Pinecone's vector database just learned a few new tricks

Welcome to Runtime! Today: Pinecone's second-generation serverless infrastructure for its managed vector database gets an upgrade, Microsoft's data-center buildout hits a snag, and the latest funding rounds in enterprise tech.

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Databases are evergreen

Pinecone, one of the leading vector database startups to emerge during the generative AI boom, thinks it has identified some of the roadblocks that companies need to clear before turning their generative AI experiments into production.

Pinecone announced Tuesday that it will begin rolling out the second generation of the serverless architecture for its flagship vector database over the next several months. The new version was designed to automatically make the right configuration decisions for a wider variety of application types, such as recommendation engines and agentic systems, without compromising on speed or cost.

• "What we see in the market today is that people use vector databases for very, very different kinds of workloads, and they expect their database to be out-of-the-box responsive and performant for all the different kinds of workloads that you have in front of you," said Pinecone co-founder and CEO Edo Liberty in a recent interview with Runtime.
• The new database comes a little more than a year after Pinecone, which has raised $138 million in funding, introduced the first generation of its serverless architecture.
• That version relieved Pinecone customers of the burden of having to configure the computing resources needed to handle their workloads, while the goal for the new version was to address another operational challenge through a new system for building indexes, or collections of files within a database.

Vector databases store information as vectors, which contain not only the data itself but information about how that particular piece of data relates to other pieces of data in the system. That makes them ideal for apps that tap into large-language models to answer questions, since they are able to quickly analyze similarities between the words in the prompt and the model's training data.

• For example, recommendation engines need to quickly read information from a database, but when speed is the priority the database tends to worry more about serving existing information rather than updating the index with new information, which can make it stale as new queries come in.
• On the other hand, generative AI search tools for retrieving documents inside a company have to be updated constantly as new documents are created, but rebuilding the index that often forces the app to spend computing resources.
• "Can we design something that is adaptive and can be able to actually handle all of it? It took us a very long time to do that, but we have done it," Liberty said.

While the generative AI hype meter has come back down to earth somewhat since Pinecone raised a $100 million funding round in April 2023, vector databases are still in demand.

• Only 20% of respondents to Retool's State of AI survey in late 2023 were using vector databases, but by the time the same report came out six months later in June 2024, 63.6% had started kicking the tires.
• But that report illustrated a potential problem for Pinecone: As database vendors of all stripes rushed to add vector capabilities to their databases in response to the AI boom, it wasn't necessarily clear why anyone would want to bet on a standalone vector database run by a startup when they were already using other databases sold and maintained by vendors with decades of enterprise experience.
• Liberty argued that vector databases are here to stay because they have the potential to unlock enormous value from all the unstructured data that most companies have lying around untouched.
• "People used to talk about web scale as being big," said Liberty, a veteran of AWS and Yahoo. "But the ratio between how much stuff you put on the web versus how many emails you've got, how many text messages you wrote, how many pictures you have on your phone … all of that data sits somewhere, and now it's actionable."

Read the rest of the full story on Runtime.

Flux capacity

Microsoft's stock price fell nearly 3% over the last several trading days after financial analysts at TD Cowen said the company had canceled plans to lease computing capacity from "at least two private data center operators," according to CNBC. Those deals involved up to at least 200 megawatts of capacity, according to Bloomberg, which is enough — more or less — to power two cloud-scale data centers.

A Microsoft representative told CNBC that the company "may strategically pace or adjust our infrastructure in some areas," but reiterated that it still plans on spending $80 billion on capital expenditures in its fiscal year, which ends in June. It's still unclear exactly where Microsoft was planning to allocate that capacity, but it seems likely that the overhaul of its relationship with OpenAI — which is now doing that Project Stargate thing — altered future plans to add capacity beyond this fiscal year.

It's getting hard to imagine that some of the five-year projections for data-center construction and power consumption will meet expectations, as models like DeepSeek prove that newer models can be trained on older generation hardware. And if you don't believe us, ask Satya Nadella, who told Dwarkesh Patel that "there will be overbuild" while noting that "I am thrilled that I'm going to be leasing a lot of capacity in '27, '28."

Enterprise funding

NinjaOne raised $500 million in an extension to a previous Series C round, which values the endpoint security company at $5 billion.

Lambda Labs scored $480 million in Series D funding to help accelerate the construction of its GPU cloud infrastructure service.

Together AI landed $305 million in Series C funding for its cloud AI infrastructure service, which values the company at $3.3 billion.

Quantum Machines raised $170 million in Series C funding, which will allow it to ramp up its production of chips that will help quantum computers turn into cloud infrastructure services.

Arize AI scored $70 million in Series C funding for its AI observability service, which hopes to expand observability concepts for the unique needs of AI applications.

Metronome landed $50 million in Series C funding to help SaaS companies switch from subscription-based billing to usage-based billing.

The Runtime roundup

IBM announced plans to acquire DataStax, which developed several NoSQL databases around the Cassandra open-source project, for an undisclosed amount.

MongoDB acquired Voyage AI, which was building embedding models that promise to improve RAG (retrieval-augmented generation), for $220 million.

Zoom reported a disappointing projection for fiscal-year revenue growth on Monday, and its stock fell 8.5% on Tuesday.

Salesforce agreed to spend $2.5 billion on cloud services from Google Cloud, which beat Microsoft and Oracle for the chance to be Salesforce's alternative cloud provider behind AWS.

Thanks for reading — see you Thursday!