Welcome to Runtime! Today: why vector databases are so hot right now, Microsoft open-sources an interesting application development platform, and the latest enterprise moves.
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With direction and magnitude
Behind every breakthrough in enterprise technology over the past few decades you'll find a database that emerged to meet the needs of applications that had evolved beyond what traditional products could provide. This year, as engineering managers and CIOs are being asked to articulate a generative AI strategy in the middle of a hype cycle for the ages, the vector database is having its coming-out party.
Vector databases are ideal for generative AI applications because they allow companies to search for relationships between their unstructured data points and help their large-language models remember those relationships over time.
- They're designed to handle these unique kinds of information retrieval tasks "in the same way that in your brain, the way you remember faces and the way you remember, I don't know, poetry, is completely different," said Pinecone founder and CEO Edo Liberty during a recent panel discussion. "It's just organized differently. It's accessed differently. It's a different kind of data."
- In an otherwise down year for venture investing, hundreds of millions of dollars are being poured into vector database startups like Pinecone, which raised $100 million from manifesto-generation machine Andreessen Horowitz in April.
- Established database companies like MongoDB, PlanetScale, and even Oracle are adding vector search capabilities to their existing products in hopes of capitalizing on the trend.
- "I think the opportunity that the market has is if you look at the size and growth of unstructured data within an enterprise — I've seen analyst reports saying that it's growing at 3x the pace of any other type of data — the volumes of unstructured data that with some of this new technology can be made harvestable is massive," said Michael Gilfix, chief product and engineering officer for KX, which has adapted its work on time-series databases into the vector database world.
Large-language models trained on massive data sets spit out what they've learned as vectors, "a quantity that has magnitude and direction" and can be represented on a graph.
- Those vectors help establish relationships between words, phrases, and sentences to understand how they are both similar and different.
- After those relationships are established, the models can rely on the vectors to help them draw lines between existing data and a new input that doesn't match anything in that data set.
- When someone types a query into an AI model, the model processes that query into a vector and then searches across the vectors already stored in a vector database to figure out a directionally correct answer.
- Vector databases also allow companies to store new queries specific to their businesses over time as vectors and provide that information to AI models for more accurate and timely results, because those models can't retain new information; they can only fall back on their training data.
One question that has yet to be resolved is whether or not companies will be able to get away with layering vector search capabilities to their existing databases, or whether or not they'll need to use a purpose-built vector database in order to unlock their full potential.
- Believe it or not, vector database startups want you to choose the best-of-breed option while existing vendors think they can provide a more comprehensive product.
- "As soon as your use cases start to grow beyond a few million data points, you'll probably need a dedicated solution," said Andre Zayarni, co-founder and CEO of vector database startup Qdrant.
- For older relational database vendors, "it's a square peg in a round hole to do this kind of vector search," said Jonathan Ellis, co-founder and CTO of DataStax.
- However, he believes "that because of how Cassandra works under the hood and how we built a pluggable indexing system, we can do this in a much more efficient and much more performant way, while still delivering the kinds of CRUD applications — create, retrieve, update, delete — that people expect from from a normal database."
Amid a resurgence of interest in platform technologies from businesses that want to make life easier for their software developers, Microsoft made an interesting decision this week to release a new development platform aimed at software engineers who want to build and deploy multicloud applications.
Radius is "a centralized toolset for developer and operator teams to effectively collaborate," according to a Microsoft blog post. "One of the things that we’re doing that is different is that we want Radius to support all types of applications, and not just be vertically opinionated about an architecture of an application or only support a certain pattern of applications — like 12-factor — or require that apps themselves are written a certain way," Microsoft Azure CTO Mark Russinovich told Techcrunch.
It sounds like technology that could be a compelling part of Microsoft's arsenal of developer tools, including Visual Studio Code and Azure itself, but the company plans to submit the project to the Cloud Native Computing Foundation. Right now Radius supports deploying applications to Azure and AWS, but support for Google Cloud is expected to arrive soon.
Shane Evans is the new chief revenue officer at Gong, following several years in a similar role at Qualtrics.
Sandrine Bossard is the new chief people officer at Dataiku, joining the company from digital music company Believe.
Meg O'Leary is the new chief marketing officer at Tenable, after serving in the same role at Cybereason.
Arun Gupta is the new governing board chair of OpenSSL, adding to his role as vice president and general manager of open ecosystem initiatives at Intel.
The Runtime roundup
OpenAI is teaming up with the UAE's G42 conglomerate, which signed a sovereign cloud deal with OpenAI investor Microsoft earlier this year.
Venture capital funding for cybersecurity startups was down 30% in the third quarter compared to last year, but up a little compared to the second quarter of this year.
Thanks for reading — see you Saturday!