Vector databases are all the rage, judging by the number of startups entering the space and the investors ponying up for a piece of the pie. The proliferation of large language models (LLMs) and the generative AI (GenAI) movement have created fertile ground for vector database technologies to flourish.While traditional relational databases such as Postgres or MySQL are well-suited to structured data — predefined data types that can be filed neatly in rows and columns — this doesn’t work so well for unstructured data such as images, videos, emails, social media posts, and any data that doesn’t adhere to a predefined data model.Vector databases, on the other hand, store and process data in the form of vector embeddings, which convert text, documents, images, and other data into numerical representations that capture the meaning and relationships between the different data points. This is perfect for machine learning, as the database stores data spatially by how relevant each item is to the other, making it easier to retrieve semantically similar data.This is particularly useful for LLMs, such as OpenAI’s GPT-4, as it allows the AI chatbot to better understand the context of a conversation by analyzing previous similar conversations. Vector search is also useful for all manner of real-time applications, such as content recommendations in social networks or e-commerce apps, as it can look at what a user has searched for and retrieve similar items in a heartbeat. Vector search can also help reduce “hallucinations” in LLM applications, through providing additional information that might not have been available in the original training dataset.“Without using vector similarity search, you can still develop AI/ML applications, but you would need to do more retraining and fine-tuning,” Andre Zayarni, CEO and co-founder of vector search startup Qdrant, explained to TechCrunch. “Vector databases come into play when there’s a large dataset, and you need a tool to work with vector embeddings in an efficient and convenient way.”In January, Qdrant secured $28 million in funding to capitalize on growth that has led it to become one of the top 10 fastest growing commercial open source startups last year. And it’s far from the only vector database startup to raise cash of late — Vespa, Weaviate, Pinecone, and Chroma collectively raised $200 million last year for various vector offerings.Qdrant founding team

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.Founded in 2021, Berlin-based Qdrant is seeking to capitalize on the burgeoning AI revolution, targeting developers with an open source vector search engine and database — an integral part of generative AI, which requires relationships be drawn between unstructured data (e.g. text, images or audio that isn’t labelled or otherwise organized), even when that data is “dynamic” within real-time applications. As per Gartner data, unstructured data makes up around 90% of all new enterprise data, and is growing three times faster than its structured counterpart.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. Elsewhere, Chroma secured $18 million in seed funding for a similar proposition, while Pinecone nabbed $100 million for a proprietary alternative.Qdrant, for its part, raised $7.5 million last April, further highlighting the seemingly insatiable appetite investors have for vector databases — while also pointing to a planned growth spurt on Qdrant’s part.“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,” Qdrant CEO and co-founder Andre Zayarni explained to TechCrunch. “Fundraising and hiring of right people always takes time.”Of note, Zayarni says that the company actually rebuffed a potential acquisition offer from a “major database market player” at the same time of receiving a follow-on investment offer