Vector databases are all the rage, judging by the number of startups entering the space and investors looking to get a piece of the pie. The proliferation of large language models (LLMs) and the genetic artificial intelligence (GenAI) movement have created fertile ground for the flourishing of vector database technologies.
While traditional relational databases such as Postgres or MySQL are suitable for structured data – predefined types of data that can be neatly filed into rows and columns – this does not work so well for unstructured data such as images, videos, emails, media social media posts and any data that does not conform 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 different data points. This is perfect for machine learning, as the database stores data spatially based on how related each item is to each 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 chatbot AI to better understand the context of a conversation by analyzing previous similar conversations. Vector search is also useful for all kinds of real-time applications, such as content recommendations on social networks or e-commerce applications, 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 by providing additional information that may not have been available in the original training dataset.
“Without using vector similarity search, you can still develop AI/ML applications, but you will need to do more retraining and optimization.” Andre ZagiarniCEO and co-founder of startup vector search Quadrant, explained to TechCrunch. “Vector databases come into play when there is a large dataset and you need a tool to work with vector integrations efficiently and conveniently.”
In January, Qdrant secured $28 million in funding to build on the growth that led it to become one of the 10 fastest-growing commercial open source startups last year. And it’s far from the only vector database startup raising cash lately — Vespa, Weaviatepine nut, and Colour collectively raised $200 million last year for various vector offerings.
Qdrant founding team. Image credits: Qdrant
Since the turn of the year, we have also seen Index Ventures lead a $9.5M round in Hyperlink, a platform that converts complex data into vector embeddings. And a few weeks ago, Y Combinator (YC) revealed its Winter ’24 cohort, which included Lampa startup that sells a hosted vector search engine for Postgres.
Somewhere else, Marco raised a $4.4 million round late last year, quickly followed by a $12.5 million Series A round on February. The Marqo platform provides a full range of vector tools out of the box, spanning vector generation, storage and retrieval, allowing users to bypass third-party tools from OpenAI or Hugging Face, and offers everything through a single API.
Co-Founders Marqo Tom Hammer and Jesse N. Clark previously worked in engineering roles at Amazon, where they realized the “huge unmet need” for semantic, flexible search in different modes such as text and images. And that’s when they jumped ship to form Marqo in 2021.
“Working with visual search and robotics at Amazon is when I really looked at vector search — I was thinking about new ways to do product discovery, and that very quickly converged on vector search,” Clark told TechCrunch. “In robotics, I used multimodal search to look through many of our images to determine if there were incorrect things like tubes and packages. Otherwise it would be very difficult to solve.”
Marqo co-founders Jesse Clark and Tom Hamer. Image credits: Marqo
Enter the business
While vector databases are having a moment in the thick of ChatGPT and the GenAI movement, they are not the panacea for every enterprise search scenario.
“Proprietary databases tend to focus entirely on specific use cases and therefore can design their architecture for performance on the tasks required, as well as user experience, compared to general-purpose databases, which must fit the current design”. Peter Zaitsevfounder of database support and services company Percona, explained to TechCrunch.
While specialized databases may excel at one thing to the exclusion of others, this is why we are beginning to see established database operators such as Elastic, Redis, OpenSearch, Cassandra, Oracleand MongoDB adding vector intelligent database search elements to the mix, as do cloud service providers Microsoft’s Azure, Amazon’s AWSand Cloudflare.
Zaitsev compares this latest trend to what happened JSON More than a decade ago, when web applications became more widespread and developers needed a language-independent data format that was easy for humans to read and write. In that case, a new class of database emerged in the form of document databases such as MongoDB, while existing relational databases also introduced JSON support.
“I think the same is likely to happen with vector databases,” Zaitsev told TechCrunch. “Users building very complex and large-scale AI applications will use dedicated vector search databases, while those who need to build some AI functionality for their existing application are more likely to use vector search functionality in the databases they already use. “
But Zayarni and his Qdrant colleagues are betting that native solutions built entirely around vectors will provide the “speed, memory security and scale” needed as vector data explodes, compared to companies that position vector search as after the fact.
“Their pitch is, ‘we can also do vector search if needed,'” Zayarni said. “Our pitch is, ‘we do advanced vector search in the best possible way.’ It’s all about specialization. In fact we recommend starting with whatever database you already have in your technology stack. At some point, users will experience limitations if vector search is a critical component of your solution.”
