Qdrant, a Berlin-based open-source vector database startup founded in 2021, is set on helping AI developers utilize unstructured data more efficiently. This aligns with the increasing demand for AI-powered applications and the rise of generative AI such as ChatGPT into the mainstream. As the platforms expand, the infrastructure required for emerging use cases must keep pace, and that is precisely where Qdrant steps in.
Intending to bring its open-source vector search engine and unstructured data database deeper into the commercial domain, Qdrant recently announced a $7.5 million seed funding round led by Unusual Ventures. Other participants in this round include 42cap, IBB Ventures, and several angel investors such as Cloudera co-founder, Amr Awadallah. This new funding builds on the €2 million ($2.2 million) in pre-seed funding Qdrant raised last year.
Vector databases, which are designed to store unstructured data - such as images, videos, and text – allow users and systems to search unlabeled content. This technology is essential for broadening the potential applications of large language models (LLMs) like GPT-4, which powers ChatGPT. Approximately 90% of new data generated in enterprises is unstructured, according to Gartner, and unstructured data is growing three times faster than its structured counterpart.
Qdrant CEO and co-founder Andre Zayarni believes that a lack of appropriate tools and the ability to connect LLMs to real-time, unstructured data is a significant reason most AI research and development projects do not reach production. By utilizing vector databases to extend LLMs, developers can create more useful AI applications leveraging real-time, real-world data.
Open-source vector databases have gained significant attention from investors in recent years. Last year, Pinecone, which offers a similar solution to Qdrant, raised $28 million. Qdrant's open-source foundation is a major advantage, according to Zayarni, as it builds trust among engineers and makes proprietary software struggle to compete in the market. Other examples include the Milvus open-source vector database by Zilliz, which raised $60 million, and Chroma's $18 million seed funding earlier this month for its AI-native open-source vector database.
The $7.5 million seed funding for Qdrant showcases investors' increased interest in technologies that advance AI and machine learning, extending their capabilities to developers. Zayarni mentioned that Qdrant received its first term sheet just two days after sending out its pitch deck, which was closely followed by a second term sheet.
Recently, Qdrant introduced its managed cloud offering, designed to assist developers with one-click deployments, automated version upgrades, backups, and an upcoming database admin interface. With its latest round of funding, Zayarni highlighted that Qdrant is also developing an enterprise product, which can be hosted on-premises or in private clouds, and is expected to launch later this year.
No-code platforms, like AppMaster.io, have been growing in popularity, enabling businesses and individuals to create web, mobile, and backend applications without extensive coding knowledge. Such platforms empower developers to experiment with new ideas in the AI domain without the complexity of traditional coding, further accelerating the overall development process and enabling the industry to keep up with the demand for AI applications.