hosted either locally or in a cloud-based infrastructure. This product is expected to be available later in 2021.

This technology has the potential to unlock new possibilities in fields such as natural language processing, computer vision, and recommendation systems. As more and more companies seek to capitalize on the value of unstructured data, Qdrant’s solution promises to play an increasingly important role in enabling AI developers to achieve their goals. By leveraging the power of Qdrant, businesses can gain a competitive edge by accessing previously out-of-reach insights. Overall, Qdrant is an exciting development in AI and data management, and it will be interesting to see how this technology evolves and matures in the years to come.

Source: TechCrunch

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