Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most starred vector database on GitHub. Milvus is a distributed vector database that shines in 1B+ vector use cases. Examples include autonomous driving, e-commerce, and drug discovery. (and, of course, RAG)
We are also hiring for other roles that I am not personally involved in the hiring process for such as product managers, software engineers, and recruiters.
oh yeah this is a great question, I get this a lot when I do my talks about RAG stuff
the way I see it is if you have a small amount of data (<10,000 vectors) then it's all the same and you should stick with the technology you are most familiar with
once you get more than that, you may want to consider a vector database
the reason that vector databases exist is because vector search is a highly compute intensive task, in regular database settings, you almost never have to run compute, the database is primarily looking to do an exact match
however, because vector search is predicated on the idea of finding similar vectors, and because exact vector matches are unlikely, you find yourself in the situation of having to optimize that
if you're building on a sql/nosql database you find yourself having to manage indexing, computing distance metrics, and load balancing
pgvector manages much of that for you, but due to the structure of SQL, it doesn't manage it in a very efficient manner - because it wasn't built to, an extra system needs to be built on top
as many experienced software engineers will tell you, adding complexity doesn't necessarily make something better, and adds more points of failure
purpose built vector databases like the ones in the article (eg milvus, chroma, weaviate) are built with this compute challenge in mind, and this becomes useful as the amount of data you have expands
Hi everyone, I put together this survey of tools for the LLM Stack in 2024. I've linked the friend-link for the Medium article in the URL. I'd love feedback from you guys about any tools I've missed.
Zilliz is hiring! We're looking for REMOTE and/or HYBRID roles in SF
Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most widely adopted vector database. Vector databases are a crucial piece of any technology stack looking to take advantage of unstructured data. Most recently and notably, Retrieval Augmented Generation (RAG). For RAG, vector databases like Milvus are used as the tool to inject customized data. In other words, vector databases make things like customized chat bots, personalized product recommendations, and more possible.
We are hiring for Developer Advocates, Senior+ Level Engineers and Product people, and Talent Acquisition. Check out all the roles here: https://zilliz.com/careers
I'm not sure if I'm missing something from the paper, but are multi-billion parameter models getting called "small" language models now? And when did this paradigm shift happen?
Anyscale consistently posts great projects. Very cool to see the cost comparison and quality comparison. Not surprising to see that OSS is less expensive, but also rated as slightly lower quality than gpt-3.5-turbo.
Zilliz | Developer Advocate, Solutions Architect | ONSITE | Full Time
Zilliz is a fast-growing startup developing the industry’s leading vector database company for enterprise-grade AI. Founded by the engineers behind Milvus, the world’s most popular open-source vector database, the company builds next-generation database technologies to help organizations quickly create AI applications. On a mission to democratize AI, Zilliz is committed to simplifying data management for AI applications and making vector databases accessible to every organization.
Interesting graphic, bland and unvoiced conclusion
You're also missing a lot of details. For example, Milvus and Zilliz are actually a little different, check this out for more details: https://github.com/zilliztech/VectorDBBench (of course run it on your own stuff, don't blindly trust companies just because their product is open source)
Also if you want to throw some more comparisons in their checkout elastic search
I work on Milvus at Zilliz and we encounter people working on LLM companies or frameworks often, I don't ask this question a lot a lot, but it looks like at the moment many companies don't have a real moat, they are just building as fast as they can and using talent/execution/funding as their moat
I've also heard some companies that build the LLMs say that those LLMs are their moat, the time, money, and research that goes into them is high
Nice, this is a cool version of ANN search. I like that at the end there is commentary on what's needed for production as well - things like parallelization, RAM considerations, and the consideration for balancing the trees. It's really the production level considerations that would steer you toward a vector database like Milvus/Zilliz