Qdrant, a startup focused on open source vector database, secures $28M in funding

Qdrant, the company behind the eponymous open source vector database, has raised $28 million in a Series A round of funding led by Spark Capital. The vector database realm is hot. In recent months we’ve seen the likes of Weaviate raise $50 million for its open source vector database, while Zilliz secured secured $60 million to commercialize the Milvus open source vector database. We are proud to share that this new X AI feature just announced by @elonmusk is powered by the Qdrant Vector Database. Right, using a Vector Database, powered by Qdrant.

Qdrant Raises $28M in Series A Round of Funding

The Berlin-based company Qdrant, known for its open source vector database, has secured $28 million in a Series A round of funding led by Spark Capital. Founded in 2021, Qdrant is targeting developers with its vector search engine and database, which is crucial for generative AI that requires drawing relationships in unstructured data such as text, images, and audio. According to data from Gartner, unstructured data accounts for 90% of all new enterprise data and is growing faster than its structured counterpart.

The realm of vector databases is currently a hot topic, as seen by recent funding for other open source vector database companies such as Weaviate, Zilliz, Chroma, and Pinecone. Qdrant itself raised $7.5 million last April, highlighting the growing interest in this technology among investors.

“The plan was to go into the next fundraising in the second quarter this year, but we received an offer a few months earlier and decided to save some time and start scaling the company now,” said Qdrant CEO and co-founder Andre Zayarni to TechCrunch. “Fundraising and hiring of right people always takes time.”

Interestingly, Zayarni revealed that the company had turned down a potential acquisition offer from a major player in the database market when it received its follow-on investment offer. Instead, the company plans to use the funding to expand its business team, as it is currently made up mostly of engineers.

Binary Logic

In the nine months since its last funding round, Qdrant has launched a new compression technology called binary quantization (BQ). This technology is specifically designed for low-latency and high-throughput indexing and can reduce memory consumption by up to 32 times and improve retrieval speeds by around 40 times.

“Binary quantization is a way to ‘compress’ the vectors to the simplest possible representation with just zeros and ones,” explained Zayarni. “This makes comparing the vectors a simple CPU instruction, resulting in significantly faster queries and less memory usage. While the concept is not new, our implementation minimizes the loss of accuracy.”

It’s worth noting that BQ may not work for all AI models, and the user must determine which compression option works best for their specific use-cases. Qdrant has found the best results with models from OpenAI, Cohere, and Google’s Gemini. The company is currently benchmarking against models from Mistral and Stability AI, among others.

This technology has attracted high-profile clients such as Deloitte, Accenture, and X (formerly Twitter). One of the most notable clients is xAI, a company developing Grok, a competitor to OpenAI’s ChatGPT model. Grok uses a generative AI model called Grok-1, trained on data from the web and human feedback. With its close alignment with X, it can incorporate real-time data from social media posts into its responses, known as retrieval augmented generation (RAG). Elon Musk has even hinted at such use-cases publicly in recent months.

Qdrant itself doesn’t disclose which clients are using its open source vector database and which are using its managed services. However, it has mentioned startups such as GitBook, VoiceFlow, and Dust, which mostly use its managed cloud service. This option saves resource-restricted companies from having to manage and deploy everything themselves, as they would with the open source version.

While Zayarni insists that the company’s open source credentials are a major selling point, even for companies that choose to pay for additional services, he also recognizes the importance of having control over your own data.

“When using a proprietary or cloud-only solution, there is always a risk of vendor lock-in,” said Zayarni. “With open source, there is always more control and the ability to switch between different deployment options.”

In addition to the funding announcement, Qdrant is also officially releasing its managed “on-premise” edition, allowing enterprises to host everything internally but still benefit from the premium features and support provided by Qdrant. This comes after news last week that Qdrant’s cloud edition is now available on Microsoft Azure, joining existing support for AWS and Google Cloud Platform.

Aside from lead investor Spark Capital, Qdrant’s Series A round also included funding from Unusual Ventures and 42cap.

In conclusion,

The $28 million raised by Qdrant in its Series A funding round is just a testament to the growing interest in open source vector databases and their importance in the world of AI. With its innovative BQ technology and high-profile clients, Qdrant is sure to make its mark in this competitive market. And with its ongoing commitment to open source, customers can rest assured that they have control over their data and the ability to switch between deployment options. Congratulations to the Qdrant team on their successful funding! In Rust we trust!

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Max Chen

Max Chen is an AI expert and journalist with a focus on the ethical and societal implications of emerging technologies. He has a background in computer science and is known for his clear and concise writing on complex technical topics. He has also written extensively on the potential risks and benefits of AI, and is a frequent speaker on the subject at industry conferences and events.

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